Preferences for Urban Form: How Density Perceptions, Lifestyle and Urban Form Affect the Acceptance of Increased Density in Neighbourhood Redevelopment

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2015-05-04 Preferences for Urban Form: How Density Perceptions, Lifestyle and Urban Form Affect the Acceptance of Increased Density in Neighbourhood Redevelopment

Meier, Ryan Alexander

Meier, R. A. (2015). Preferences for Urban Form: How Density Perceptions, Lifestyle and Urban Form Affect the Acceptance of Increased Density in Neighbourhood Redevelopment (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/27411 http://hdl.handle.net/11023/2239 master thesis

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UNIVERSITY OF CALGARY

Preferences for Urban Form: How Density Perceptions, Lifestyle, and Urban From Affect the

Acceptance of Increased Density in Neighbourhood Redevelopment

Ryan Alexander Meier

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF ENVIRONMENTAL DESIGN

GRADUATE PROGRAM IN ENVIRONMENTAL DESIGN

CALGARY, ALBERTA

APRIL, 2015

Abstract

Around the world the planning profession has acknowledged the impact

that urban sprawl is having on the environment, society and municipal finances.

Municipalities and the planning profession are working towards a greater vision of densifying existing inner-city neighbourhoods as a means to build better communities, maximize infrastructure, and reduce the overall environmental impact. In redeveloping these neighbourhoods, planners, developers and

architects are experiencing opposition from community groups and residents,

who are presenting a spectrum of complaints about change. This is resulting in

slowed development and increased costs which leads to lowered affordability

and increased viability of suburban development. Understanding both the

personal and physical characteristics that contribute to resident acceptance of

increased density will enable the effective redevelopment of inner city

neighbourhoods. Using Calgary, Alberta, Canada as a case, the research study

aims to understand which urban forms, all presented at constant densities of 400

people and jobs per net developable hectare are preferred by residents.

The study was framed around five research questions, which seek to

understand: (1) preferred urban form, (2) how density is perceived, (3) the

relationship between preferred urban form and perceived density, (4) the

preferred urban form based on lifestyle and (5) the perceived characteristics of

preferred and least preferred urban forms and how this relates to the perceptions

of the local planning and development industry. The research resolved these iii

questions through the use of an empirical experiment, using statistical analysis of questionnaire responses and documenting participant perceptive reactions to a series of controlled visual stimuli. The visual stimuli used were a series of six, three dimensional digital models of a redeveloped neighbourhood which were

constructed and measured to ensure variables were documented and constants

were controlled. Participant responses to personal characteristics (i.e.

demographic and lifestyle) and participant perceptions and preferences of the

neighbourhood representation were collected using a series of two

questionnaires.

The overall findings showed that perceived density does not correlate with participant’s preferences for neighbourhood urban forms. The findings showed

that there was a difference in how participants perceived the spatial and

population density of each urban form. The most preferred urban form, and the

one preferred by most lifestyle groups, was the nodal or urban village density

pattern with 44% of participants stating it was their first or second preference.

The least preferred urban form was the modern density pattern. The findings

suggest that specific characteristics, such as traffic, lack of sufficient parking, and

visual privacy, that have been identified as challenging in the redevelopment of

neighbourhoods are not necessarily indicative of neighbourhood preferences. In

fact, those characteristics (e.g. on street parking, traffic, visual privacy, etc.) did

not have a large impact on whether a particular urban form was preferred.

The results indicate that urban form has a significant impact on resident

acceptance for higher density urban environments, and further, that perceived

iv density has less of an impact on the degree to which a person prefers a neighbourhood form. The results also demonstrated that urban form does have a significant impact on how participants perceived the density of each neighbourhood form. In all, this suggests that how neighbourhoods are designed and the form that is produced has a significant impact on the acceptability of density and redevelopment in general.

Acknowledgements

I would like to take this opportunity to thank those people that made this

research possible and who have supported me in its development. First and foremost I would like to thank my research supervisors Dr. Larissa Muller and Dr.

Mark Lindquist who together guided the development and completion of this thesis document. Your knowledge, guidance and support has been invaluable in the development of this thesis and expanding my knowledge.

Thank you to those that were hired or volunteered to support specific elements of the thesis, specifically Ximena Gonzalez De Aguinaga who transcribed the key informant interviews, Jessica Hall, Caitlyn Bidochka who completed the detailed Sketchup elements of the three dimensional models. You

have all been incredibly important in my successful completion of this research.

I would like to acknowledge my employer, the City of Calgary, my managers and my teams for giving me the flexibility to complete my thesis while working full time.

Finally a great thanks to my fellow EVDS colleagues, my friends and family I don’t know where I would be without you. Your continued love, support and constant encouragement have been irreplaceable. Megan and Christina thank you both for volunteering your time with completing the three dimensional models and editing the final document I would not have finished without you.

Dedication

To my mom, Mary Ann Meier, for her love, strength, and support

vii

Table of Contents

Abstract ...... ii

Acknowledgements ...... v

Dedication ...... vi

Table of Contents ...... vii

List of Figures and Illustrations ...... xiv

List of Tables ...... xviii

1. Introduction ...... 1

1.1 Background ...... 2

1.1.1. The Trend of Urban Sprawl ...... 2

1.1.2. The Issue and Impact of Urban Sprawl ...... 5

1.1.3. How Cities are Solving the Issue of Urban Sprawl ...... 7

1.1.4. Challenges to Increasing Density in Established Areas ...... 9

1.2 Contribution to Knowledge ...... 10

1.3 Aims and Objectives ...... 12

1.3.1. Research Questions ...... 12

1.3.2 Research Hypotheses ...... 13

1.4 Assumptions of the Study ...... 14

1.5 Organization of the Thesis ...... 14

viii

2. Literature Review ...... 16

2.1. An Approach for Evaluation of Density and Environmental Aesthetics .. 16

2.1.1. Density and Crowding Research...... 17

2.1.2. Neighbourhood and Housing Satisfaction Research...... 19

2.1.3. Environmental Aesthetics and Preference Studies ...... 23

2.1.4. Personal and Psychosocial Factors ...... 32

2.2. Market Segmentation: Lifestyle ...... 35

2.2.1. Market Segmentation ...... 35

2.2.2. Definition of Lifestyle ...... 37

2.2.3. Activities, Interests and Opinions ...... 38

2.2.4. Lifestyle in Housing Studies ...... 41

2.3. Research Contribution ...... 41

2.4. Conceptual Framework ...... 43

3. The Case: Calgary, Alberta, Canada ...... 47

3.1. Justification for the Case: Why Calgary? Why Now? ...... 47

3.1.1. Other Contributing Factors ...... 49

3.2. Context: Calgary, Alberta, Canada ...... 49

3.2.1. Location Description ...... 49

3.2.2. Demographic Profile ...... 50

3.2.3. General Housing Profile for Calgary, AB ...... 54

3.3 Summary ...... 57

4. Methods ...... 58

4.1. Document Analysis: Defining the Need for Densification ...... 58

4.1.1. Document Review Findings ...... 59

4.2. Key Informant Interviews: Analysis from the Planning and Development

Industry View ...... 60

4.2.1. Analysis and Findings from Key Informant Interviews ...... 64

4.3. Typology: Building Type and Density Pattern ...... 67

4.3.1. Building Type Typology ...... 68

4.3.2. Density Pattern Typology ...... 71

4.4. Mixed Method: Perceptive Instrument ...... 79

4.5. Perceptive Instrument Design: Lifestyle and Preferences Questionnaire

4.5.1. Part 1: Background, Lifestyle and Housing and Neighbourhood

Preferences ...... 81

4.6. Perceptive Instrument Design: Visual Stimuli ...... 84

4.6.1. Physical Constants ...... 85

4.6.2. Base Model ...... 89

4.6.3. Physical Variables ...... 92

4.6.4. Visual Stimuli: Visual stimuli Development Process ...... 93

4.6.5. Visual Stimuli Descriptions ...... 95

...... 117

4.6.6. Animation ...... 118

4.7. Perceptive Instrument Design: Neighbourhood Visualizations

Questionnaire ...... 119

4.7.1. Part 2: Perception of Density and Neighbourhood Perceptions .... 119

4.8. Participant Sampling ...... 121

4.8.1. Recruitment ...... 121

4.8.2. Participant Profiles ...... 122

4.9. Data Collection Procedure ...... 127

4.9.1. Procedure ...... 127

4.10. Data Analysis ...... 130

4.11. Summary ...... 136

5. Results: Profiling Participants and Exploring Lifestyles ...... 137

5.1. Overview of Participants’ Housing Characteristics ...... 137

5.1.1. General Housing Characteristics of Participants ...... 137

5.1.2. Ownership Importance ...... 140

5.1.3. Lifestyle Activities Interests and Opinions (AIO) of Participants .... 140

5.1.4. Housing and Neighbourhood Location Preferences of Participants

143

5.1.5. Housing and Neighbourhood Item Preferences of Participants .... 147

5.2. Analysis of Lifestyle Dimension ...... 151

5.2.1. Lifestyle Factors: Factor Analysis ...... 151

5.2.2. Lifestyle Clusters: Cluster Analysis ...... 156

5.2.3. Profiling Lifestyle Clusters ...... 158

5.3. Summary ...... 166

6. Results: Urban Form, Density Perceptions and Lifestyle ...... 167

6.1. Findings: Urban Form and Density Perceptions ...... 167

6.1.1. Research Question 1: Preferred Urban Form ...... 167

6.1.2. Research Question 2: Perceived Density ...... 176

6.1.3. Research Question 3: Urban Form Preference and Perceived

Density Relationship ...... 192

6.1.4. Research Question 4: Lifestyle ...... 196

6.2. Discussion: Preferred Form, Density, and the Lifestyle factor ...... 211

6.2.1. Discussion of Perceived Density ...... 214

6.2.2. Discussion of Preferred Urban Form ...... 215

6.2.3. Discussion of the Relationship between Preferred Urban Form and

Perceived Density ...... 219

6.2.4. Discussion of Lifestyles and Preferred Urban Forms ...... 220

6.1. Conclusions ...... 227

xii

7. Results: Characteristics of Preferred Urban Form ...... 228

7.1. Results: Preferred Urban Form Characteristics ...... 228

7.1.1. Research Question 5: Perceived Neighbourhood Characteristics 228

7.2. Discussion: Influential Characteristics ...... 244

7.3. Conclusions ...... 248

8. Summary and Recommendations ...... 249

8.1. Introduction ...... 249

8.2. Empirical Findings ...... 250

8.3. Implications of the Research ...... 254

8.3.1. Planning and environmental aesthetics research ...... 254

8.4. Recommendations ...... 256

8.4.1. Planning Practice Implications ...... 256

8.5. Limitations of the Approach and Methodology ...... 260

8.6. Reflections on Methodology and Questionnaire Questions ...... 261

8.7. Future Research ...... 262

8.8. Conclusion and Outlook ...... 263

References ...... 265

Appendix A: CFERB Approval Letter: Key Informant Interviews ...... 291

Appendix B: Key Informant Interview Protocol...... 292

Appendix C: Key Informant Interview Summary ...... 298

xiii

Appendix D: CFERB Approval Letter: Questionnaire and Visualization ...... 326

Appendix E: Recruitment Poster ...... 327

Appendix F: Part 1 Questionnaire ...... 328

Appendix G: Part 2 Questionnaire ...... 339

Appendix H: Debriefing Script...... 359

Appendix I: Descriptive Statistics ...... 362

Appendix J: Questionnaire Qualitative Analysis ...... 376

Appendix K: Perceived Factors ...... 388

Appendix L: Summary of Document Review Analysis ...... 411

xiv

List of Figures and Illustrations

Figure 1: Rapoport’s Framework for Density Perception adapted and generalized

...... 17

Figure 2: Conceptual Framework ...... 44

Figure 3: Location of Calgary in Alberta ...... 50

Figure 4: Building Type 1: Single Family Detached Dwelling ...... 69

Figure 5: Building Type 2: Duplex / Semi Detached Dwelling ...... 69

Figure 6: Building Type 3: Row House / Townhouse ...... 70

Figure 7: Building Type 4: Staked Townhouse / Low Rise Apartment ...... 70

Figure 8: Building Type 5: Mid-Rise Apartment / Condo ...... 70

Figure 9: Building Type 6: High-Rise Apartment / Condo ...... 71

Figure 10: Density Pattern Type 1: Modern Pattern ...... 73

Figure 11: Density Pattern Type 2: Corridor Pattern ...... 74

Figure 12: Density Pattern Type 3: Nodal or Urban Village Pattern ...... 75

Figure 13: Density Pattern Type 4: Evenly Distributed or Neotraditional Pattern 76

Figure 14: Density Pattern Type 5: Evolutionary Pattern ...... 77

Figure 15: Density Pattern Type: 6: Transit Oriented Development Pattern ...... 78

Figure 16: Land-use Diagram of the Fictional Base Area Used for the Research

Study ...... 90

Figure 17: Visual Stimuli 1 – Street level view ...... 96

Figure 18: Visual Stimuli 1 - Land use Diagram...... 97

Figure 19: Visual Stimuli 1 – Aerial View ...... 98

Figure 20: Visual Stimuli 2 – Street View ...... 99

Figure 21: Visual Stimuli 2 - Land use Diagram...... 100

Figure 22: Visual Stimuli 2 – Aerial View ...... 102

Figure 23: Visual Stimuli 3 - Land - use Diagram ...... 103

Figure 24: Visual Stimuli 3 – Street View ...... 103

Figure 25: Visual stimuli 3 – Aerial View ...... 106

Figure 27: Visual stimuli 4 – Street View ...... 107

Figure 26: Visual Stimuli 4 - Land use Diagram...... 107

Figure 28: Visual Stimuli 4 – Aerial View ...... 110

Figure 29: Visual Stimuli 5 - Street View ...... 111

Figure 30: Visual Stimuli 5 - Land use Diagram...... 111

Figure 31: Visual Stimuli 5 - Aerial View ...... 113

Figure 32: Visual Stimuli 6 - Street View ...... 114

Figure 33: Visual Stimuli 6 - Land use Diagram...... 115

Figure 34: Visual Stimuli 6 - Aerial View ...... 117

Figure 35: Walkthrough Path Used in Videos ...... 119

Figure 36: Neighbourhoods Where Participants Reside ...... 123

Figure 37: Image of Participants in the Taylor Family Digital Library Visualization

Studio ...... 129

Figure 38: Overall Data Collection Procedure Diagram ...... 130

Figure 39: Data Analysis Flow Diagram ...... 132

Figure 40: Most Important Location Factors in Choosing the Ideal Home (%) . 145

Figure 41: Least Important Factors in Choosing the Ideal Home (%) ...... 146

Figure 42: Participants' Housing and Neighbourhood Preferences ...... 150

xvi

Figure 43: Lifestyle Clusters - Housing Preferences Semantic Differential Chart

(Part 1) ...... 164

Figure 44: Lifestyle Clusters - Housing Preferences Semantic Differential Chart

(Part 2) ...... 165

Figure 45: Participant Ranking of Each Visual Stimuli ...... 169

Figure 46: Visual Stimuli Ranking with Paired Preferences ...... 171

Figure 47: Visual Stimuli Preference Ranking with Weighting Applied ...... 173

Figure 48: Summary of Visual Stimuli Preferences ...... 176

Figure 49: Participant Rating of Built Form ...... 178

Figure 50: Participant Rating of Built Form (Grouped) ...... 179

Figure 51: Participants Perceived Population Density Level ...... 186

Figure 52: Participant Perceived Population Density Level (Grouped) ...... 186

Figure 53: Summary of Perceived Built Form results ...... 191

Figure 54: Summary of Perceived Population Density ...... 191

Figure 55: Lifestyle Preferences Visual Stimuli 1: Modern Density Pattern ...... 197

Figure 56: Lifestyle Preferences Visual Stimuli 2: Corridor Density Pattern ..... 198

. Figure 57: Lifestyle Preferences Visual Stimuli 3: Nodal / Urban Village Density

Pattern ...... 199

Figure 58: Lifestyle Preferences Visual Stimuli 4: Evenly Distributed /

Neotraditional Density Pattern ...... 201

Figure 59 Lifestyle Preferences Visual Stimuli 5: Evolutionary Density Pattern 203

Figure 60: Lifestyle Preferences Visual Stimuli 6: Transit-Oriented Development

Density Pattern ...... 205

xvii

Figure 61: Lifestyle Considerations Visual Stimuli Relationship Diagram ...... 225

Figure 62: Semantic Differential Chart of Perceived Neighbourhood

Characteristics ...... 230

Figure 63: Visual Stimuli 3: Nodal or Urban Village Density Pattern - Street View

...... 236

Figure 64: Visual Stimuli 5: Evolutionary Density Pattern - Street View ...... 237

Figure 65: Visual Stimuli 1: Modern Density Pattern - Street View ...... 242

Figure 66: Lifestyle Activities and Interests ...... 364

Figure 67: Lifestyle Opinions ...... 367

xviii

List of Tables

Table 1: Population Growth of Calgary, Alberta, and Canada (1990-2014) ...... 51

Table 2: Housing Unit Increase in Calgary, Alberta, Canada (2003-2013) ...... 52

Table 3: Demographic Characteristics of Calgary, Alberta, and Canada (2011) 53

Table 4: Household Characteristics of Calgary, Alberta, and Canada (2006) .... 54

Table 5: Housing in Calgary, Alberta, and Canada (2006 & 2011) ...... 56

Table 6: Summary of Density Pattern Types ...... 73

Table 7: Density Calculation Constants ...... 87

Table 8: Details Included and Excluded in the Development of Visual Stimuli ... 88

Table 9: Land Use Area Measurements ...... 92

Table 10: Summary of the Materials and Details Added Using Sketch-up ...... 95

Table 11: Visual Stimuli 1 – Building Type Count ...... 97

Table 12: Visual Stimuli 1: Density Calculations ...... 99

Table 13: Visual Stimuli 2 – Building Type Count ...... 101

Table 14: Visual Stimuli 2 – Density Calculations ...... 101

Table 15: Visual Stimuli 3 – Building Type Count ...... 104

Table 16: Visual Stimuli 3 – Density Calculations ...... 105

Table 17: Visual Stimuli 4 – Building Type Counts ...... 108

Table 18: Visual Stimuli 4 – Density Calculations ...... 109

Table 19: Visual Stimuli 5 – Building Type Counts ...... 112

Table 20: Visual Stimuli 5 – Density Calculations ...... 114

Table 21: Visual Stimuli 6 – Building Type Counts ...... 115

Table 22: Visual Stimuli 6 – Density Calculations ...... 116

xix

Table 23: Demographic Characteristics of the Participants ...... 124

Table 24: Ethnic Characteristics of the Participants ...... 125

Table 25: Household Characteristics of the Participants ...... 126

Table 26: Household Income of the Participants ...... 127

Table 27: Type of Current Home ...... 138

Table 28: Length of Current Residency ...... 138

Table 29: Previous Living Accommodation if Changed in the Last 5 Years ...... 139

Table 30: Future Housing Plans ...... 139

Table 31: Importance of Home Ownership ...... 140

Table 32: Importance of Land Ownership ...... 140

Table 33: Ideal Geographic Neighbourhood Location ...... 144

Table 34: VARIMAX-rotated Extraction of Final Three-factor Solution ...... 154

Table 35: VARIMAX-rotated Factor Loadings of Final Three-factor Solution ... 155

Table 36: Inter-item Reliability of Lifestyle Factors: Cronbach’s alpha ...... 156

Table 37: Validation of Cluster with Reduced-sample Clustering ...... 158

Table 38: One-way ANOVA, Brown-Forsythe Test, and Fitch’s LSD

Comparisons: Lifestyle Clusters and Lifestyle Factors ...... 159

Table 39: Visual Stimuli Preference Ranking ...... 169

Table 40: Visual Stimuli Ranking with Paired Preferences ...... 171

Table 41: Visual Stimuli Preference Ranking with Weighting Applied ...... 173

Table 42: Participant Rating of Physical Density on a Scale ...... 180

Table 43: Participant Rating of Physical Density on a Scale (Grouped) ...... 181

Table 44: Participants Perceived Population Density Level ...... 185

Table 45: Relationship between Visual Stimuli Preferences and Perceived

Physical and Population Density ...... 193

Table 46: Relationship between Preference Placement and Overall Density

Perception ...... 195

Table 47: Lifestyle Cluster Preferences of Visual Stimuli 1: Modern Density

Pattern ...... 198

Table 48: Lifestyle Cluster Preferences of Visual Stimuli 2: Corridor Density

Pattern ...... 199

Table 49: Lifestyle Cluster Preferences of Visual Stimuli 3: Nodal / Urban Village

Density Pattern ...... 201

Table 50: Lifestyle Cluster Preferences of Visual Stimuli 4: Evenly Distributed /

Neotraditional Density Pattern ...... 202

Table 51: Lifestyle Cluster Preferences of Visual Stimuli 5: Evolutionary Density

Pattern ...... 204

Table 52: Lifestyle Cluster Preferences of Visual Stimuli 6 ...... 205

Table 53: Lifestyle Cluster by Lifestyle Consideration ...... 206

Table 54: Lifestyle Considerations Analysis: Convenience Consideration ...... 207

Table 55: Lifestyle Considerations Analysis: Family Needs Consideration ...... 209

Table 56: Lifestyle Considerations Analysis: Single Family Amenities

Consideration ...... 210

Table 57: Summary of Discussion Factors ...... 212

Table 58:Summary of Perceived Neighbourhood Characteristics ...... 232

xxi

Table 59: Statistical Significance of Perceived Characteristics of Visual Stimuli

...... 234

1. Introduction

People are passionate about where they live. Homes and neighbourhoods are not only places that provide shelter and security, they are the physical representation of peoples’ personality, culture and lifestyle (Aragonés, 2002;

Robert B. Bechtel, 1997). A change to this environment, no matter how big or small, is of great significance to the individuals who live there. Good and ethical planning practice uses engagement with these residents to help bring them into the process of decision making in order to create better solutions and create buy- in (Seltzer & Mahmoudi, 2012). Even with engagement processes, architects, planners, and real estate developers continue to struggle with community opposition when redevelopment, specifically higher density development, is proposed within existing inner city communities. Often these professionals are coming from the informed perspective that this change is best for the neighbourhood, city, and economy (Dear, 1992). However, in the words of

Einstein “every fact is relative to the observer,” and these facts are not always the perspective of the existing residents. Public engagement has been widely used over the last several decades as a means for communication of proposed changes to existing neighbourhoods, while also providing an opportunity for community members to provide input in the process (Brabham, 2009; Seltzer &

Mahmoudi, 2012). The planning community understands the value of public participation in the planning process, both for the discussion and input provided by community, as well as for the greater acceptance for change from the community members (Brabham, 2009; Seltzer & Mahmoudi, 2012). Challenges

2 exist when planning and development projects are brought forward to the community, even with public engagement, and the projects are consistently opposed. This comes down to the characteristics of the project itself, the density, the form and the overall design, but also how the local residents perceive the proposed plan and increased density developments. In order to move redevelopment projects forward, research into residents’ perceptions of urban environments, specifically in terms of their key concerns, needs to be undertaken. The research presented here addresses this need by investigating resident preferences of urban forms, perceptions of density and perceptions of neighbourhood characteristics.

1.1 Background

1.1.1. The Trend of Urban Sprawl

Since the beginning of the Industrial Revolution there has been a significant shift in the global human population. People in all regions of the world are moving from living in primarily rural communities to living within urban regions

(United Nations, 2014). In 1950, approximately 30% of the world’s population

(765 million out of 2.55 billion people) were believed to live in cities. According to the United Nations, in 2014 the number of city residents has grown to 54% of the population (3.888 billion out of 7.2 billion people) and it is projected to exceed

66% of the global population (6.138 million out of 9.3 billion) by 2050. It is also expected that the majority of future population growth past 2050 will occur in urban regions (2014). In North America, these ratios are even greater with more than 80% of the population in both Canada and the United States now living in

3 urban regions (United Nations, 2014). This influx of population has predominantly been occurring at the periphery of major urban centres in the form of single family, suburban development (Teaford, 2008). This development style was influenced by a series of events that occurred in North America at the conclusion of World War II, including government backed mortgages and the mass construction of tract houses (Teaford, 2008). While suburban development had existed since the 1800s, this was the first time that economics became the driver for this land use pattern (Teaford, 2008). Between 1880 and 1920, suburban development pioneer idealists, such as Ebenezer Howard and the Garden City,

Frank Lloyd Wright and the Broadacre City and Daniel Burnham and City

Beautiful, were the early inventors of the North American suburb (Fishman, 1982;

Jacobs, 1961; Rybczynski, 2010; Teaford, 2008; Wilson, 1989). They were looking for a way that people who were working in the major cities could live in the beauty and tranquility of nature and keep their families away from the health issues of industrializing cities (Fishman, 1982; Jacobs, 1961; Rybczynski, 2010;

Teaford, 2008; Wilson, 1989). In the postwar decade, both the Canadian and

United States governments, offered millions of veterans low interest, long term mortgages in order to purchase homes and restart the economy (Jacobs, 1961;

Rybczynski, 2010; Teaford, 2008). This was the first time that tract housing was constructed across the continent and resulted in a boom of single family home development at the periphery of major cities (Teaford, 2008). Tract housing is where a large parcel, or tract, of land is purchase and subdivided for

4 development of what is typically the repetition of the same house. Thus, the current manifestation of the North American suburb was created.

Suburbia reflected the growing belief in the “American Dream,” where veterans and their families could achieve success by working hard and ultimately having a piece of land to call their own (Teaford, 2008). This idea stems from the way that North America was marketed to immigrants for centuries: wide open spaces, land ownership, away from the confines of European cities and the ability to fashion one’s own destiny (Teaford, 2008). Though suburbia is often thought of as simply a physical condition, it is intrinsically tied to this social and cultural ideal about a certain way of living. The development pattern of the suburbs is the physical manifestation of this ideal (Teaford, 2008)

During the postwar decade, developers gained a strong understanding of how to plan and develop tract housing and learned how to capitalize on the economic efficiencies associated with this type of development (Teaford, 2008).

They also began to take advantage of the concept of the “American Dream” and the idea of home ownership as the ultimate goal in life. This was further supported by the mass media and advertising industries of the time due to the use of idyllic imagery in their advertisements (Fishman, 1987; Rybczynski, 2010;

Teaford, 2008). This combination of marketing and media tactics and economic efficiencies enabled developers to continue to build and market suburban, single family neighbourhoods, which have become a central component and indicator of the North American economy. This economic reality has empowered developers to successfully lobby government to annex farmland for the development of

5 suburban neighbourhoods (Fishman, 1987; Rybczynski, 2010; Teaford, 2008).

Governments have further supported this development through highway expansion programs, the rise of zoning practices that emphasized low-density development, and financial models that support development levies as the primary source of municipal income (Fishman, 1987; Rybczynski, 2010; Teaford,

2008). The proliferation of these neighbourhoods has resulted in the suburban land use pattern seen today, which is problematic due to their environmental, economic and social impacts outlined in the next section.

1.1.2. The Issue and Impact of Urban Sprawl

The suburban development pattern, often referred to as urban sprawl, is characterized by the outward spreading of city development into the rural landscape in the form of low-density, auto-centric, highly segregated housing developments (Dunham-Jones & Williamson, 2011; Teaford, 2008). It is the very goals of the suburban ideal that are becoming the downfall of this development pattern: providing a piece of land for all, personal space close to nature, and privacy and security (McGuire & Sjoquist, 2003; Rybczynski, 2010; Teaford,

2008). At multiple scales, and for multiple reasons, sprawl is having combined environmental, social and economic implications on society.

Cities, by their nature as hubs for movement of goods and resources, are located on some of North America’s prime land for natural resources and agriculture. As urban regions expand, some of the prime resources that are needed to support the growing global population are being lost to development and the associated economics of land prices (Breuckner, 2000; McGuire &

Sjoquist, 2003; Roseland, Connelly, Hendrickson, Lindberg, & Lithgow, 2005).

The unintended consequences of this process are that it destroys the elements that make suburban living attractive: nearby nature and wildlife, recreational opportunities, view sheds, rural character, and air quality (Kearney, 2006; Lorinc,

2006; McGuire & Sjoquist, 2003; Teaford, 2008). It also is a land-use pattern that has led to the proliferation of the automobile and increased automobile use has increased the emissions of fossil fuels, which have been shown to directly affect global warming (Roseland, 2000; Roseland et al., 2005). Other areas of impact, such as construction, energy and waste, the loss of farmland and natural areas, and the linkages to global warming, show the vast impact of sprawl on the environment.

Many researchers have also attributed this land-use pattern to other societal issues. Authors such as Frumkin have linked urban sprawl to low physical activity and increased rates of chronic health problems resulting from obesity (Capon, 2007; Dannenberg et al., 2003; Frumkin, 2002; Frumkin, Frank,

& Jackson, 2004; Perdue, Gostin, & Stone, 2003; Vandegrift & Yoked, 2004).

Others have linked urban sprawl and single family living to social isolationism and chronic mental health issues (Dalgard & Tambs, 1997; Frumkin et al., 2004;

Sturm & Cohen, 2004).

At a societal level the social and environmental implications of sprawl are significant, however, it is the economic challenges that are forcing developers and governments to look at changing the way cities are planned and real estate is sold. The market drives suburban development and the North American

7 economy is dependent on the housing market (Breuckner, 2000; Brueckner &

Kim, 2003), however, municipal governments are experiencing extensive economic challenges to service these sprawling neighbourhoods (Song & Zenou,

2006). As development spreads outwards, municipal governments are forced to develop and fund new infrastructure such as roads, parks, water supply and sewers. Municipalities must also expand and decentralize services and facilities to support the expanding population and city boundaries (Glaeser & Kahn, 2004;

Nechyba & Walsh, 2004; Song & Zenou, 2006). Cities across North America, including many Canadian cities, are having an increasingly difficult time financing their growing services and new capital developments (Lorinc, 2006; Nechyba &

Walsh, 2004; Slack, 2002). Services such as transit, emergency, and recreation are becoming increasingly challenging to expand and operate efficiently and effectively for all citizens (Glaeser & Kahn, 2004; Ladd, 1992; Nechyba & Walsh,

2004; Song & Zenou, 2006). Cities are also spending the majority of tax funded capital dollars on new infrastructure, which is resulting in deterioration of existing infrastructure and facilities because of a lack of funding for existing building renewal (Slack, 2002). As stated, cities facing numerous financial challenges and are unable to pay for sprawl (Slack, 2002).

1.1.3. How Cities are Solving the Issue of Urban Sprawl

Design and planning professions, along with multiple levels of government across North America, are acknowledging that there are environmental, economic and social limits to sprawl (Breuckner, 2000; Dunham-Jones &

Williamson, 2011). To deal with the issues stemming from sprawl, many are

8 adopting and implementing policies and strategies including combination greenbelts, smart growth, new urbanism, transit oriented development, and complete communities. (Breuckner, 2000; Teaford, 2008). While each strategy is different, they all have one fundamental goal in common: to increase density both within existing urban regions and in new development at the urban periphery

(Teaford, 2008).

The process of increasing density in existing areas, referred to as intensification or densification, has many advantages for both municipalities and residents. Density tends to be planned and implemented around significant amenities such as waterfronts, rivers, cultural districts, parks, and downtowns.

Increasing the number of people within these areas maximizes those amenities and stimulates investment that in turn results in better amenities for residents. In addition, increased density supports an efficient mass transit system. By increasing density around transit centres there is an increase in ridership which results in an increase in funding and quality of services (Rybczynski, 2010). This continued investment and improvement of amenities and transit is what makes increased density feasible and is what is needed to attract future residents.

Increasing the zoning allowances for density has also been directly tied to increasing property values which results in an increased tax base (Peiser, 1989;

Slack, 2002). Increasing density enables cities to maximize existing infrastructure without major new investment.

1.1.4. Challenges to Increasing Density in Established Areas

Although the benefits to increasing density are many, often planners and city governments experience significant push back from existing residents who do not want higher densities close to their homes (Talen, 1999; Talen. 2001;

Teaford, 2008). There is also a belief that there is little evidence showing suburban dwellers would shift their preferences to more compact living (Talen,

1999; Talen. 2001; Teaford, 2008). Developers and homebuilders often argue that their buyers are not interested in purchasing more compact housing forms.

The critics of the development industry have two counter arguments to this discussion. The first is that home buyers are not made aware of the options because other models are rarely built, related to the old adage “they don’t know what they don’t know.” The second is that there is hardly any market research on the subject so developers have little empirical evidence that home buyers are not interested in more compact forms. Both these issues are widely held opinions and neither side appears to have empirical evidence. Additionally, there has been minimal academic research on this topic. Regardless, the resistance by residents, often referred to as the “not in my backyard” (NIMBY) perspective, and the arguments of the development industry are two of the most significant challenges that cities face when looking at increasing density.

The NIMBY response experienced in North American cities is a unique paradigm of contradictory ideals. Residents of major cities generally understand the implications of sprawl, especially those with physical manifestations like traffic, and many do not want their city to continue to grow outwards (Teaford,

2008). However, when higher density developments are proposed in existing communities these projects are often opposed by both residents and community groups (Teaford, 2008). A number of reasons have been cited for this opposition: height, privacy, noise, heritage status, neighbourhood character, traffic, parking, impact on property values, impacts of shading, and the socio-economic groups sometimes associated with higher densities (Saegert, 1978). The perspective of existing residents poses a significant challenge to the objectives of planners and municipalities. Over the last two decades, the planning profession has taken a much more proactive approach to counteracting opposition of proposed plans through the use of extensive public engagement (Brabham, 2009; Seltzer &

Mahmoudi, 2012). This process engages local residents in the decision making process and provides a means for planners and community members to work together to develop the best solutions (Brabham, 2009). Even with these approaches to create buy-in, there are still residents that challenge development proposals. By challenging these development proposals, residents increase the time for development, increasing costs for the developer and inherently the cost of units and ultimately limiting the affordability of redevelopment projects. The downstream effect is that suburban developments remain more affordable for the buyer and therefore continue to proliferate.

1.2 Contribution to Knowledge

Based on these challenges, and in order to move in the direction of sustainable development, it is crucial to understand what factors contribute to residents’ acceptance of increased urban density. One potential factor is urban

11 form -- how buildings and neighbourhoods are designed and configured.

Previous research has investigated the role of complexity, façade articulation, streetscape entropy and silhouette complexity (Hur, Nasar, & Chun, 2010; S.

Kaplan, Kaplan, & Wendt, 1972; Lindal & Hartig, 2013; A. E. Stamps, 2002), however to date there is little research that has addressed preference for urban forms in relation to density at a neighbourhood scale. In addition, literature has largely pointed to neighbourhood amenities as pivotal factors in neighbourhood satisfaction (Kearney, 2006; Yang, 2008) and has shown that urban form can be associated with the types of amenities that exist in a neighbourhood (Jabareen,

2006). By looking at urban form at a larger scale, amenities can be attributed to form and begin to play a more significant role in how form and density are perceived.

In addition, understanding factors about the residents themselves can provide insight into how residents’ perspectives and perceptions are shaped.

Previous research has identified both demographic and personal experience as a means to segment residents and understand their perceptions (Parkes, Kearns,

& Atkinson, 2002; Strumse, 1996; Zube, Pitt, & Anderson, 1974). From a different view point, the market research and development industry commonly uses lifestyle as a means to understand consumer behaviour. While there has been housing research that has used lifestyle to determine housing choices (Pinkster & van Kempen, 2002), there has been little use of the lifestyle dimension to understand peoples’ perceptions of the overall urban environment. This thesis aims to contribute to knowledge by addressing the effect of urban form on the

12 acceptance of density at a neighbourhood scale using lifestyle dimension as an indicator.

1.3 Aims and Objectives

The aim of this thesis is to understand which urban forms, all presented at constant densities of 400 people and jobs per net developable hectare, are preferred by residents. In addition, the research aims to understand the characteristics of both the residents and the preferred forms in order to inform strategies for increasing density.

In supporting the purpose of the thesis there are four research objectives:

1. to determine if preferred urban forms are influenced by perceptions of

density;

2. to examine the relationships between resident’s lifestyles and preferred

urban form;

3. to examine the perceived characteristics of the preferred urban forms;

and

4. to examine if the preferred forms coincide with the current knowledge

and trends in the planning and development industry.

1.3.1. Research Questions

In order to achieve these research objectives the following research questions were examined:

Q1: Which visual stimuli represents the preferred type of urban form?

Least preferred type of urban form?

Q2. Which visual stimuli and associated form was perceived as the

densest? The least dense?

Q3. Is there a correlation between visual stimuli preference and the

perceived density?

Q4. Is there a relationship between the determined lifestyle clusters of

participants and preferred visual stimuli?

Q5. Do the perceived characteristics of the preferred visual stimuli indicate

why one urban form is preferred over others?

1.3.2 Research Hypotheses

Prior to completing the research study there were some expected results by the researcher, this section is intended to document those hypotheses:

1. Low rise buildings evenly distributing density throughout the

neighbourhood would be perceived as less dense.

2. Taller buildings with density concentrated in one area would be perceived

as more dense.

3. Neighbourhood forms with lower rise buildings that are more evenly

distributed would be the most preferred.

4. There will be a correlation between preferred neighbourhood form and

perceived density.

5. The lifestyle characteristics of the participants would be indicative of

which neighbourhood form they preferred.

6. The characteristics and complaints industry receives about higher density

developments would indicate neighbourhood form preferences.

1.4 Assumptions of the Study

While the introduction discusses the issues related to urban sprawl and the solution that density can provide, this project does not attempt to prove the validity of densification. That is to way the say that project does not seek to argue the effectiveness of density as a solution to urban sprawl. The project seeks to determine what will make density more palatable to residents of existing neighbourhoods.

1.5 Organization of the Thesis

This thesis presents the background literature review and case study area prior to describing the methodology of the research undertaken. The structure of the document is as follows:

Chapter 2 is a review of the literature as it relates to density and crowding, environmental psychology, perception of urban form and lifestyle segmentation.

The chapter also establishes the theoretical framework for the research and the background on the case location.

Chapter 3 presents an introduction to the case study area and the context in which the research study took place.

Chapter 4 documents the methods used to collect the information required to explore the theoretical framework and discusses how the analysis of the results will be undertaken.

Chapter 5 presents the results related to the profiles of the participants of the research. It also presents the analysis and findings to determine the lifestyle characteristics of the participants.

Chapter 6 presents the results of preferred urban forms, perceived density and the relationship with participant lifestyles. This chapter will also include the discussion of the results.

Chapter 7 presents the results of the perceived characteristics of the most preferred and least preferred urban forms and will provide a discussion of the results.

Chapter 8 documents a summary of the results found in the study and provides the implications and recommendations of the findings for planning practice and environmental aesthetics research. It also provides the limitations of the study, suggestions for future research, and final conclusions.

2. Literature Review

This chapter will review the theory and precedent research in order to identify current gaps in the literature and develop a framework to evaluate this research topic. It discusses three areas of literature that contributed to the development of the research conceptual framework.

2.1. An Approach for Evaluation of Density and Environmental

Aesthetics

The issue of density is a fundamental part of urban planning, urban

development, architecture and urban economics. As a result, understanding the

impacts of increasing neighbourhood density on people has long been an area of academic research with numerous researchers investigating the issue over the last several decades (Baum & Paulus, 1987; Bonnes, Bonaiuto, & Ercolani,

1991; Boots, 1979; Calhoun, 1962; Esther, 1987; Evans, Saegert, & Harris,

2001; Rapoport, 1975; Yang, 2008). While this research was not defined until more recently, it falls into the much wider field of environmental psychology.

Environmental psychology is the interdisciplinary field that investigates the

interrelationship between environments, both natural and built, and the human

affect, cognition and behaviour (Robert B Bechtel & Churchman, 2003; Craik,

1973; De Young, 2013; Gifford, 2007; Stokols & Altman, 1987). Within the field,

there have been three areas that have investigated the issue of neighbourhood

density, each with a different approach: 1) density and crowding research; 2)

neighbourhood and housing satisfaction research; and 3) environmental

17 aesthetic and preference research. In each of these three areas there is a basic framework used to evaluate a lay person’s perceptive response to an environment. In this case, perceived density has multiple accompanying factors that include the measured density, the particular situation, expectations of the situation, the other people in the situation and their behaviours, and an individual’s control over events (Schmidt, Goldman, & Feimer, 1979; Sherrod,

1974). Rapoport (1975) best simplified this relationship with respect to density in his seminal paper Toward a Redefinition of Density; an adapted version is shown in Figure 1. This framework will be the theme around which this review of the literature has been structured. Each of the three research areas were explored in order to develop a framework for this research and assess the role that urban form plays in the acceptance of density. These areas were also explored to further understand the many factors that may positively or negatively affect one’s response to a higher density environment in an effort to inform neighbourhood design characteristics.

Matched to Physical System Psychosocial Factors Affective (Sociophysical Perception (Personal Culture, Perception Construct) Experience and (Good/Not Good) Ideals)

Figure 1: Rapoport’s Framework for Density Perception adapted and generalized Source: (Rapoport, 1975)

2.1.1. Density and Crowding Research

Density and crowding research is one of the earliest areas of study on the

impact of density on people. It started in the 1960s with a number of researchers

18 investigating the effects of high density environments on animals (Calhoun, 1962;

Clough, 1965; Schmidt et al., 1979; Southwick, 1955). These studies focused on the behavioural responses of the animals in relation to the size of their physical space and number of subjects within it. Attempts to generalize the results of animal studies and reproduce results on the human population have shown that the phenomena of human crowding is far more complex (Schmidt et al., 1979).

Human studies have shown that population density, the physical measure, and the cognitive experience of crowding, otherwise known as the perception of density, are not synonymous concepts (Rapoport, 1975; Schmidt et al., 1979;

Stokols, 1972). The perceived density must consider behavioural, functional, and subjective factors that result from human experience and social ideals in relation to the physical measure. (Rapoport, 1975; Schmidt et al., 1979; Stokols, 1972).

More recent studies have taken density and crowding research away from the generalized relationship between the individual and the physical environment to an approach which is contextual (Altman & Rogoff, 1987; Stokols & Altman,

1987) considering three additional factors. First, that a relationship exists between the individual and the sociophysical environment since density perception can also result from social stimulation. A 1972 study by Desor supports the concept that the perception of crowding is more the result of social stimulation and not lack of space (Desor, 1972). Second, that these relationships should be investigated within the context in which they naturally occur (Bonnes et al., 1991). Third, that there is a temporal component to the environmental experience relating both to the physical place and the psychological

19 interpretation by persons within that place (Bonnes et al., 1991; Stokols &

Altman, 1987). In essence, perceived density is a result of the physical construct

and the behaviour of people within the environment in relation to a person’s

perception of that physical environment which is influenced by their experiences

and cultural background in a given context (Bonnes et al., 1991; Leventhal &

Levitt, 1979; Rapoport, 1975; Schmidt et al., 1979; Sherrod, 1974; Stokols &

Altman, 1987).

This previous research suggests that the issues with increased density

exist as a result of a perceptive response to physical and social density rather

than the measure of density itself (Bonnes et al., 1991; Kearney, 2006; Leventhal

& Levitt, 1979; Rapoport, 1975; Stokols, 1972). Based on this concept it could be

argued that adjusting the indicators of density would result in a different perceived density. In this case, changing the urban form, and thereby changing how density is articulated, could result in different perceived densities. In addition to this, by evaluating different psychosocial factors of residents, one could

determine what factors would predispose residents to accept or not accept density.

2.1.2. Neighbourhood and Housing Satisfaction Research

Similar to density and crowding research, neighbourhood and housing

satisfaction research is also based on perceptive response however in this case,

it is in regard to one’s living environment. Also called residential satisfaction, it

can be defined as the degree to which people perceive their residential

environment’s ability to meet their needs and further the attainment of their goals

(Hur & Morrow-Jones, 2008; Hur et al., 2010; Marans & Rodgers, 1975; Mesch &

Manor, 1998; Yang, 2008). Satisfaction with one’s neighbourhood also plays a significant role in their intentions for residential mobility (Hur & Morrow-Jones,

2008). This model is dependent on the objective physical characteristics of the environment, the subjective perceptions or assessments of the environment’s attributes and the evaluator’s background characteristics (G. Galster, 1987; G. C.

Galster & Hesser, 1981; Marans & Rodgers, 1975). Satisfaction research requires that the subjects have spent a significant amount of time in the studied area. In the case of neighbourhood and housing research, this typically means a resident of the area (Yang, 2008).

Researchers commonly agree that there is a gap between the theoretical prospects of neighbourhood planning approaches and the perceptions of the lay people who reside in the physical manifestations of theory (Francescato, 2002;

G. Galster, 1987; Lu, 1999a; Yang, 2008). Satisfaction research has been used to evaluate these characteristics of theoretical neighbourhood development approaches that are supposed to increase satisfaction and quality of life. For example, Talen (1999) has shown that there is a lack of evidence demonstrating the links between physical manifestation of New Urbanism and the social constructs proposed in much of its literature. Other studies have shown that residents prefer typical postwar suburbs in comparison to neotraditional designs

(Morrow‐Jones, Irwin, & Roe, 2004; Nasar & Julian, 1995) indicating that neighbourhood form plays a role in neighbourhood satisfaction.

Other studies have used residential satisfaction as a means to evaluate the effects of density. Generally most of these studies have shown that higher density (regardless of the definition of density) and mixed land-uses have an inverse relationship with neighbourhood satisfaction (Dahmann, 1983; Lee &

Guest, 1983; Marans & Rodgers, 1975; Speare, Goldstein, & Frey, 1975; Yang,

2008). However, as argued by Yang (2008), many of these older studies took place in older established neighbourhoods with entrenched economic and social problems, building forms that would now be considered obsolete, and a lack of community amenities. More recent studies, such as those by Kearney (2006) and

Yang (2008), have shown that in newer neighbourhoods there is a balance between community and natural amenities and higher density. Specifically, not being able to see one’s neighbour (Kearney, 2006) and having enough amenities and convenience to outweigh the problems of density, were seen to be major factors (Yang, 2008). Both these findings support the theory that urban form plays a significant role in the acceptance of higher density environments. Of interest from the study conducted by Yang (2008), is that residential blocks with medium density (defined as a mix of single family, townhouses, and low-rise apartments) were most distinctly shown to be associated with lower residential satisfaction. This result was attributed to medium density not having access to the amenities available in high density neighbourhoods nor having the advantages of low-density living (Yang, 2008).

Previous neighbourhood satisfaction research shows that there are other characteristics and amenities that affect residents’ satisfaction with their

22 neighbourhood. For example research has studied the effects of key amenities including: quality of schools (Parkes et al., 2002), perceived neighbourhood safety (Basolo & Strong, 2002; Mohan & Twigg, 2007; Parkes et al., 2002), nuisance or noise (Mohan & Twigg, 2007), satisfaction with the dwelling itself

(Lu, 1999b; Mohan & Twigg, 2007), fellow residents including racial homogeneity

(Mohan & Twigg, 2007; Morrow-Jones, Wenning, & Li, 2005), and public services like transit (Basolo & Strong, 2002). Traffic issues have been shown to have a significant relationship with neighbourhood satisfaction (Hur & Morrow-Jones,

2008). Areas with lower traffic volumes tend to have higher satisfaction rates while areas with higher traffic volumes are shown to be directly related to lower neighbourhood satisfaction (Bosselmann, 1987; Hur & Morrow-Jones, 2008). It could be suggested that these other factors may also affect one’s perception of increased density if the negative or positive attribute is associated with density.

As shown by Yang (2008), not all density levels have the same amenities or stress factors, and it has been further shown by Jabareen (2006) that not all urban forms have the same services and amenities. This would suggest that indicating amenities along with increased density level could have an effect on one’s acceptance of increased density.

A series of studies have put great significance on physical appearance as a factor for neighbourhood satisfaction (Kaplan, 1985b; Langdon, 1988; Parkes et al., 2002; Sirgy & Cornwell, 2002). However, there is a dichotomy in the literature around this factor pointing to the resident’s background characteristics as the root cause (Hur & Morrow-Jones, 2008). Hur and Morrow-Jones (2008)

23 indicate that newer residents to a neighbourhood specify that physical appearance is most important to satisfaction while more tenured residents point to major social factors such as neighbourhood tension, income levels, crime and racial issues (Potter & Cantarero, 2006).

Based on the overall aims and objective of this research, there are three key theories from neighbourhood satisfaction literature that support the overall investigation: (1) There is a relationship between factors that contribute to overall quality of life, such as amenities and neighbourhood characteristics, and overall neighbourhood satisfaction. This relationship is further exemplified when looking at neighbourhoods of different densities. (2) There is a relationship between neighbourhood satisfaction and the social characteristics of the neighbourhood when closely tied to length of resident tenure. (3) Physical appearances of neighbourhoods play a role in neighbourhood satisfaction. These three factors indicate that neighbourhood urban form may play a role in the acceptance of increased neighbourhood density when key amenities, social and physical characteristics and aesthetic are associated with form.

2.1.3. Environmental Aesthetics and Preference Studies

Environmental aesthetics and preference studies are another area of research that evaluates perceptive responses of environments but from the

perspective of visual quality. It is an area of research that comes from the

landscape planning and landscape architecture field and was first used to

determine what attributes are preferred or important in landscapes (Kaplan,

1985a; Kaplan & Kaplan, 1989). The intent in these studies is to use rated

24 preference of visual environments to systematize the complexity of the human perceptual experience in an effort to ascertain the value of landscape attributes

(Kaplan, 1985a; Kaplan & Kaplan, 1989; Strumse, 1996). As Kaplan (1985a) explains, there is a number of levels of complexity in the human perceptual experience. She states that people don’t only respond to objects but the arrangement and orientation of those objects and the inferences created by the orientation of those objects (Kaplan, 1985a). Further to that, Kaplan (1985a) explains that people respond to objects and visual elements based on a particular situation, their previous experiences and other cultural variables (

Stamps & Nasar, 1997; Strumse, 1996). This is closely related to the basic structure of both density crowding research and satisfaction research.

In this approach, subjects are typically asked to compare a series of visual stimuli in terms of preference level (Kaplan, 1985a; Kaplan & Kaplan, 1989). In some studies subjects are also asked to evaluate specific attributes within the represented environments (Kaplan, 1985a). This enables the classification of complex attributes based on the preferences of the subjects, which demonstrates why the environment is preferred (Kaplan & Kaplan, 1989). In the evaluation of landscape, Kaplan and Kaplan (1989) refer to this approach as category- identifying methodologies where categories are based both on content and the spatial configuration of the landscape. Kaplan and Kaplan developed four categories in which landscape could be grouped: blocked views, spacious and structured, open and undefined, and enclosed. The Kaplans proposed that these categories could be used to predict the preferences among the representative

25 groups (Kaplan & Kaplan, 1989). Due to their evolutionary nature, humans constantly evaluate their surroundings to prepare for effective action, environments that support this action would therefore be preferred (Herzog,

1992; Kaplan & Kaplan, 1989). The prediction was that spacious and structured environments would be most preferred and generally, research has shown this prediction to be accurate (Herzog, 1992).

Herzog (1992) was one of the first to apply the Kaplans’ methodology to urban environments. His research suggests that the model developed by the

Kaplans is supported in urban environments, however, the proposed categorization is insufficient to explain the spatial arrangements in urban environments (1989). The study by Herzog supports the notion that both spatial and non-spatial attributes are important in explaining environmental preferences

(Herzog, 1992). This points to both physical and social constructs contributing to environmental preferences in an urban context. Since this earlier work there have been two areas that have been extensively explored in terms of environmental preference and environmental restorative qualities. The first area that has been prevalently researched is residential façades, building form and the role of complexity in both; the second is parks, vegetation, openness and natural areas in the urban environment.

Facades, Form, and Urban Complexity

There have been a number of environmental aesthetics research studies that have looked at the rated preferences for factors affecting residential façades and form. Notably this research has focused on a few concepts that have been

26 shown to play a role in environmental preference including visual complexity, shape, massing, character, and façade articulation (Akalin, Yildirim, Wilson, &

Kilicoglu, 2009; Alkhresheh, 2012; Heath, Smith, & Lim, 2000; Stamps, 2000,

2004). Stamps (2000), one of the main researchers in this field, has shown that each of these concepts plays a role in urban environmental preference. His research only evaluated each factor at an individual façade or streetscape level, which means that these factors have been evaluated in isolation and have not been shown within a greater urban scale.

Visual complexity is one area that has been investigated frequently and is closely tied to urban form. Visual complexity is the number of elements on a façade or entity and the noticeable difference between the elements (Rapoport,

1990). It directly relates to the amount of detail shown on a specific entity and the amount of information that the individual can ascertain from the detail including both the novelty of the details and the organization of the details (Akalin et al.,

2009). Much of this work has led to the standing hypothesis that the level of detail on buildings is an important part of building preference (Stamps, 1999a).

Previous research studies that have sought to investigate whether the level of complexity in a visual pattern is related to how pleasurable it is perceived to be have shown a dichotomy between results. Some have shown that there is a positive linear relationship between preference and complexity (Devlin & Nasar,

1989; Kaplan et al., 1972), while other studies have shown a weakly negative inverted U-shaped relationship (Imamoglu, 2000). It has been suggested that the inconsistency is largely due to the difference in how complexity was measured or

27 defined in each study (Herzog, 1989; Lindal & Hartig, 2013). Stamps (1999b), for example, investigated the preferences for residential façades by measuring surface complexity, silhouette complexity, and articulation as indicators of preference. In this study it was reported that surface complexity was the best indicator of preference. This supported an earlier finding in which A. Stamps and

Miller (1993) found that the amount of detail had a strong positive relationship with public preferences. This is also supported by other studies (Alkhresheh,

2012; Groat, 1988). In these cases the range of complexity was moderate. Other studies of visual complexity have evaluated solid to void ratio (Alkhresheh, 2012), how buildings fit in their context in terms of site, overall building form and façade detailing (Groat, 1988), restorative quality of architectural variation and height

(Lindal & Hartig, 2013), and the effects of mystery on preference (Ikemi, 2005).

Previous studies have also shown the complexity in a form’s silhouette can be predicative of preferences. Measured in terms of number of turns in the silhouette, it has been shown that complexity has a positive linear relationship with the number of turns in the silhouette. In one study, Smith, Heath, and Lim

(1995) used a mathematical model that creates simulated skylines to evaluate the effect of the proportion and spacing of tall buildings on preferences. Using that same model, Heath et al. (2000) investigate the effect of skyline silhouette and façade complexity on preferences using a series of skyline images. The study found that silhouette complexity had the highest levels of perceived complexity and strongest preferences. Façade articulation was shown to only

28 affect evaluations of skyline complexity and was not related to preference (Heath et al., 2000).

In terms of perceived complexity, ratings of an overall city block may be different than the individual buildings within it (Stamps, 2002). A block with all similar buildings may be perceived as uniform while the buildings themselves may have a high level of complexity. In contrast, a block with all very different buildings can be perceived as complex even though all the buildings are simple

(Lindal & Hartig, 2013). Entropy is a mathematical calculation that objectively summarizes the visual diversity of a block using the frequency of design features so the result can be used for subjective evaluation (Lindal & Hartig, 2013). In its simplest form, if all the buildings in a block are the same, entropy is zero. As the number of features vary, entropy increases. While there have been few studies that have used entropy, all have shown that there is a strong positive linear correlation between entropy and preference (Lindal & Hartig, 2013). This suggest that the level of variation in urban form and more broadly the built form plays a role in overall preference.

In summary, this precedent research indicates that key items that represent factors of urban form, such as façade, silhouette complexity, and entropy, play a role in people’s aesthetic preferences. However, to the knowledge of the researcher there has been no previous research that has linked these concepts to density level or changes in density level. There has been some research that has investigated the influence of height, complexity and style on preferences and restorative quality for specific buildings (Lindal & Hartig, 2013;

Stamps, 1991), but this has not been evaluated at a larger urban scale. Based on this, it can be suggested that factors of urban form do affect environmental preference and that if tied to density levels it could also play a role in preferences.

Height and Enclosure

Enclosure is one of the most fundamental features of urban environments.

As a human creation, urban environments are a construct of creating shelter and are therefore inherently enclosures. The sense of enclosure in an urban environment is created by unbroken blocks of buildings. Studies have shown that there is a strong correlation between the percentage of vertical surfaces with low visual permeability and ratings of enclosure (Stamps, 2005). A. E. Stamps (2005) tested the effect of building height on perceived enclosure. The study found that that there was a nonlinear relationship between building height and perceived enclosure. The enclosure rating was greater between two and four storey buildings than between four and six. Based on this study, and the findings of others from Stamp’s meta-analysis, the height of a continuous block of buildings along with the buildings at the very ends of the block have the greatest effect on sense of enclosure (Lindal & Hartig, 2013).

The physical environment has attributes that affect the sense of enclosure and the feeling of being limited in movement and physical access. This sense

has been tied to the parahippocampal place area of the brain which reacts

strongly to enclosed environments and weakly to enclosed environments with

separate faces (Epstein & Ward, 2009). This reaction has been identified as a

largely primal reaction linked to survival instincts and natural selection (Stamps,

2005). These primal instincts are tied to attention, specifically attention on the

physical features of the environment (Stamps, 2005). Prospect-refuge theory

suggests that the aesthetic interpretation of environments is related to the

survival instincts stemming from the earliest developments in human evolution

(Appleton, 1996). This is the ability to have both the ability to see predators in

time, as well as the ability to take shelter when required. Theoretically this suggests that people would prefer at least some degree of enclosure in their environments (Lindal & Hartig, 2013). However, empirical studies such as

Herzog (1992) contradict this theory. This study found that people did not like

wide open unstructured areas just as much as they did not like enclosed settings

with blocked views. Stamps (2005) has shown that the distance a person can

see greatly affects the sense of enclosure; the further one can see, the lower the

sense of enclosure. Thus, preferences for urban environments can largely be

affected by buildings blocking views at the end of the street.

In summary, height and enclosure research has shown that building height

along street faces increases the sense of enclosure and precedent research has

shown that enclosure has an inverted U-shaped relationship with environmental

preferences. In this relationship too little or too much enclosure are seen as

negative. In addition, the sense of prospect can reduce the overall sense of

enclosure in the urban environment (Lindal & Hartig, 2013). This is in line with

other literature which suggests that people need a balance between sense of

enclosure and having the ability to see into the distance (Speck, 2012) Based on

31 this, it is suggested that urban forms which represent the a high degree of enclosure and those with a low degree of enclosure would be least preferred, while forms that have some degree of enclosure while presenting the opportunity for long views would be more preferred.

Parks, Shared, and Natural Areas

Related to openness, there have been a number of studies that have shown the direct relationship between shared neighbourhood space and its impact on neighbourhood satisfaction (Kearney, 2006). Studies such as those by

Kim and Kaplan (2004), Southworth (1997), Holahan (1976), and Plas and Lewis

(1996) have shown that neighbourhoods with more natural areas and shared community spaces have a greater degree of neighbourhood satisfaction.

Specifically Kim and Kaplan (2004), compared a neotraditional new urbanist community and a traditionally designed community in the same vicinity. The neotraditional neighbourhood was shown to have a greater sense of community largely attributed to community layout, architectural style and community natural areas. A significant amount of research has shown that shared spaces with nature have an even greater role in fostering neighbourhood satisfaction (Kaplan

& Kaplan, 1989; Kearney, 2006). There have been studies that have shown that natural areas in inner-city public housing projects have a positive effect on relationships between tenants and overall lower crime rates (Sullivan & Kuo,

1996; Taylor, Wiley, Kuo, & Sullivan, 1998). Other studies, such as those by

Kaplan, Austin, and Kaplan (2004) and Kaplan (1985b) have shown that

32 specifically the presence of trees, preferably forests, and other well landscaped areas, are important to neighbourhood satisfaction.

A study by Kearney (2006) looked at shared natural space along with density level and evaluated its impact on neighbourhood satisfaction. The study found that density may not have as much of an effect on neighbourhood satisfaction as the presence of natural spaces and that views to natural areas had a greater effect than physical proximity.

In summary, this research indicates that naturalness and open park environments play a role in neighbourhood satisfaction and preference and may play more of a role than density. This suggests that when evaluating the effects of other factors related to density, naturalness and open park areas should be controlled.

2.1.4. Personal and Psychosocial Factors

In examining the literature it is clear that individual differences between

people plays a significant role in the perceptual experience. The literature

indicates a dichotomy between the biological and socio-cultural factors affecting

perceptions (Strumse, 1996). In attempting to overcome this challenge, Bourassa

(1990) identified that there is multiple modes in environmental experience:

biological, cultural and personal. Bourassa’s research points out that landscape

is largely experience by humans on a biological and instinctual level and should

be experienced primarily through the biological mode (Strumse, 1996). By

comparison, urban environments are largely influence by humans and therefore

should only be viewed through the cultural mode (Strumse, 1996). This

33 framework was presented as a means of distinguishing between the three modes of environmental experiences and to present researchers with a way of clarifying the application of future research (Strumse, 1996). For the purpose of this research study the cultural mode will be further explored because of the focus on urban environments.

Experience

Experience is the prior knowledge or recognition of an environment.

Previous research has shown that familiarity and experience is an indicator of environmental preference. Research in landscape has shown that childhood residence and place of residence has an influence on environmental preferences later in life (Daniel, Boster, & Forest, 1976; Zube et al., 1974). It has also been shown that longer term residents versus visitors have a greater understanding of the specific features of their environment and thus have a greater preference level for that environment (Kaplan, 1977). These studies show that there is generally a positive relationship between experience and preferences (Kaplan,

1977, 1985b; Strumse, 1996). While these studies are based on landscape environments, it can largely be assumed that similar experiences could have an effect on the preferences for urban environments.

Individual Characteristics

Previous studies in residential satisfaction and aesthetic preference studies have shown demographics can be indicative of overall residential satisfaction. Younger people are found to be typically less satisfied than older

(Lu, 1999b; Parkes et al., 2002), likely due to insufficient capital to live in the

34 location of their preference. Single women have been shown to be more dissatisfied with their residence than other demographic groupings (G. C. Galster

& Hesser, 1981). Presence of children has been shown to increase satisfaction

(Parkes et al., 2002) typically because it includes increased social interaction for all household members (Dekker & Bolt, 2005), which has also been shown to lead to greater satisfaction (Kasarda & Janowitz, 1974; Parkes et al., 2002).

Higher income and higher educational level have been shown to have higher level of neighbourhood satisfaction (Harris, 2001; Lu, 1999b; St John & Clark,

1984) likely attributed to the availability of capital, meaning more choice of living locations and specific neighbourhoods. Home owners are typically more satisfied than renters (Parkes et al., 2002) similarly this could be attributed to more available choice. There is a mixed effect of ethnicity on neighbourhood satisfaction with some studies showing it has a strong effect (Lu, 1999b) and others showing no effect (Harris, 2001; Parkes et al., 2002; St John & Clark,

1984).

Previous research shows that experience and demographics are predictors of preferences, however these are not indicators that are directly related to housing. The real estate and development industry has, in the past, taken a market research approach in targeting key groups to market residential products (Jansen, 2011). The real estate industry commonly uses lifestyle and life stage to capture cultural information about residents and buyers. To the knowledge of the researcher there has been no previous environmental aesthetics research that has used lifestyle as a predictor of preference.

2.2. Market Segmentation: Lifestyle

Understanding and predicting the preferences and behaviours of people

have long been an area of academic interest. The area of theory and practice

that most regularly approaches this area is the field of marketing, specifically

market research.

2.2.1. Market Segmentation

The first use of market segmentation was in 1956 by Smith, since which

time segmentation has become an essential part of market research and has become very common in fields that target customers (Green & Schaffer, 1998;

Weiss, 2000). A market segment is defined as a group within a market (group of consumers) that can be clearly identified by a series of criteria. The core purpose of market segmentation is to divide the market into several groups that can more clearly be explained (Swenson, 1990). Market segmentation has become a necessary process in consumer behaviour since as society becomes more complex, consumption patterns become more sophisticated (Swenson, 1990).

Typically each market segment has similar consumer needs and each segment has factors that are different than the other segments. There are multiple approaches to market segmentation. For instance, mass media primarily uses demographics to describe segments (Wicks, 1988). Others methods that have emerged in research are geographic, consumption patterns, and product usage (Beane & Ennis, 1987; Lesser & Hughes, 1986). While there are multiple approaches to segmentation, Lesser and Hughes (1986) indicate that

36 psychographic research provides detail rich information in regards to lifestyle.

Psychographic segmentation provides a description of the latent characteristic of a person rather than the outward characteristics where demographic segmentation focuses.

Psychographics is a commonly used term to describe consumer behaviour, however Wells (1975) indicates that there is no generally accepted definition. Generally psychographics is described as quantitative research used to place people on different psychological dimensions for the purposes of segmentation. While referenced, as by Lesser and Hughes (1986), psychographics is not always regarded as synonymous with lifestyles, indicating some confusion between the terms psychographics and lifestyles as they have been used in different ways in previous research. Lawson and Todd (2002) provide the greatest clarity around this issue in their summary of others work, where they describe three ways in which psychographics and lifestyles are closely linked: (1) psychographics refers to quantitative methods by which lifestyle profiles are constructed; as referenced in Wells (1975); (2) psychographics refers to psychological measures, while lifestyles are the constructs of activities and behaviours; as cited in Dorny (1971); and (3) the terms are used interchangeably; as cited in Gunter and Furnham (1992). Lawson and Todd (2002) state that because of these three linkages lifestyle falls into the structure of psychological segmentation.

Lesser and Hughes (1986), along with Plummer (1974), argue that the lifestyle approach provides information about people that researchers had not quantified in past research. Plummer states (1974, p. 33):

“The basic premise of lifestyle research is that the more you

know and understand about your customers the more

effectively you can communicate and market to them.”

Inherently this statement refers to the acknowledgement that the more information available about a person the more you can understand their choices and preferences.

2.2.2. Definition of Lifestyle

Since it was introduced to market research in 1963 by Lazer, lifestyle has become one of the more common market segmentation techniques (Plummer,

1974) and a technique to explain behaviour in multiple fields (Jansen, Coolen, &

Goetgeluk, 2011). Due to this, there are multiple fields that have an interest in the

definition of lifestyle including: sociology, cultural anthropology, psychology,

philosophy, marketing and human geography (Jansen et al., 2011). While there

are differing opinions among the fields of theory on definitions, Veal completed

an analysis to determine the clearest most precise definition (Veal, 2000). Veal

defined lifestyle as: “the pattern of individual and social [behaviour]

characteristics of an individual or group.” He further noted that a lifestyle needs to be shared by a number of people (2000). However in market research,

Solomon’s definition tends to be the simplest and most common. Solomon defines lifestyle as a consumption pattern that indicates how a person chooses to

38 spend their time and money (Holbrook, 1999; Solomon, Zaichkowsky, Dahl,

Polegato, & White, 2013). In all cases, lifestyle is essentially the linkage between an individuals’ activities and behaviours in order to determine differences and similarities between groups. Plummer also cites Lazer’s original definition of lifestyle as “.... a systems concept. It refers to the distinctive mode of living, in its aggregative or broadest sense . . . It embodies the patterns that develop and emerge from the dynamics of living in a society” (Lawson & Todd, 2002, p. 296;

Plummer, 1974, p. 33). It was based on Lazer’s definition that Wells and Tigert developed the most widely used approach to the measurement of lifestyle: the

Activity, Interest and Opinion (AIO) approach (Plummer, 1974). This approach was developed based on the premise that lifestyle is influenced by many factors including age, education, socio-economic status, income, wealth, marital status, presence of children, location, values, and hobbies (Beamish, Goss, & Emmel,

2001). It also takes into account the underlying factors that link these items and the dynamics that living in society has on personal lifestyle (H. Lee, 2005).

2.2.3. Activities, Interests and Opinions

Based on the definition of lifestyle, there are many factors that influence

and define lifestyle categories. Since lifestyles evolve over time there can be no

predetermined set of lifestyles categories (Beane & Ennis, 1987). As a result, the

activity, interest and opinion (AIO) statements approach developed by Wells and

Tigert in 1971 is designed to capture the lifestyle segmentation based on the

concept of the product it is evaluating in relation to the sample group (Beane &

Ennis, 1987).

AIO statements are evaluated based on levels of agreement scales and uses statements about everyday activities such as “I go grocery shopping every day” (Beane & Ennis, 1987). In the first rendition of this approach, Wells and

Tigert (1971) developed a questionnaire with 300 AIO that covered various topics ranging from everyday activities, to interests from media to clothing and opinions on items of general interest (H. Lee, 2005). However, the three dimensions were not defined until Reynolds and Darden provided definitions (Huang, Shih,

Thiruvadi, & Song, 2011; Reynolds, Darden, & Martin, 1974). Activity was defined as a behaviour, an action that is observable and implies interaction.

Interest refers to a positive reaction to an object, event, or topic. Opinion refers to an expression either verbal or written that can identify an attitude about a situation or physical entity (Huang et al., 2011). These are concepts that fall into the three components of AIOs as (Huang et al., 2011, p. 404; Lee, 2005, p. 10;

Plummer, 1974, p. 34):

1. Activities: work, social events, vacation, hobbies, entertainment, club

membership, community, shopping and sports.

2. Interests: Community, family, job, home, recreation, food, fashion,

media and achievements.

3. Opinions: themselves, business, social issues, economics, education,

products, culture and future.

Thus, lifestyle is an integrated concept that requires multiple variables to be constructed and defined (Huang et al., 2011; Plummer, 1974). The multivariate nature of this construct requires a multivariate analysis approach to

40 summarize the results. Some previous studies have used a combination of factor and cluster analysis (Lee, 2005), while others have simply used cluster analysis

(Davis, Allen, & Cosenza, 1988).

While this original study outlined the premise of AIO statements, other research studies have significantly expanded the use of AIO statements in order to understand behaviour. Lee developed 59 AIO statements in order to determine the influence of lifestyle on housing preferences of multi-family housing residents

(Lee, 2005). The 59 statements consisted of 17 activity statements and 42 interest and opinion statements and used a six point Likert agree-disagree scale with a not applicable option. Hawes (1988) developed 151 AIO statements in order to understand the travel-related lifestyle characteristics of older women using a five point agree-disagree Likert scale.

In addition AIO statements have been used in numerous other studies.

Davis et al. (1988) used AIO statement to profile local residents’ attitudes towards tourism. Others have used AIO statements to understand the lifestyle factor in relation to museum visiting behaviour (Todd & Lawson, 2001), behaviours of e-shoppers and non e-shoppers (Allred, Smith, & Swinyard, 2006), over-the-counter drug purchases by elderly citizens (Shufeldt, Oates, & Vaught,

1998), vacation type and guest satisfaction in alpine skiing tourism (Matzler,

Hattenberger, Pechlaner, & Abfalter, 2004), and the healthy living vacationer

(Hallab, 2006). Huang et al. (2011) have also used AIO statements to understand the varying lifestyles of international students at different schools.

2.2.4. Lifestyle in Housing Studies

Lifestyle is not considered a new concept in housing studies. Previous housing studies have shown that often people with the same background and demographic characteristics have differing preferences and behaviours while those with differing backgrounds have similar preferences and behaviours

(Gunter & Furnham, 1992; Jansen et al., 2011; Pinkster & van Kempen, 2002).

Jansen, argues that demographic, socioeconomic, and sociocultural shifts have taken place which have resulted in greater variation of households and an increase in affluent households and lifestyle based subcultures (Jansen et al.,

2011). As a result of this change, these factors are no longer considered sufficient to understand and predict preferences with regards to the built environment and housing (Gibler & Nelson, 2003; Jansen et al., 2011). Heijs,

Carton, Smeets, and van Gemert (2009) stated that lifestyles fill the gap that these other approaches create by reintroducing the human factor and the cultural aspects of society. Jansen et al. (2011) uses the example of how this works by stating that income level and demographics may determine an affordable house with five bedrooms, but lifestyles may determine architectural style.

2.3. Research Contribution

This research study aims to contribute to the body of knowledge by

exploring if neighbourhood level urban form plays a role in how density is

perceived. The departure point for study was that existing urban and suburban

neighbourhoods are being retrofitted with increased density developments and

42 existing residents are having adverse reactions to the environmental change.

The study seeks to identify if there are variables of urban form that will contribute to a greater level of acceptance of proposed increased density development.

Further, it investigated if there are urban form attributes that the lay individual describes to explain why they prefer one form over another.

Previous studies have investigated this issue on a smaller scale. This study evaluated this issue from a neighbourhood scale so as to include neighbourhood characteristics and amenities that have been previously shown to affect neighbourhood satisfaction and preferences. Some of these characteristics include open space, trees, parks, parking, commercial amenities, community amenities etc. The amenities and characteristics were closely tied to the specific urban form types as research has shown that different urban forms support different amenities (Jabareen, 2006).

In describing the affective perception, this study used socioeconomic profiles previously identified in the literature as factors affecting perceptive response such as: experience living in different densities, education, income, age, etc. However, different from previous literature, this study also tested affective perceptions based on neighbourhood lifestyle indicators. From both the marketing and real estate development industry, lifestyle has long been used as a means to understand people’s behaviour. To the knowledge of the researcher there has been little or no research using lifestyle to describe people’s environmental aesthetic preferences or neighbourhood satisfaction.

Previous researchers such as Hur et al. (2010) have measured the environments they were evaluating spatially and others, such as Stamps

(1999b), have used rendering to control variables. To the knowledge of the researcher, through an evaluation of these existing approaches, there has been no research that has used a fully controlled and measurable three dimensional virtual environment to evaluate urban form and density. The opportunity of this approach is to limit the number of variables across multiple urban forms in order to have the ability to measure each different form example to ensure the exact same density.

2.4. Conceptual Framework

The following diagram, Figure 2, represents the conceptual framework that has guided the logic of this research inquiry. As defined by Maxwell and Loomis,

the conceptual framework is a summary of the relevant factors and variables to

be studied along with the expected relationships between them (Maxwell &

Loomis, 2003).The conceptual framework for this research study was developed

out of the basic environmental psychology framework represented previously in

Figure 1 on page 17. It is the basic premise that a perception is informed by a

juncture of both physical factors and psychosocial factors (Rapoport, 1975). To

fully understand a person’s preference for, and perceptions of, an environment,

all of the physical and psychosocial factors must be taken into consideration. In

the case of this study the intent is to combine both perception of density as well

as environmental preference with the concept of lifestyle.

The research question posed in this thesis is framed around the challenges that exist when residential density is increased within existing neighbourhoods. One of those challenges is the resistance amongst existing residents when densification is proposed in their neighbourhood. The resistance has largely been defined by residents and the planning community as an issue of measured density, however, it is the hypothesis of the research that it is how density is perceived that is causing the antagonism towards densification. The intent of the research is to identify if residents have preferences for different neighbourhood form and design characteristics that would help inform what building types should be proposed in what locations in existing neighbourhoods.

Figure 2: Conceptual Framework

In this research study the physical element that is being tested for is residents’ perceptions of neighbourhood form and density. As demonstrate, the literature indicates that there are multiple factors that influence neighbourhood form. For the purposes of this research study, building form, density pattern, density, and neighbourhood design characteristics are the key factors informing neighbourhood form (see Chapter 4). These factors represented in light blue are all interconnected in the relationship with neighbourhood form. As indicated in the literature, context plays a significant role in how these factors are perceived. In order to capture contextual issues, density perception factors become an input from the case study shown in dark blue.

The study uses lifestyles as a way of describing residents’ psychosocial factors, as has been done in previous housing preference studies and as is commonly used in residential real estate market research (Gunter & Furnham,

1992; Jansen et al., 2011; Pinkster & van Kempen, 2002). However, based on previous density perception research, it is known that experience and demographics are also factors that inform perceptions (Rapoport, 1975; Schmidt et al., 1979; Sherrod, 1974). In the framework these psychosocial and socioeconomic factors are represented in dark green.

The framework itself shows the physical factor, neighbourhood form, is influencing three perceived elements: perceived density, preferred form, and perceived characteristics. These perceived elements are being evaluated against the psychosocial factors to determine resident’s reactions and their perceptions.

These perceptions are then evaluated back to the actual physical factors, thus testing alignment between the perceived and the actual.

3. The Case: Calgary, Alberta, Canada

3.1. Justification for the Case: Why Calgary? Why Now?

Calgary, Alberta, Canada, is the case location for this research study.

Calgary is a vibrant young city, with a booming resource and energy based

economy, and has an abundance of skilled and unskilled well-paying jobs. This

environment has resulted in Calgary becoming one of the fastest growing cities in

Canada with between 15,000 and 40,000 people moving to the city each year.

Traditionally this population growth has been accommodated by the rapid

development of suburban communities at the city’s edge. This development,

similar to many other North American cities, has caused Calgary to be

characterized by a dense central business district, containing mostly commercial

buildings, surrounded by kilometers of low density single family residential

neighbourhoods.

Recently, the City of Calgary proposed a fundamental new direction for the

development and expansion of the city in order to mitigate the sprawling

suburban landscape and the financial challenges it is posing for the municipality.

The latest Calgary Municipal Development Plan (MDP) and Growth Management

Strategy propose a reduction of development at the urban fringe and a move

towards more sustainable development patterns (Land Use Planning & Policy,

2009, September). The documents acknowledge that development and

population growth in Calgary is currently almost entirely at the urban periphery

and aims to move towards a target of accommodating 50% of new population

48 and jobs through infill development over the next 60 to 70 years (Land Use

Planning & Policy, 2009, September). The City proposes that this development should occur through the strategic redevelopment and densification of key neighbourhoods.

The Calgary MDP discusses strategies such as sustainable urbanism, complete communities, smart growth, transit-oriented development and others.

Current literature on these topics identifies increasing density as the fundamental element to ensure the success of these approaches (Roseland et al., 2005;

Teaford, 2008). Local Area Redevelopment Plans (ARP) are identifying where increased density should be occurring and developers are proposing higher density developments in areas that had previously been considered suburban.

However, similar to other cities, Calgary has experienced significant push back from residents of established neighbourhoods as these new plans and developments recommending increased density have been introduced. This response, often referred to as Nimbyism or the “Not in My Backyard” response, is not uncommon when new projects or increased density is proposed in existing areas. Typically, people are emotionally attached to their home and neighbourhood and when change is proposed it is an emotional response to the changes that is manifested. It is this situation that makes Calgary an excellent case for investigating this changing paradigm in urban development.

This research study aimed to understand the effect that urban form has on residents’ acceptance of higher density and the effect form has on perceived density. In using a case environment undergoing change and densification, this

49 study was able to gather the perspective of residents who live in an urban environment that is going through change. This research is intended to support the objective of increasing density by determining what the physical factors of urban form are that contribute to greater acceptance.

3.1.1. Other Contributing Factors

The research study took place at the University of Calgary. Due to proximity

and ease of project execution, using Calgary for the case in this study was logical

and the most effective option. Further to the locational factor, there have been some key institutions and associations that have been supportive and interested in this research. These organizations included the City of Calgary, Federation of

Calgary Communities, Calgary Real Estate Board and the Calgary Home

Builders Association. These organizations were asked and supported the advertisement of the research study.

3.2. Context: Calgary, Alberta, Canada

The target area of the study was Calgary, Alberta, Canada. This section will

provide an overview of the case study information.

3.2.1. Location Description

Calgary is located in southwestern Alberta within the foothills region just

east of the eastern slopes of the Canadian Rocky Mountains. Figure 3 indicates

Calgary’s location within the context of the province of Alberta. The

demographics, profiles of resident housing, and housing trends were analysed

50 based on three sources, the 2014 Calgary Civic Census, the 2011 Canadian

National Census, and the Canadian Mortgage and Housing Company.

Edmonton

Calgary

Figure 3: Location of Calgary in Alberta

3.2.2. Demographic Profile

The population of Calgary, based on the 2014 City of Calgary Civic Census,

was 1,195,194 residents with an 11.5% increase over the most recent national

census completed in 2011 by Statistics Canada. Table 1 indicates the population

51 growth municipally, provincially and nationally between 1991 and 2014. During most census periods Calgary has seen much greater growth then both the

Alberta provincial average and the Canadian national average (Table 1). Over the 2011 to 2014 period Calgary has been ranked in the top 10 fastest growing cities in Canada and has been ranked first in terms of large cities. Table 2 shows a summary of the housing unit increases between 2003 and 2013. It indicates that over the last decade more of the new housing units being constructed are in multi-family or multi-unit buildings. Between 2003 and 2013 there was an increase of 16,412 multi-family units in comparison to 2998 single detached dwelling units (Table 2). The fact that multi-family units have grown faster than any other dwelling type indicates that more of Calgary’s population growth is being accommodated through multi-family, multi-unit buildings.

Table 1: Population Growth of Calgary, Alberta, and Canada (1990-2014) 1991 1996 2001 2006 2011 2014 Total Population Calgary, Alberta 692,885 749,073 860,749 956,078 1,071,515 1,195,194 Population Growth Calgary, Alberta 8.1% 14.9% 11.1% 12.1% 11.5% Alberta 7.1% 10.2% 11.9% 10.8% Canada 5.6% 4.8% 5.0% 5.4% Source: (Elections and Information Services, 2014; Statistics Canada, 2012)

Table 2: Housing Unit Increase in Calgary, Alberta, Canada (2003-2013) 2003 2008 2013 Housing Units Single Detached 207,328 208,950 210,326 Ground Oriented 61,491 64,607 66,564 Multi-unit 73,406 85,808 89,818 Total 342,709 359,895 367,264 Housing Unit Increase Single Detached 0.8% 0.7% Ground Oriented 5.1% 3.0% Multi-unit 16.9% 4.7% Total 5.0% 2.0% Source: (City Wide Strategy - Geodemographics, 2014)

Table 3 provides a summary of the demographic characteristics of the

people in Calgary, Alberta, and Canada in 2011. On average, residents in

Calgary are younger than both the provincial and national averages with more residents in the 25 to 34 and 35 to 44 age categories and less in the 55 to 64 and

65+ age categories (Table 3). Calgarians are also better educated than both the provincial and national averages with a significantly higher percentage of residents having a university certificate, diploma, or degree (Table 3).

Table 3: Demographic Characteristics of Calgary, Alberta, and Canada (2011) Calgary, Demographic Characteristics AB Alberta Canada Total population 1,096,833 3,645,257 33,476,688

Gender (% of total population) Male 49.9 50.1 49.0 Female 50.1 49.9 51.0

Age Characteristics (% of total population) 18 to 24 years 9.6 9.7 9.2 25 to 34 years 16.7 15.4 12.9 35 to 44 years 15.7 14.2 13.4 45 to 54 years 15.6 15.4 15.9 55 to 64 years 10.9 11.4 13.1 65 years and above 10.0 11.1 14.8

Education in 2006 Total Population 15 years and over 801,265 2,625,145 25,664,220

No certificate, diploma or degree 18.1 23.4 23.8 High school certificate or equivalent 25.6 26.2 25.5 Apprenticeship or trades certificate or diploma 8.3 10.9 10.9

College, CEGEP or other non-university certificate or diploma 17.7 18.0 17.3

University certificate or diploma below the bachelor level 5.0 4.0 4.4

University certificate, diploma or degree 25.3 17.5 18.1 Source: (Canada, 2007; Statistics Canada, 2012)

Table 4 profiles families and households in Calgary, Alberta, and Canada.

Statistics Canada defines household as “a person or a group of persons (other than foreign residents) who occupy the same dwelling and do not have a usual place of residence elsewhere in Canada” and census family as “a married couple

(with or without children), a common-law couple (with or without children) or a lone parent family” (Statistics Canada, 2012). On average, Calgary has higher

54 median household and family incomes than both the provincial and national medians (Table 4).

Table 4: Household Characteristics of Calgary, Alberta, and Canada (2006) Calgary, Household Characteristics AB Alberta Canada Total households** 384,723 1,256,200 12,437,470

Number of census families in private households 268,840 904,845 8,896,840 Size of Census Family households* 2 persons 136,965 484,825 N/A 3 persons 67,700 213,675 N/A 4 persons 65,275 203,800 N/A 5 persons or more 26,060 97,225 N/A Number of persons not in census families 203,700 616,070 N/A

Average household size Average household size (person per household) 2.6 2.6 2.5 Average family size (person per family) 3.0 3.0 2.9

Economic characteristics in 2005 Median household income (CDN dollars) 67,238 63,988 53,634 Median family income (CDN dollars) 77,658 73,823 63,866 Source: (Canada, 2007) *Census Family: Refers to a married couple (with or without children), a common-law couple (with or without children) or a lone parent family **Household: Refers to a person or a group of persons (other than foreign residents) who occupy the same dwelling and do not have a usual place of residence elsewhere in Canada

3.2.3. General Housing Profile for Calgary, AB

Table 5 provides a summary of the housing characteristics of Calgary, Alberta, and Canada based on the 2006 and 2011 national census. Calgary has a higher percentage of owner occupied housing units (72.8%) than the national average

(68.4%), but has a somewhat low number than the provincial average (73.1%)

(Table 5). Calgary has a higher than the national average number of single- detached houses, and also has a higher than average number of semi-detached dwellings and row houses. Between 2006 and 2011 the number of single-

55 detached dwellings and apartment buildings with more than five storeys are the only housing types trending upwards, indicating these were being built at a greater rate than the other housing types.

Table 5: Housing in Calgary, Alberta, and Canada (2006 & 2011) Calgary, AB Alberta Canada Housing Characteristics 2006 2011 2006 2011 2006 2011 Total Housing Units 384,740 422,875 1,256,200 1,390,275 12,437,470 13,320,615

Ownership Status (% of total occupied housing units) Owner-occupied housing units 72.8 N/A 73.1 N/A 68.4 N/A Renter-occupied housing units 27.2 N/A 26.3 N/A 31.2 N/A

Units in Structure ( as a % of total occupied private dwellings) Single-detached dwellings 57.8 58.7 63.4 63.5 55.3 55.0 Semi-detached dwellings 5.8 6.0 4.8 5.2 4.8 4.9 Row houses 9.1 8.8 7.0 7.0 5.6 5.9 Apartments, duplex 4.2 3.9 2.6 2.4 5.3 5.3 Apartments in buildings with fewer than five storeys 15.8 15.0 14.7 14.2 18.4 18.0 Apartments in buildings with five or more storeys 6.8 7.0 4.4 4.2 8.9 9.3 Other dwelling types 0.6 0.5 3.1 3.5 1.6 1.6 Source: (Canada, 2007; Statistics Canada, 2012) 57

3.3 Summary

Calgary is a rapidly growing city with a strong economy and a number of key policies intended to increase density. Calgary’s inner-city neighbourhoods, where much of this increased density is proposed, are mostly made up of single family detached dwellings. The residents’ of these neighbourhoods bought their homes with the expectation that the neighbourhoods would not change. As change becomes a reality, these residents’ both challenge the developments and have an emotional reaction to change. These conditions make Calgary an excellent case study to investigate the aims and objective of this research study.

4. Methods

This research study was designed as a mixed methods research approach

developed within the context of a specific case study. The case study approach was chosen in order to evaluate this real world wicked problem at a specific point in time and make the phenomenon more manageable (Creswell, 2003; Robson,

1993). Data was collected through a document review, key informant interviews, an online questionnaire and an in-person questionnaire with a digital visualization session. Calgary, Alberta, Canada was selected as the case study both for the proximity to the researcher and for the changing paradigm that exists within its planning and development industry. This chapter presents a detailed account of the methods used to collect and analyse data within this research study.

4.1. Document Analysis: Defining the Need for Densification

In order to identify the changing planning paradigm and the challenges that

exist within the case study it was necessary to understand the policy direction of

the City of Calgary. As identified by Patton, document review and analysis is

frequently used to understand direction and purpose of the organization producing the document (Patton, 1990). In this project, document analysis was used to understand The City’s vision for increasing density and the different approaches used. Over thirty City of Calgary documents were reviewed for common themes and principles in regard to increased density and the associated

urban forms. Key themes and planning outcomes were documented and tallied

based on the occurrences across the documents. Sources included information

59 available on the City of Calgary municipal website as well as information provide to the researcher by key informants.

Specifically, the Calgary Municipal Development Plan (MDP) was reviewed to provide a broad-based vision for density in the city. Other municipal documents including Area Redevelopment Plans (ARP), Transit Oriented

Development (TOD) strategies and corridor studies were reviewed for specific information in regards to the densification approaches used. Other documents used included: City of Calgary public engagement strategies and feedback documents, real estate market demand and forecast documents and market research documents where available. This review provided key insights into the direction of planning for the City of Calgary, but also indicated some of the fundamental challenges that are being faced in regards to community opposition to densification. See Table L1 in Appendix L for a summary of this analysis

4.1.1. Document Review Findings

There were three key findings from the document review which were carried

into the development of the research experiment. Those findings were as follows:

. The City of Calgary is attempting to achieve a minimum of 200 people and

jobs per developable hectare in major activity nodes in an effort to support

multiple transit lines.

. Proposed density tends to range in urban form pattern across the different

planning documents. Inner city redevelopment tends to be one of three

patterns: corridor based, transit oriented development or nodal or urban

village pattern. However, documents related to inner city planning also

acknowledge that development is evolutionary and that it could take many

years for the proposed plan to be realized. Suburban developments tend

to have either a more neotraditional or evenly distributed density pattern,

or a more modern density pattern where higher density developments are

off to the side of the development placing more focus on single family

housing.

. There is a major focus on redeveloping around existing transit lines and

stations as well as along road corridors.

4.2. Key Informant Interviews: Analysis from the Planning and

Development Industry View

As identified through the literature and document review there are a

number of challenges that both the municipality and developers are facing in regards to increasing density. While there are a range of challenges, each city and region has its own processes and specific issues that arise within its resident population. In order to capture the issues that are affecting the case area it was necessary to understand the perspectives of the industry in Calgary. Interviews are intended to gather the perspective of another individual to gain insight into information that cannot be directly observed by the researcher (Patton, 1990). By introducing the expert or key informant aspect, the researcher is also able to gather information from a subjective viewpoint based on the specific experience of the individual (Flick, 2014).

Based on an in-depth literature review and document review, key themes were identified to be further explored through semi-structured key informant

61 interviews. Interview questions were designed to address matters relating to the participants professional experience and opinion as a member of the planning and development industry. Key informants were divided into two groups, the government planning community and the development community. Two sets of questions (Appendix B) were developed based on the two groups. The first was structured for those working within the approval process, typically those working within the municipal government; the second for those working within the development industry who require approval on specific projects.

Key informants were recruited through a combination of purposive and snowball sampling (Creswell, 2003). Purposive sampling enables the researcher to target participants based on specific information at the researchers own discretion, while snowball sampling enables the researcher to evolve the list of participants as new information arises (Creswell, 2003). In this case, developers of specific inner-city projects were targeted based on expected experiences these individuals would have had with community groups. Developers were also contacted based on specific community challenges which had been documented in various media sources. Other contacts were established based on pre-existing relationships through the researcher’s execution of the required course materials and through professional connections. In no cases were there conflicts of interest as the result of pre-existing professional connections. This research included 21 key informants from suburban and inner-city home builders, land developers, private planning firms, municipal government and multi-family developers. All key

62 informants were professionally involved in the planning and development industry in Calgary, Alberta, Canada.

The interviews occurred between September 2012 and April 2013.

Interviews were audio taped and then transcribed. Interviews ranged from approximately 30 minutes to just over one hour. All interviews had the same basic set of questions, however, the structure and flow of each interview depended on the direction taken by the interviewee. In all cases the interviews evolved from the original set of questions and the interviewer used prompting questions to gather information as the discussion progressed. Interviewees were given context around the themes of the discussion prior to the interview but were not given the specific questions (See Appendix B for specific questions).

In accordance with the requirements of the Conjoint Faculties Ethics

Review Board all interviewees signed a consent form prior to answering any questions. Three participants requested that they be identified as member of the development industry or public official and by area of expertise, one participants chose to not have the organization they represent identified, two chose to be cited by position and organization only, and 14 identified they could be quoted by name, position and organization. A listing of the key informants can be found in

Appendix C.

Key informants were asked a series of questions pertaining to their experiences within the profession, professional opinions, critiques and suggestions. Specifically the interviews were intended to identify: (1) the experiences and challenges these professionals had in developing various urban

63 forms at mid-range density; (2) their current understanding of the market for higher density forms in the context of the case study; (3) the information on which decisions for selection of urban form are currently being based; (4) if there is market research on the preferences for urban forms at higher densities, and if it is readily available; and (5) their opinion as to whether urban form plays a role in the acceptance of higher densities. These discussion topics were designed to inform the second part of the research study.

The information collected in the key informant interviews was analyzed for commonalities and discrepancies across the participants. Coding of the interview transcripts was developed based on the themes of the line of questioning.

Additional coding was added based on the progression of the interviews. Similar experiences, opinions and challenges among participants were documented as were key differences.

The information synthesized from the key informant interviews was used to inform three areas of the research inquiry. The previous experiences and projects from industry members were used in the development of the building form typology. It was also used to inform what specific perceptive and neighbourhood design factors are at issue within the case area. Key informants were asked what forms they felt would be most preferred within the case area and this information was compared with what residents said was the most preferred form.

4.2.1. Analysis and Findings from Key Informant Interviews

There were a number of key findings from the key informant interviews

which were used in the development of the experiment. The following section

outlines the findings from the key informant interviews.

Market Research: Some key informants indicated that the development industry undertakes some market research to ascertain buyer preferences for various housing products. However, many also indicated they make decisions on future

development based on products that have sold previously. Four of the key

informants indicated that the primary method for conducting this research was

with current buyers entering their show rooms or from previous purchasers of

their housing products.

Lifestyle: Key informants stated that demographics have not been an effective

indicator of buyer choices for both condos and single family homes. Key

informants stated lifestyle and life stage have been far more effective in both

ascertaining preferences and predicting buyer behaviour.

Neighbourhood and Building Characteristics that People Prefer: The key

informants were asked what characteristics increased residents’ acceptability of

higher density areas:

 Street Trees – key informants indicated that in their experience the

presence of street trees, especially mature street trees, reduces the visual

impact of higher density building. Informants also suggested that street

trees increase the sense of safety on the street which they felt was

important to residents.

 Amenities – key informants discussed amenities from two perspectives.

Firstly, they indicated that having a sufficient number of amenities allows

for smaller units and personal space. Secondly, they also stated the

importance of the positive relationship between the increase in density

and the increase of community and private amenities.

 Public Green Space – experts indicated that having sufficient public green

space is essential to balancing higher density buildings.

 Façade Variation – key informants suggested that the greater the variation

in the façade of the buildings and the frequency of material changes and

breaks would increase desirability. Some experts suggested this related to

people’s perceptions of single family homes.

 Ground oriented buildings – several key informants indicated that there

appears to be a greater preference for residences where residents will

have their own front door at the street level.

Challenges with Community Member Opposition: The expert key informants were asked about the complaints and issues they have experienced in engaging communities with proposals for increased density developments.

 Traffic – was widely mentioned as a significant issue brought up by

community members when redevelopment proposals and Area

Redevelopment Plans were brought forward to the community. Key

informants indicated that community members often associate higher

density buildings with increases in traffic.

 Parking – key informants indicated that often residents do not believe that

the provided parking in proposed developments will be sufficient and that

building residents would park on the street.

 Height – it was indicated as an issue for community members especially

when the proposed buildings are out of context of the community and are

believed to impact visual privacy. Key informants suggested that the

transition between building types can have a significant impact on

residents’ perceptions of density such as the difference between

townhomes and four to six storey apartment buildings.

 Mass and shadowing – key informants suggested that the mass of the

building and the impact of the mass on shadowing is a major concern for

residents.

 Land value – experiences have shown residents have the perception that

increased density will reduce the values of adjacent properties.

 Multi-family stigma – expert informant indicated that residents don’t often

state this perception but there is often an underlying tone that multi-family

housing is seen as less expensive and as lower income and/or rental. This

has been indicated as an issue of residents that would have less

ownership over the community.

 Density – some experts have experienced community representatives

challenging the proposed developments based on density even though the

density is within the parameters of the zoning of the site.

4.3. Typology: Building Type and Density Pattern

In order to construct the visual stimuli needed to test perceptions of density, and to understand the affect that urban form has on those perceptions, the major physical urban form factors needed to be defined. Building types, forms and orientations, as well as density patterns, are the most visible factors of urban form and have the most significant impact on overall neighbourhood form

(Scheer, 2010). For the purposes of this research, two factors were focused on in order to delineate differences between neighbourhood urban forms: building type and density pattern. Each of these factors is comprised of a number of variables and it is necessary to simplify these factors to support the measurement and classification of neighbourhood forms.

Typologies are a method of systematically classifying types of items or

concepts in order to define similarities and differences between each (Newman,

Ridenour, Newman, & DeMarco Jr., 2003). They are designed to simplify and impose structure on complex concepts with multiple variables for organizational and communicative purposes (Guest, 2012). Due to the complex nature of

building design and planning, typologies are commonly used to define elements

within these areas.

4.3.1. Building Type Typology

Defining building form and type is a highly complex endeavour due to the

ever changing trends in the housing market and building design and technology

(Scheer, 2010). It is for this reason that there has been little work done around

classifying the extensive list of building forms. Rather, most research has either

defined a specific typology for the context of the specific area of study or has

described an existing state; few have developed typologies to describe future

states. Dunham-Jones and Williamson have developed one example of a future

state typology which used a transect approach that resulted in a typology with 18

different building types (Dunham-Jones & Williamson, 2011). The intent of this

typology was to identify the range of different residential housing types that could

be used to retrofit existing communities. Dunham-Jones and Williamson also

began to take into account factors of building form in this typology. In the early

stages of investigating building form close to 40 variables were identified, which

made creating a typology based on building form complicated. For the purpose of

this research it was determined that a simplified typology based on variables of

building type, rather than building form, would be most effective. Building form is

dictated by neighbourhood form, which will be discussed further in section 4.6.

The development of this building typology used building type variables identified

by Dunham-Jones and Williamson (2011), as well as input from the key informant

interviews. The primary variables used to document this typology were based on

building height, number of units and unit orientation.

The typology focused on residential land-uses. With this approach there were six basic building types: single family detached, duplex / semi-detached, row house / townhouse, stacked townhouse / low-rise apartment, mid-rise apartment / condo, and high-rise apartment / condo (see

Figure 4 to Figure 9).

Figure 4: Building Type 1: Single Family Detached Dwelling

Figure 5: Building Type 2: Duplex / Semi Detached Dwelling

Figure 6: Building Type 3: Row House / Townhouse

Figure 7: Building Type 4: Staked Townhouse / Low Rise Apartment

Figure 8: Building Type 5: Mid-Rise Apartment / Condo

Figure 9: Building Type 6: High-Rise Apartment / Condo

4.3.2. Density Pattern Typology

Similar to building type, to determine the density patterns that could exist at

the same measured density, the different pattern types needed to be classified. A density pattern typology was developed using information collected through the key informant interviews, existing neighbourhood developments, and patterns

documented in literature. The classification was based on the locations of the

highest concentration of people and jobs per hectare. The typology developed

was then used to develop the visual stimuli used in the perceptive instrument.

Urban form is the result of aggregating a number of repetitive element

based concepts. Jabareen identifies some of these concepts as street pattern,

block size and shape, lot configurations and setback, and location of parks and

public spaces (Jabareen, 2006). The combination of these different concepts

ultimately results in patterns which can be grouped at a conceptual level

(Jabareen, 2006). Jabareen does this in his 2006 paper, Sustainable Urban

Forms: Their Typologies, Models and Concepts, where four sustainable urban forms are identified based on specific characteristics. Using a similar approach, this typology is intended to summarize the pattern of physical density at the neighbourhood level. There is little documentation around this specific topic, however, the many strategies for neighbourhood design and densification identified over the last several decades were used as inputs.

There were four significant inputs that contributed to the development of the density pattern typology: (1) existing research which identified strategies and approaches to developing more compact and sustainable cities; (2) a series of figure-ground diagrams of existing neighbourhoods were completed in order to determine some of the existing density patterns within Calgary; (3) existing densification strategies identified in the City of Calgary planning documents; and

(4) development and densification approaches that were identified through the key informant interviews.

From the inputs outlined, six different density patterns were established, shown in Table 6. The following sections will describe these patterns in greater detail.

Table 6: Summary of Density Pattern Types

Number Name Density Pattern Type 1 Modern density pattern Density Pattern Type 2 Corridor density pattern Density Pattern Type 3 Nodal or urban village density pattern Density Pattern Type 4 Neotraditional or evenly distributed density pattern Density Pattern Type 5 Evolutionary density pattern Density Pattern Type 6 Transit-oriented development density pattern

Density Pattern Type 1: Modern Pattern

Location of higher density buildings

Figure 10: Density Pattern Type 1: Modern Pattern

The modern pattern density type was developed based on how

neighbourhoods have been designed over the last several decades. It is based

largely on Clarence Perry’s concept of the neighbourhood unit, which focuses on

the increased usage of the car and is designed around supporting one school,

typically located in the middle (Hodge, 1986). Over time, the neighbourhood unit

has become the strongest organizational principle used in community planning

(Hodge, 1986). While not the original intent of the concept, higher density buildings are typically zoned to the outer areas of communities, often on large,

awkwardly shaped land parcels. Usually these buildings are located close to

major roadway and commercial strip malls. Also, the building form is inward

74 focused rather than street focused. Figure 10, indicates in relation to the shown road pattern, where the higher density buildings would be located.

Example neighbourhoods in Calgary: Fairview, Glamorgan

Density Pattern Type 2: Corridor Pattern

Location of higher density buildings

Figure 11: Density Pattern Type 2: Corridor Pattern

The corridor density pattern was developed based on strategies which involve increasing density along major corridors. It is commonplace for municipalities to encourage development of larger density developments along major roadways and major transit corridors (e.g. light rail transit and bus rapid transit). Rollin Stanley, General Manager of Planning, Development &

Assessment at the City of Calgary commonly cites that his primary solution for increasing density in Calgary is encouraging greater density along major corridors. This is a common strategy in locations that have existing commercial corridors where gentrification is the approach to increasing density and property values (Smith, 2002). Although the intent of this kind of densification is not solely to increase density along the corridors, the division between the original development and the gentrified corridor tends to be fairly visible. Figure 11 indicates how this pattern may appear.

Example from Calgary: Sunnyside/Hillhurst

Density Pattern Type 3: Nodal or Urban Village Pattern

The nodal or urban village density pattern is based on strategies that involve increasing density around a focal point such as a public square, park, major intersection etc. The centre of these neighbourhoods are the focal point where most of the mixed use buildings are located. The general intent is that the neighbourhoods contain all the necessary amenities within the core area. Often, in practice, this density pattern does not necessarily result in all the higher density at the centre but rather is distributed along the corridors immediately adjacent to the central area. As a result this typology also tends to have a greater degree of variation in building height and shape. Figure 12 indicates how this pattern may appear.

Example from Calgary: Bridgeland, Seton Location of higher density buildings

Figure 12: Density Pattern Type 3: Nodal or Urban Village Pattern

Density Pattern Type 4: Evenly Distributed or Neotraditional Pattern

Location of higher density buildings

Figure 13: Density Pattern Type 4: Evenly Distributed or Neotraditional Pattern The evenly distributed or neotraditional density pattern was developed based on two realms of thought. The first stems from new urbanism, which is a neighbourhood design approach intended to focus on community, placemaking and a more compact way of living. It tends to return to urban design principles which were prevalent prior to the advent of the automobile where all areas of neighbourhoods and villages were walkable. This approach results in higher densities than typical neighbourhood development and because of the traditional architecture associated with these neighbourhoods, the buildings tend to be between four and six storeys evenly distributed throughout. However, in practice, new urbanist neighbourhoods also have a significant amount of single family housing (Jabareen, 2006). The second area of theory is the idea of compact cities, which is largely based on development principles used in European cities

(Jabareen, 2006). Similar to new urbanist principles, compact city concepts promote a middle range density that is more evenly distributed, promoting a greater mix of uses within the different buildings (Jabareen, 2006). The approach

77 encourages walkable streets with mixed use throughout the neighbourhood.

Figure 13 indicates how this pattern may appear.

Example from Calgary: Garrison Woods

Density Pattern Type 5: Evolutionary Pattern

Location of higher density buildings

Figure 14: Density Pattern Type 5: Evolutionary Pattern The evolutionary density pattern was developed based on examples of neighbourhoods where the entire neighbourhood has been rezoned for increased density. Over time new buildings are constructed as developers are able to purchase and consolidate land to create parcels large enough to economically support the development. While this is not a well-documented process, it results in a unique land-use pattern with higher density buildings in locations throughout a neighbourhood with smaller low density buildings in between. As documented by many authors, this pattern would not be conducive to compact sustainable development but is a necessary step in the evolution of neighbourhoods. Figure

14 indicates how this pattern would appear in a neighbourhood context.

Example from Calgary: Victoria Park / Connaught / Beltline

Density Pattern Type 6: Transit Oriented Development Pattern

Location of higher density buildings

Figure 15: Density Pattern Type: 6: Transit Oriented Development Pattern The Transit Oriented Development density pattern was developed based on a commonly used practice for densification, which is to increase residential development around rail stations (Boarnet & Crane, 1997; Jabareen, 2006).

Typically these types of developments have a transit station along with public spaces in the centre (Bernick & Cervero, 1997; Jabareen, 2006). The highest density land uses are zoned in the immediate vicinity of the transit station with a gradual decrease in density further away from the transit station (Bernick &

Cervero, 1997; Cervero, 2004; Land Use Planning & Policy, 2009, June 1). Often this approach results in the highest densities in the immediate vicinity of the transit station being constructed first. Lower density buildings that enable integration into the existing community take longer to develop. This results in a rather stark pattern as shown in Figure 15.

Example from Calgary: None specially exist however Heritage Station,

Brentwood Station and Westbrook Village are all in development stages.

4.4. Mixed Method: Perceptive Instrument

Mixed method research is broadly defined as when a researcher “collects and analyzes data, integrates the findings, and draws inferences using both qualitative and quantitative approaches or methods” in a research study

(Tashakkori & Creswell, 2007). Based on the definition, the entire research project can be defined as a mixed method study based on the requirement to collect both qualitative and quantitative data, however this part of the project is specifically a mixed method collection technique. The information to be collected includes: characteristics of residents including lifestyles, perceptions of density and urban form along with the descriptive reasons for their preferences, and the representation of urban form and its design characteristics.

As noted in the literature review, there is no one method that has been developed that can capture the information required for this research study. Due to this, a new research methodology was created based on methods developed in other research studies including crowding studies (Gillis, 1979; Gillis, Richard,

& Hagan, 1986), stated preference studies (Audirac, 1999; Howie, Murphy, &

Wicks, 2010; Lee, 2005), visual preference studies (Akalin et al., 2009;

Alkhresheh, 2012; Conrad, Christie, & Fazey, 2011; Herzog, 1989, 1992; Nasar

& Stamps, 2009; Stamps, 1999b), satisfaction studies (Hur et al., 2010; Kearney,

2006; Permentier, Bolt, & van Ham, 2011) and perceptions studies (Kaplan,

1985a; Kaplan et al., 1972). Specifically, three studies provided the methodology framework around which this research study was developed. Hur et al. developed a research study looking at neighbourhood satisfaction as it relates to

80 openness in real neighbourhoods which used spatial measurements using GIS and compared that to the perceptions of residents collected in a questionnaire format (2010). Lindal and Hartig developed a study which evaluated how architectural variation and building height affect restorative experiences. This study used a combination of computer generated images and questionnaires to test perceived restorative qualities of urban environments (2013). Lee conducted a study on the influence of lifestyle on housing preferences of multi-family housing residents using a quantitative questionnaire approach (2005).

A two part research instrument was developed for the study based on the precedence identified in the literature. Each of the two parts included the use of a questionnaire with both defined answer and open ended questions. Part 1, described in section 4.5. was a questionnaire used to understand participants’ demographic profiles, lifestyles, stated housing and neighbourhood preferences and previous and future housing experiences. Part 2, described in sections 4.6 and 4.7, used a combination of visualization stimuli and a questionnaire. The visual stimuli were used to establish a controlled representation of an urban environment where constants and variables could be established and measured.

The questionnaire was used to capture participants’ density perceptions, perceived characteristics, preferred urban form, and qualitative reasoning for these perceptions and preferences. The instrument was reviewed and approved by the University of Calgary Conjoint Faculties Ethics Review Board (See

Appendix D for approval letter).

4.5. Perceptive Instrument Design: Lifestyle and Preferences

Questionnaire

This section will discuss the development of the first part of the perceptive

instrument design. Part 1, as it is named throughout the document, was designed

to be a take home questionnaire which participants could complete online or on paper. The online option used the Fluid Surveys platform.

4.5.1. Part 1: Background, Lifestyle and Housing and Neighbourhood

Preferences

Section 1: Lifestyles

The first section of this study was designed to determine participants’

lifestyle. The section included 73 activity (see Appendix F), interest and opinion

(AIO) statements related to housing and neighbourhood choices. The statements

were developed using a five level Likert agree or disagree scale: strongly

disagree, disagree, neutral, agree, and strongly agree. The first 35 statements

were about activities and interests that participants would participate in or act

upon in their home or neighbourhood. The format of the activity statements were based on AIO statements found in previous research studies, however most of the statements were created by the researcher. In order to determine what activity statements should be included, a series of expected lifestyle categories were created; these categories were: the homebody, the socialite, the athlete, the traveler, the shopaholic, the workaholic and the designer. While it was not the expectation that participants would fall exclusively into these categories, these

82 categories were used to ensure a range of lifestyles were included. Specific activity items that were related to cooking and eating out were based on a previous study on kitchen space (Beamish et al., 2001). Many of the activity based items related to the home were based on those in a study related to lifestyle and multi-family housing (Lee, 2005). All the statements used were adjusted to ensure they covered all neighbourhood and housing types.

The other 38 statement were focused on opinions related to housing.

Many of these statements were written based on previous studies in which AIO statements were used (Beyer, 1959; Cutler, 1947; Lee, 2005; Mitchell, 1984;

Wells & Tigert, 1971). Similar to the activity and interest based statement, a series of categories in regards to opinions about housing and neighbourhoods were created to ensure the multiple opinions in regards to housing were covered.

These categories were: health and wellness, environment, family-focused, security, aesthetic, maintenance and disregard. The statements from previous studies were grouped into these categories and additional opinion statements were added.

Section 2: Housing and Neighbourhood Preferences

In the second section of the part 1 questionnaire, participants were asked about their preferences when choosing their ideal home and neighbourhood.

Participants were asked what features were most important when selecting a new home. Participants were also asked about their ideal neighbourhood location with a multiple choice question specifying: downtown, inner-city, established suburbs, new suburbs, small town, or rural area. Participants were also given 15

83 factors (see Appendix F) that some may consider when selecting the location of their home and were asked to rank their five most important factors and five least important factors. The last question was a series of five item semantic differential scale questions. Semantic scales typically are either a five or seven point scale with opposite descriptors or adjectives on either end (Rosenthal & Rosnow,

1984). The semantic differential scale was developed as a graphic based scale to measure subjective meaning from given stimuli (Osgood, Suci, &

Tannenbaum, 1957). It has often been used in marketing research and the measurement of opinions on particular social constructs (Brinton, 1961;

Rosenthal & Rosnow, 1984). The semantic differential scale questions were used to determine participants’ preferences in regards to housing and neighbourhood design factors. 36 factors were identified and grouped in to the seven following categories: commercial amenities, community amenities, neighbourhood structure, public realm, safety and privacy, neighbourhood demographic, parking, and transportation.

Section 3: Current and Past Living Experience

The third section of part 1 pertained to current and past living information.

Questions in this section were developed based on a questionnaire developed by

Lee for a study which evaluated multi-family housing preferences in relation to lifestyle (2005). While these questions were the basis of the section, the questions were adjusted to cover a range of housing types. Specific questions pertained to length of residency in their current home, type of current home, current home ownership situation, current neighbourhood, and previous house

84 type if moved in the last five years. A question was also asked to understand future housing intentions.

Additional questions were created based on expected factors that may influence participants’ perceptions of density. Housing types previously lived in, along with influential childhood home, may have a relationship with perceptions of density. The importance of home ownership and the importance of land ownership were also considered factors in preferences for neighbourhood form and density.

Section 4: General Demographic Information

The final section in part one was designed to collect demographic and socioeconomic information. Questions pertained to gender, age, education, household income, employment situation, marital status, household size, household structure, and ethnicity.

4.6. Perceptive Instrument Design: Visual Stimuli

The research study used a series of six computer generated simulations

of neighbourhoods and streetscapes. The purpose of these visual stimuli was to

test for the most preferred urban form when the density is held constant. There

are many variables that can affect neighbourhood urban form so in order to test

residents’ perceptions and preferences for density and urban form, all other

major factors needed to be controlled.

4.6.1. Physical Constants

In order to control the number of variables and test for the specific

variables that inform the research questions, a number of constants were defined

from the onset of the development of the visual stimuli. The constants were

defined in three categories: structural, density and visual.

Structural Constants

While structural and land-use factors play as significant role in

determining neighbourhood urban form, the intent of this research was to

evaluate how residential building form can affect perceptions of density in the

densification of existing neighbourhoods. This requires that the base area and

structure of the neighbourhood remain constant. For the purposes of this

research study the structural factors that were constrained were as follows: street

pattern, laneway locations, block shape and size, one elementary school always

in the same location, one grocery store always in the same location, the

centralization of commercial and mixed land uses, and the amount and location

of public green spaces.

Density Constants

The most significant factor which needed to be constrained was the

density of the overall area. The density used was identified using information

from City of Calgary planning documents. The Calgary Municipal Development

Plan (MDP) states that a major activity centre and urban corridors with one or more primary transit stations should have a minimum of 200 people and jobs per

86 gross developable hectare (Land Use Planning & Policy, 2009, September).

While few area redevelopment plans overtly state the density levels projected by the plans, calculations indicated that these plans attempt to achieve densities greater than those identified in the Calgary MDP. For this reason the researcher chose to use a density of 225 people a jobs per gross developable area as the constant used in this research. For the purposes of the research this number was converted to a net developable density based on the base stimuli which will be discussed in section 4.6.2. This conversion equates to 400 people and jobs per net developable hectare.

In order consistently measure this density across all the visual stimuli, constants which can be used to measure density also needed to be established.

Table 7 provides a summary of the values which remained constant across all calculations. An assumption was made that every household should be calculated based on the actual average number of people per household.

According to Statistics Canada 2011 national census that number is 2.61 people per private household (Statistics Canada, 2012). An average size for single detached, semi-detached and town house was assumed to be 2200 square feet

(204 square metres) and an average for condos and apartments were assumed to be 850 square feet (79 square metres). The number of jobs associated with the elementary school was defined as 125 jobs (this was based on the researcher’s previous experience as a facilities manager for a school board). The average number of jobs per area was defined as one job for every 322 square feet (30 square metres) (De Chiara & Koppelman, 1975). This was defined based

87 on the assumption that there would be a combination of both office space and retail space, where office space ranges between 1 job per every 100 – 200 square feet and retail ranges between 1 job per every 200-400 square feet (De

Chiara & Koppelman, 1975).

Table 7: Density Calculation Constants Constant Measurement Additional Info Average # of people per household 2.61 people*

Average size of each single detached, semi-detached and town Based on a 1500 – 3000 2200 sqft house sqft range Based on a 600 – 1200 sqft Average size of each condo / apartment 850 sqft range An elementary school employs 125 people

Average number of jobs per area 1 job / 322 sqft 30 sqm

*(Statistics Canada, 2012)

Visual Constants

The use of three dimensional visualizations in planning is not an explicitly

new concept however, with recent technological advances, its use has become

much more common. There have been a number of research studies that have

used visual stimuli to test perceptions in urban and landscape environments and

each has had a range in terms of the use of realism (Alkhresheh, 2012; Cervero,

2004; Conrad et al., 2011; Fisher-Gewirtzman, Burt, & Tzamir, 2003; Lindal &

Hartig, 2013; Nasar & Stamps, 2009; Rohrmann & Bishop, 2002; Stamps, 1994;

Stamps, 1991, 1993, 1999b). Recently there has been some studies in both

urban planning and landscape architecture around the amount of detail and

realism required to accurately represent an environment and test a lay person’s

reaction to that environment (Appleton & Lovett, 2003; Bandrova & Bonchev,

2013; Hofschreuder, 2004; Lange, 2001). This research has not indicated a

88 definitive answer but has specified that a greater degree of realism in the representations is preferred (Bandrova & Bonchev, 2013; Lange, 2001). For this research study it was determined that a greater degree of detail and realism was required. Table 8 provides a summary of the details that were included and excluded in the development of the visual stimuli. Specific details were only included on the higher density building types and were not included on single family detached dwellings, semi-detached dwellings and townhomes. This decision was made based on the scenario that these higher density buildings would have been built after the smaller dwellings. Also the smaller dwelling types tend to have more style options associated with them and will start to affect perceptions and preferences for a particular aesthetic.

Table 8: Details Included and Excluded in the Development of Visual Stimuli Included Excluded  Mullions detailed with material  Railings  Materials applied to ground (grass, asphalt, and concrete)  Rendering of materials  Materials applied to buildings with colour  People on the streets  Curb, sidewalk and road differentiation  Building signage  Glazing with transparent material applied  Street furniture (benches, tables, garbage cans, bus stops)  Street lights  Trees  Shrubbery  Street signage  Cars and buses on the street  Balconies

*Note this list may not be exhaustive

Some details were included or excluded based on precedent research or

the characteristics which were being tested. Vegetation has been shown to have

an effect on neighbourhood perceptions and preferences (Smardon, 1988) and therefore was controlled with approximately the same number of trees used

89 throughout each model. Other constants were as follows: (1) street furniture was used in areas where an active frontage would exist; (2) cars and buses were placed on the street to show some activity on the street; and (3) street lights.

Handrails were excluded based on exclusion from previous studies (Stamps,

1999b) and because in many cases they would not be overtly visible in the models. People on the streets were excluded as one of the evaluation characteristics is in regard to the amount of activity on the streets.

4.6.2. Base Model

The base model was the first step in developing visual stimuli. It was

developed as a fictional neighbourhood so as to not be specifically identified as

an existing neighbourhood within the case area. However, the structure of the

neighbourhood was developed using sections of existing Calgary

neighbourhoods so that the fictional neighbourhood would have some similar characteristics to the case area. These neighbourhoods included: Fairview, West

Hillhurst, Silver Springs, and MacKenzie Town. The neighbourhood street patterns were created using City of Calgary .shp files exported as .dwg files and manipulated using Autodesk AutoCAD 2014. Autodesk AutoCAD was used to ensure the accuracy of measurements.

The base model was designed as a 72.2 hectare (178.4 acres) urban area, approximately 7.3 hectares from north to south and 10.0 hectares from east to west. This area is just over the size of a quarter section which is measured at

65 hectares (160 acres). Figure 16 provides a land-use diagram of the fictional base area used in the research study.

Figure 16: Land-use Diagram of the Fictional Base Area Used for the Research Study

Street pattern

There are two major two-lane corridors that run through the model and intersect roughly in the centre of the area. The major corridors separate the model into four quadrants. Each quadrant has a representation of the typical street patterns used in modern neighbourhood developments: a gridiron pattern typical of turn of the century neighbourhoods; a broken grid pattern typical of

1950s to 1970s neighbourhoods; a curvilinear pattern typical of 1980s, 1990s and 2000s neighbourhoods; and a mixed pattern typical of contemporary and new urbanist neighbourhood design. Also a combination of rear lane streets and non-rear lane streets were used as both are common in the case area.

Block and Lot Configuration

As stated previously, block shape and configuration plays a significant role in determining the form of buildings and neighbourhoods (Jabareen, 2006). The use of four different street patterns in the base model has resulted in a range of block sizes and shapes. This resulted in a range of lot shapes and street relationships enabling a diversity of building form.

Sidewalks

Similar to most Calgary neighbourhoods monowalks were chosen as the only type of sidewalk that would be used in the base model. In order to keep this factor constant monowalks were drawn on both sides of all streets. The monowalks measured at 1.5 metres in all locations plus a .25 meter curb.

Land-use

The base model land use was researcher defined. The area measurement of the development parcels and their specified land use was completed using

AutoCAD 2014. In determining where the different land uses would be located and the areas of each land use type in the base model, the developable land area needed to be calculated first. The overall area was measured at 72.2ha

(178.3 acres). The municipal rights of way were then subtracted from the overall area. This included the roadways (17.1 ha) and rear laneways (3.0 ha). The property lines were then drawn with the appropriate distances based on measurements taken from City of Calgary geospatial information data. The area between the property lines and roads and the municipal road widening (4.6 ha)

92 were then measured and subtracted from the overall area. The resulting area, approximately 47.5 hectares, was the area designated as developable land.

Of the developable land, park locations were identified evenly across the model at a measured area of 2.2 hectares. An area of 2.1 hectares was designated as institutional for the purposes of an elementary school. The land in the immediate vicinity of the main intersection was designated for commercial uses (2.5 ha).

The remainder of the area was designated as residential land use, approximately

40.6 hectares. Figure 16 indicates land use structure of the base model, Table 9

provides a summary of the land use measurements.

Table 9: Land Use Area Measurements

Area Type Measure Hectares (ha) Acres Total area 72.2 178.3 Developable residential land 40.6 100.4 Green space 2.2 5.5 Institutional 2.1 5.2 Commercial 2.5 6.3 Roads 17.1 42.2 Laneways 3.0 7.4 Municipal road widening 4.6 11.4 * Note all measures are rounded to nearest tenth (1/10) (0.1)

4.6.3. Physical Variables

The physical variables in the visual stimuli were defined, in part, by the

variables identified in the development of the two typologies; the building type

typology and the density pattern typology. The six neighbourhood density

patterns were the basis of the six visual stimuli which were used as the visual

stimuli for the study. The neighbourhood density typology, including the

93 supporting research, was used to guide the layout of each of the six stimuli. This included the type and locations of buildings, the orientation of the buildings and the building street relationships. The building types were informed by the building typology and were used as a framework for documenting the differences between the visual stimuli.

Building form, as indicated in section 4.3.1, has a large number of variables that are dependent on the structure of the neighbourhood, the associated land uses and the density pattern.

4.6.4. Visual Stimuli: Visual stimuli Development Process

The visual stimuli were developed in four phases: defining land use,

building layout and measurement, modeling, and details and materials. This

section will describe each of these phases along with a description of each of the

visual stimuli.

Land use

Using the base model land use and the density pattern typology, an

overall land use strategy was developed for each of the visual stimuli. It was

acknowledged that, based on research, each of the patterns would have to

deviate from the base model based on the amount of mixed use that typically

exists in each pattern. A land use diagram was created for each of the six models

to guide the building layout and measurement. Figures 18, 21, 24, 27, 30 and 33

indicate the land-use determined for each of the corresponding models.

Building Layout and Measurement

The layout of the visual stimuli was completed using AutoCAD 2014. The base model, which included the road facing property lines, was used as a starting point. The developable areas were divided into lots of various sizes based on the street pattern of the given area. Lot width ranged from 7.5 metres wide to 30 metres wide. Initially, the neighbourhood was drawn exclusively with single family and semi-detached homes with a ratio of one to four and were located based on the street pattern and lot widths. Outlines of larger building types, with designated heights, were then drawn in locations at the discretion of the researcher in accordance with the density pattern typology. The initially drawn single family homes were then deleted and the lots combined prior to drawing the building. Using the area tool, the area of the buildings would be measured and then entered into a Microsoft Office Excel 2013 calculator tool developed by the researcher. The Excel calculator tool was programmed using the density calculation constants shown in Table 7 to calculate to the density measured in

people and jobs per hectare. This was used to ensure that once complete each

of the models would have approximately the same density within a tolerance of

+/-200 people across the entire model. Tables 8, 10, 12, 14, 16, and 18 indicate

the output of these calculations for each of the models.

Modeling

The three-dimensional modeling part of the model development was completed using the 3D Modeling function in AutoCAD 2014. First the roads, curbs, sidewalks and grass areas were detailed by using the extrusion function.

The building models were developed by extruding the footprints drawn in the layout and measurement phase. The extrusions used a 2.5 metre floor to floor height measurement, a typical height used in residential building development.

For commercial and ground floor areas, the floor to floor measurement ranged between 3 and 4 metres depending on the building type and design. Various massing functions were used to add additional detail to the buildings.

Details and Materials

The AutoCAD .dwg files were imported into Trimble Sketch-up Pro, which was used to add additional details and materials to the models. Two Master of

Architecture Students from the University of Calgary were hired to support the researcher in adding detail to the models. Table 10 provides a summary of the details which were added using Sketch-up Pro.

Table 10: Summary of the Materials and Details Added Using Sketch-up

Materials Details

 Glazing on windows  Trees  Aluminum on mullions  Shrubbery  Asphalt on roads  Street lights  Concrete on sidewalks  Street furniture  Asphalt on parking lots  Road signage  Grass on open areas  Pathways  Brick, metal, concrete, wood, etc.  Patios,  Cladding on buildings  Driveways  Wood on fences  Cars and buses

4.6.5. Visual Stimuli Descriptions

This section provides a description of each of the visual stimuli along with

the measurements associated with each. The stimuli development was framed

as an existing suburban neighbourhood mostly made up of single family

96 dwellings. Each model is a possible evolution of the neighbourhood into a higher density neighbourhood.

Visual Stimuli 1: Modern Density Pattern

Figure 17: Visual Stimuli 1 – Street level view Visual stimuli 1 was designed based on the modern density pattern. The land use for this stimuli is the same as the base model as shown in Figure 18. In this model higher density developments were clustered mostly away from the centre of the neighbourhood. Shown in Figure 19 these high-rise and mid-rise buildings are close to the major corridors. As typical with this density pattern, the mid-rise and high-rise building are located on oddly shaped lots, with surface parking, grass surrounding them and inward focused entries to the buildings. The buildings in this stimuli had large setbacks and very few had units or commercial space on the main level. See Figure 17 for an indication of what this neighbourhood would look like from the street view.

Figure 18: Visual Stimuli 1 - Land use Diagram

Table 11: Visual Stimuli 1 – Building Type Count

Building Type Number of Buildings Number of Units Single detached dwelling 929 929 Duplex / Semi-detached dwellings 220 220 Townhomes / Rowhouses 133 133 Low-rise apartment / Stacked townhomes 0 0 Mid-rise apartment / Condos 29 2416 High-rise apartment / Condos 21 3649

At the centre of the model is an existing commercial strip mall that consists of a grocery store, a pharmacy and shops typical of these commercial plazas. There is also a small section of street-based commercial buildings which had a combination of both retail and office space.

Overall, visual stimuli 1 had the most buildings classified as high-rise buildings (21) but also had the most single family detached dwellings (929)

(Table 11). Visual stimuli 1 was the only model to not include the low-rise apartment / stacked townhouse building type (Table 11).

Figure 19: Visual Stimuli 1 – Aerial View

Table 12: Visual Stimuli 1: Density Calculations Measure Result Number of jobs 492 Number of residents 15830 Total number of people/jobs 16222 Required number of households 6065 Percentage of jobs 3% Percentage of people 97%

Table 12 provides as summary of the density calculations for visual stimuli

1. This model and density pattern had the lowest ratio of jobs to residents with only 3% of the density measurements based on the number of jobs in the neighbourhood area.

Visual Stimuli 2: Corridor Density Pattern

Figure 20: Visual Stimuli 2 – Street View

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Figure 21: Visual Stimuli 2 - Land use Diagram

Visual stimuli 2 was designed based on the corridor density pattern. In this

model the higher density buildings are directly adjacent to the major corridors,

which can be seen in the aerial view in Figure 22. As defined in the density pattern

typology, the corridor density pattern commonly has retail and mixed use functions on the lower level of residential buildings. Figure 21 shows how the

base model land use was adjusted for the purposes of this model. The large

parcel that was designated as commercial in the previous model is designated as

mixed use as well. This parcel houses a mixed use development with retail,

including a grocery store, residential, and office space. The other mixed use

designated buildings for the most part include ground level retail with residential

above.

Visual stimuli 2 has a lower number of high-rise buildings (5) in

comparison to model 1. The majority of the higher density residential units are in

low-rise apartment buildings (27) and mid-rise apartment buildings (28) (Table

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13). Most of these buildings are street oriented with the main entries facing the

street and the majority have underground or concealed parking. Among the non-

mixed use buildings many were designed with ground oriented apartments with

entries off the street. These are intended to be converted to commercial functions

in the future but create visual interest and activity on the street level in the meantime.

Table 13: Visual Stimuli 2 – Building Type Count Building Type Number of Buildings Number of Units Single detached dwelling 711 711 Duplex / Semi-detached dwellings 174 174 Townhomes / Rowhouses 133 133 Low-rise apartment / Stacked townhomes 27 1565 Mid-rise apartment / Condos 28 2320 High-rise apartment / Condos 5 433

In this model there are few higher density buildings away from the main

corridors resulting in a separation between in the areas with single family, semi-

detached and townhouse dwellings, and higher density buildings. Figure 22 depicts the relationship between the higher density and lower density buildings.

Table 14 provides a summary of the density calculations for visual stimuli

2. This model has a greater proportion of jobs to residents/people with 14% of the calculation based on jobs.

Table 14: Visual Stimuli 2 – Density Calculations Measure Result Number of jobs 2254 Number of residents 13930 Total number of people/jobs 16184 Required number of households 5337 14% Percentage of jobs 86% Percentage of people

Figure 22: Visual Stimuli 2 – Aerial View

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Visual Stimuli 3: Nodal or Urban Village Density Pattern

Figure 24: Visual Stimuli 3 – Street View

Figure 23: Visual Stimuli 3 - Land - use Diagram

Visual stimuli 3 was designed based on the nodal or urban village density pattern. In this pattern the higher density buildings surround a focal point, in this case the main intersection of the neighbourhood. Similar to visual stimuli 2 and as defined to the density pattern typology, this density pattern tends to have a greater number of mixed use buildings. The diagram in Figure 23 is a representation of how the base model land use was adjusted for this model. Also,

104 similar to visual stimuli 2, the large parcel at the centre of the model houses a mixed use development with retail, including a grocery store, residential, and office space. The other mixed use designated buildings for the most part include ground level retail with residential above, some also include an office component.

Visual stimuli 3 has seven (7) buildings designated as high-rise buildings

(Table 15). While this density pattern tends to be around a focal point, the high- rise buildings are spread out along the two major corridors. Similar to visual stimuli 2, the majority of the mid-rise buildings are along the two major corridors.

The difference between the two models is the increased number of low-rise apartment buildings between the mid-rise corridors and the single detached, semi-detached and townhouse dwellings. This provides a greater transition between the low density areas and the higher density areas. The aerial view in

Figure 25 shows this transition. In this form there is a greater degree of variation in height and shape of buildings throughout the model.

Table 15: Visual Stimuli 3 – Building Type Count Building Type Number of Buildings Number of Units Single detached dwelling 789 789 Duplex / Semi-detached dwellings 176 176 Townhomes / Rowhouses 108 108 Low-rise apartment / Stacked townhomes 32 1814 Mid-rise apartment / Condos 23 1881 High-rise apartment / Condos 7 679

Overall visual stimuli 3 has the most evenly distributed number of the building types and the most evenly distributed number of units in each building type. See Table 15 for this distribution of these numbers.

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Table 16 provides a summary of the density calculations for visual stimuli

3. Similar to visual stimuli 2 this model has a greater ratio of jobs to residents with 13% and 87% respectively.

Table 16: Visual Stimuli 3 – Density Calculations Measure Result Number of jobs 2151 Number of residents 14061 Total number of people/jobs 16212 Required number of households 5387 Percentage of jobs 13% Percentage of people 87%

Figure 25: Visual stimuli 3 – Aerial View

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Visual Stimuli 4: Evenly Distributed or Neotraditional Density Pattern

Figure 27: Visual stimuli 4 – Street View

Figure 26: Visual Stimuli 4 - Land use Diagram

Visual stimuli 4 was designed based on the evenly distributed or neotraditional density pattern. In this model, the majority (80) of the higher density buildings fall into the low-rise / stacked townhouse category (buildings between three and four storeys) (Table 17). Of the eight (8) buildings that fall into the mid-rise category all are below six (6) storeys in height. As a result, this stimuli has the lowest overall diversity in building type and unit type. Due to the

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number of low-rise apartments/stacked townhouses required to achieve the

required density this stimuli has the lowest combined number of single detached,

semi-detached and townhouse dwellings (850) (Table 17).

Similar to both visual stimuli 2 and 3, the evenly distributed / neotraditional

density pattern tends to include mixed use building in the main areas of the

neighbourhood. Figure 26 indicates how the land uses were adjusted from the

base model for this stimuli. The majority of the mixed use buildings included retail on the main level with residential uses above. In most cases higher density residential buildings in this model utilize underground or hidden parking. The large parcel which was previously a commercial/retail plaza is a mixed use development with retail, office and residential uses including a grocery store.

Table 17: Visual Stimuli 4 – Building Type Counts Building Type Number of Buildings Number of Units Single detached dwelling 671 671 Duplex / Semi-detached dwellings 128 128 Townhomes / Rowhouses 51 51 Low-rise apartment / Stacked townhomes 80 4258 Mid-rise apartment / Condos 8 354 High-rise apartment / Condos 0 0

Figure 28 is aerial view of this stimuli. It indicates the evenly distributed

nature of the density of this model.

Table 18 provides a summary of the density calculations for visual stimuli

4. Similar to visual stimuli 2 and 3, a significant proportion of the density

calculations are made up of jobs, with 12% of the density ratio attributed to jobs

(Table 18).

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Table 18: Visual Stimuli 4 – Density Calculations Measure Result Number of jobs 1985 Number of residents 14346 Total number of people/jobs 16331 Required number of households 5497 Percentage of jobs 12% Percentage of people 88%

Figure 28: Visual Stimuli 4 – Aerial View

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Visual Stimuli 5: Evolutionary Density Pattern

Figure 29: Visual Stimuli 5 - Street View

Figure 30: Visual Stimuli 5 - Land use Diagram

Visual stimuli 5 was designed based on the evolutionary density pattern. It was designed using an unchanged base model land use, shown in the diagram in Figure 30 . In the street view image of visual stimuli 5, Figure 29, this model appears to be the most spacious of the models, however in the evolutionary density pattern, density is spread throughout the model. This density type is an example of where an area or neighbourhood is re-zoned for increased residential

112 density. This allows developers to purchase properties, amalgamate the separate parcels, and develop them to the maximum new zoning. As a result the higher density, high-rise and mid-rise buildings are located on parcels throughout the model. Also, often attributed to this density pattern are buildings with surface level parking with large building setbacks. Figure 31 indicates how this density pattern is manifested in an urban form.

Similar to visual stimuli 1, there is an existing commercial/retail plaza located at the centre of the model. It includes a large parking lot, a grocery store, a pharmacy, banks and other personal service, and retail functions. There are also some street oriented retail buildings with a single level of office space above.

In comparison to the other five visual stimuli, model 5 has the second largest number of buildings that fall into the mid-rise and high-rise building categories, second to visual stimuli 1 (Table 19). Due to the way that this density pattern exhibits itself, this model has the lowest number of single family dwellings of all the models (Table 19).

Table 19: Visual Stimuli 5 – Building Type Counts Building Type Number of Buildings Number of Units Single detached dwelling 599 599 Duplex / Semi-detached dwellings 168 168 Townhomes / Rowhouses 123 123 Low-rise apartment / Stacked townhomes 13 451 Mid-rise apartment / Condos 39 2403 High-rise apartment / Condos 14 2358

Figure 31: Visual Stimuli 5 - Aerial View

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Table 20 provides a summary of the density calculations used in visual

stimuli 5. The majority of the density in this model is attributed to residential units.

Table 20: Visual Stimuli 5 – Density Calculations Measure Result Number of jobs 492 Number of residents 15926 Total number of people/jobs 16418 Required number of households 6102 Percentage of jobs 3% Percentage of people 97%

Visual Stimuli 6: Transit Oriented Development Density Pattern

Figure 32: Visual Stimuli 6 - Street View

Visual stimuli 6 was designed based on the transit oriented development density pattern. In this pattern the higher density buildings are clustered around a central point which would have a major transit station at the centre. As stated in the density pattern typology, the transit oriented development pattern is typically characterized with mixed use functions at the core usually with some office space. Figure 33 indicates how the base model land use was adjusted for the development of this visual stimuli. Different from the other five models this model

115 has a significant number of high-rise apartment/condo (14) buildings and all are clustered around the centre of the model (Table 21).

Figure 33: Visual Stimuli 6 - Land use Diagram

Table 21: Visual Stimuli 6 – Building Type Counts Building Type Number of Buildings Number of Units Single detached dwelling 795 795 Duplex / Semi-detached dwellings 198 198 Townhomes / Rowhouses 167 167 Low-rise apartment / Stacked townhomes 10 679 Mid-rise apartment / Condos 25 2200 High-rise apartment / Condos 14 1459

Table 22 provides a summary of the density calculations used for visual stimuli 6. Similar to visual stimuli 2, 3, and 4, the density in this model is calculated based on a large jobs to people ratio with 12% of the overall calculation attributed to jobs (Table 22).

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Table 22: Visual Stimuli 6 – Density Calculations Measure Results Number of jobs 1962 Number of residents 14348 Total number of people/jobs 16310 Required number of households 5497 Percentage of jobs 12% Percentage of people 88%

Figure 34: Visual Stimuli 6 - Aerial View

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4.6.6. Animation

The videos of the visual stimuli were developed using Trimble Sketch-up

Pro modeling software. Each video consisted of 88 views resulting in each video being four minutes and twenty seconds long. The videos each followed the same path down the two main corridors of the models. The video commences with an axonometric view looking from the northeast corner of the model. The video proceeds by aligning with the shorter of the two main corridors traveling from north to south. At the end of the shorter street the frame turns around giving a series of axonometric views from the southwest before aligning to the west side of the longer of the two main corridors. The video proceeds to travel from west to east along the longer of the two main corridors. The video frames turn at the end of the last corridor and finish with a few additional southeast axonometric views.

The diagram in Figure 35 indicates the path used in all six videos. The video frames were placed between one metre and 1.5 metres above the ground to give a view similar to walking or driving through the model. The view itself is framed in the middle of the street presenting more as a view from a car than that of a pedestrian.

The videos were exported as .mp4 files and were formatted with a frame height of 1080 pixels and a frame width of 1920 pixels. The videos were played at 24 frames/second using VLC media player.

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Figure 35: Walkthrough Path Used in Videos

4.7. Perceptive Instrument Design: Neighbourhood Visualizations

Questionnaire

The second part of the study involved a paper questionnaire which was

filled out by participants who attended the on-campus session at the University of

Calgary Taylor Family Digital Library Visualization Lab. The questionnaire was

designed to be filled out by participants after watching each of the visualizations.

4.7.1. Part 2: Perception of Density and Neighbourhood Perceptions

Neighbourhood Visualizations

The first section of the questionnaire included a series of eight questions which participants were asked to answer six times – once for each of the six visual stimuli. The first question was designed to gauge participants’ initial reaction to the stimuli. Participants were given a series of 16 descriptive words

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(see Appendix G) that were a combination of positive, negative and neutral as

well as blank options and were asked to pick three words to describe their

thoughts about the stimuli. The second question was used to test perception of

built form. It used a 7 point Likert scale ranging from “very spacious” to “very

dense” with a neutral option in the middle. The third question tested for

acceptability of the stimuli. The fourth question asked participants to estimate the

population density of the visual stimuli using a multiple choice question with

options ranging from 100 people and jobs per hectare and 900 people and jobs

per hectare. The fifth question asked participants to indicate to what extent they

felt the visual stimuli shown would support their lifestyle. This question used a 7-

point Likert scale ranging from “Would very much so not support my lifestyle” to

“Would very much so support my lifestyle” with a neutral point in the middle. In

the sixth question participants were given an aerial image of the visual stimuli

and were asked to circle where they would choose to live in this neighbourhood.

A final qualitative question was created to give the opportunity to capture what

participants liked and did not like about the visual stimuli. In this question the

participants were asked “Please describe what you like and or do not like about

the neighbourhood shown”. The final question was strategically placed at the end

to capture any further thoughts that participants had.

Ranking

The final section of part two included a single question asking participants

to rank each visual stimuli in order of preference. Participants were given a list of the six neighbourhoods that were shown and asked to write the number 1

121 through 6 next to the stimuli; where 1 was most preferred and 6 was least preferred.

4.8. Participant Sampling

The sampling frame for the research study was residents of Calgary,

Alberta, Canada. Participation in the research study was completely voluntary and was based on responses to the recruitment advertisement (Appendix E). In order to obtain a higher response rate, participants were offered a $10 gift card to either Starbucks or Petro-Canada when they completed both parts of the study.

4.8.1. Recruitment

In order to obtain a sample that was far reaching and diverse, the flyer was posted using multiple mediums in locations that would attract the widest range of population including physical flyers posting, social media, print media and snowball distribution. Physical flyer were posted in locations throughout the city including the University of Calgary Campus, grocery store community posing boards, Starbucks community posting boards, and outdoor spaces in community areas throughout the city. Social media networks including Facebook and Twitter were used by posting images of the flyer on the networks; a snowball distribution approach was used with key reposting from @makecalgarytalk and @yyctweetup in order to reach the widest audience. The advertisement was also used in print media including the Calgary Real Estate Board monthly newspaper and the

Federation of Calgary’s Communities monthly newsletter. A snowballing distribution approach was also used by sending the flyer to participants from the

122 key informant phases of the research with a request to send the information to potential participants.

When sampling it was assumed that the location people chose to live would relate to lifestyle factors. Specific decisions were made to make sure that advertisements would reach residents of as many areas of the city as possible.

The locations that physical advertisements were posted included downtown, inner city, established suburban and new suburban locations.

4.8.2. Participant Profiles

A total of 63 research participants from part 1 of the research study were included in the analysis of the participant profiles. A number of descriptive analyses were reported to give a general overview of the participants included in the study. This section provides an overview of this descriptive analysis.

Neighbourhood Location of Participants

Among the participants who reported on the neighbourhood in which they currently live, the sample represented a total of 46 different neighbourhoods. The neighbourhoods in which participants indicated they lived is shown in Figure 36

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Figure 36: Neighbourhoods Where Participants Reside

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Demographic Characteristics of Participants

Among the participants, twenty-one (21) identified as male and forty-one

(41) identified as female; one (1) participant did not specify a gender (Table 23).

Sixty-five percent of the participants were between the ages of 25 and 44 and

22% of the participants were 45 years and above (Table 23). Seventy-nine percent of the participants identified as having, at minimum, a bachelor’s degree and 38% with a master degree or above (Table 23). The most significant group of participants identified themselves as White/Caucasian or Euro-American at 71%, the second largest group identified themselves at East Asian at 11% (Table 24).

Table 23: Demographic Characteristics of the Participants Total Demographic Characteristics n % Number of Participants 63 (100)

Gender Male 21 (33) Female 41 (65) Undetermined 1 (2) Total 63 (100)

Age 18 to 24 years 8 (13) 25 to 34 years 26 (41) 35 to 44 years 15 (24) 45 to 54 years 9 (14) 55 to 64 years 5 (8) 65 years and above 0 (0) Total 63 (100)

Educational Attainment High School Diploma 3 (5) Technical School 2 (3) Some College Credit no diploma 1 (2) College Diploma 5 (8) Bachelor Degree 26 (41) Master Degree 20 (32) Professional Degree 2 (3) Doctoral Degree 2 (3) Undetermined 2 (3) Total 63 (100) Note: The total percentages may not be 100 due to rounding

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Table 24: Ethnic Characteristics of the Participants Total Ethnic Characteristics n %

Ethnicity Black, Afro-Caribbean or African American 1 (2) Latino or Hispanic 2 (3) East Asian 7 (11) White/Caucasian or Euro-American 45 (71) South Asian or Indian 4 (6) Middle Eastern or Arab 1 (2) Russian Jewish 1 (2) Central Asian 1 (2) Undetermined 1 (2) Total 63 (100) Note: The total percentages may not be 100 due to rounding

Participant Household Characteristics

Among the participants, 14% identified as living alone, 26% identified as living with their spouse with or without a dog, and 24% identified as living with a spouse and children with or without a dog. A large contingent, 17%, of participants identified as living with roommates or boarders (Table 25). The average household income among participants was between $74,000 and

$99,999, while the median household income was between $100,000 and

$149,999. The largest group of participants fell into the $150,000 to $249,999 income bracket at 24%.

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Table 25: Household Characteristics of the Participants Total Household Characteristics n % Mean of household members 3.00 (100)

Household Makeup Participant Only 9 (14) Participant + Spouse 13 (21) Participant + Spouse + Dog 3 (5) Participant + Spouse + Child(ren) 10 (16) Children 5 and Under 5 Children 6 to 12 6 Children 13 to 18 1 Children over 18 2 Participant + Spouse + Child(ren) + Dog 5 (8) Children 5 and Under 1 Children 6 to 12 4 Children 13 to 18 1 Children over 18 1 Participant + Spouse + Child(ren) + Parent 1 (2) Children 5 and Under 1 Children 6 to 12 1 Participant + Child(ren) 5 (8) Children 5 and Under 1 Children 6 to 12 1 Children 13 to 18 3 Children over 18 1 Participant + Child(ren) + Parent 2 (3) Children 6 to 12 2 Children over 18 1 Participant + Child(ren) + Parent + Other Relatives 1 (2) Children 6 to 12 1 Children 13 to 18 1 Participant + Parent(s) 1 (2) Participant + Parent(s) + Other Relative 1 (2) Participant + Child(ren) + Roommates/Boarders 1 (2) Children 6 to 12 1 Children 13 to 18 1 5 Roommates 1 Participant + Roommates 11 (17) 1 Roommate 2 2 Roommates 4 3 Roommates 4 4 Roommates 1

Total 63 (100) Note: The total percentages may not be 100 due to rounding

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Table 26: Household Income of the Participants

Household Characteristics Mean household $75,000 to $99,999 Median household income $100,000 to $149,999

Total Household Income n % Less than $35,000 9 (14) $35,000 to $49,999 5 (8) $50,000 to $74,999 8 (13) $75,000 to $99,999 10 (16) $100,000 to $149,999 11 (18) $150,000 to $249,999 15 (24) $250,000 or more 5 (8) Total 63 (100) Note: The total percentages may not be 100 due to rounding

4.9. Data Collection Procedure

Data collection was completed in two parts. The first part used an online

questionnaire completed using Fluid Surveys. The second part was a session

that was held on the University of Calgary campus in the Taylor Family Digital

Library Visualization Lab. The sessions used a large high definition display to

show the visualization stimuli and paper questionnaire. The same participants

were included in both parts of the study.

4.9.1. Procedure

All forms of the research advertisement requested that interested parties

contact the researcher directly via personal e-mail. The purpose of the study and

other necessary information was sent to interested parties (e.g. informed

consent, research process) via personal e-mail along with the option to not

participate in a return e-mail. When interest was confirmed participants were sent

a link with the online questionnaire at Fluid Surveys. At no point were participants

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asked for any personal information; each was given a three digit numeric

identifier to be used during the study.

When starting the online portion of the study (referred to as part 1 of the

study) participants were presented with a consent form which when agreed to

allowed them to continue on to the questionnaire. The part 1 questionnaire

included a series of questions pertaining to four areas: demographic and

background information; lifestyle activity, interests and opinions; current and past

housing choices; and housing and neighbourhood preferences (as outlined in

section 4.5).

Once they completed part 1 of the research study, participants were asked

to attend a prearranged session at the University of Calgary Taylor Family Digital

Library Visualization Laboratory (Figure 37). This session is called part 2 of the research study. During the session participants were presented the series of six videos discussed in section 4.6. above. Prior to starting the videos participants were given pre-emptive information as a frame of reference in order to answer the questions. The information given was as follows:

 Each stimuli included a grocery store

 Each stimuli was a total of 72 hectares

 A Calgary downtown city block is approximately 1 hectare to a hectare

and a half

 The population assumption in each stimuli was that each household was

the Calgary average household which is 2.61 people per household.

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 The densest neighbourhood in Calgary is Beltline however it has an

average household size of 1.60 people per household (Elections and

Information Services, 2014).

Figure 37: Image of Participants in the Taylor Family Digital Library Visualization Studio

Each of the six videos were then played; after each video participants were given time to complete the part 2 questionnaire. Figure 38 provides a summary of the overall procedure. While participants were completing the questionnaire an axonometric view of the visual stimuli was left on the screen as shown in Figure 37. Once all six videos were completed, participants were asked to rank each of the six stimuli in order of preference. During this process participants were given a second chance to look at an image of each of the stimuli. Once all questionnaires were completed and participants had returned

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them to the researcher a debriefing session was completed. Refer to Appendix H

for the debriefing script.

Figure 38: Overall Data Collection Procedure Diagram

4.10. Data Analysis

The analysis of the data collected took place in six stages: (1) compiling of

the typologies; (2) the coding and analysis of the key informant interviews; (3) the

finalization of the typologies; (4) the design, development and measurement of

the visual stimuli (5) the statistical analysis of the questionnaire results; and (6)

the qualitative analysis of the questionnaire results and key informant interview

results. The discussions in Chapter 5, 6, and 7 of this document will provide more

detail on the analysis of stages one, two, and three; this section provides detail

around the analysis of stages four, five and six.

The data analysis was completed in accordance with the research

questions to be tested. The results included: (1) overview of the respondents; (2)

lifestyle dimension of the participants; (3) lifestyle cluster profiles; (4) the

relationship between stated housing and neighbourhood preferences and lifestyle

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clusters; (5) urban form preferences; (6) perceived density; (7) the relationship

between preferred form and perceived density; (8) the relationship between

lifestyle clusters and preferred urban form; (9) the relationship between perceived

characteristics and preferred urban form; (10) the relationship between preferred urban form and qualitative commentary; and (11) the relationship between preferred urban form and what industry stated was the most preferred housing product. Figure 39 provides a summary of the flow of data analysis and how each analysis informs the answering of the research questions. In order to achieve the results indicated, a number of different data and statistical analysis techniques were used. The following provides a brief overview of each of those techniques.

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Figure 39: Data Analysis Flow Diagram

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Descriptive Statistics

Also called univariate statistics, descriptive statistics is a method of computing data in order to tell a story about that data (Diekhoff, 1992). Mean, median, mode, range, standard deviation and percentages are the more commonly used descriptive statistics. These approaches are commonly organized in a combination of charts and graphs in order to best communicate the results (Diekhoff, 1992). Descriptive statistics were used: (1) to profile the participants of the study; (2) to determine perceived density, preferred urban form, and perceived characteristics of each visual stimuli; and (3) to determine which visual stimuli was preferred by each lifestyle cluster.

Factor Analysis

Factor analysis is a multivariate statistical technique used to understand the underlying structure of the data (Hair Jr., Anderson, Tatham, & Black, 1998).

Different from other statistical analysis techniques, factor analysis does not look for the dependencies between variables but rather looks for the interdependencies between variables (Hair Jr. et al., 1998). It simultaneously looks at each variable in relation to all other variables in order to form factors which are used to describe the entire set of variables (Hair Jr. et al., 1998).

Factor analysis was used in this study to determine the underlying factors that describe the lifestyles of the research participants

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Cluster Analysis

Cluster analysis is a multivariate statistical technique used to group cases based on a specific set of characteristics (Hair Jr. et al., 1998). Also called classification analysis, it allows researchers to describe groups based on characteristics that have been predefined (Hair Jr. et al., 1998). It is a common approach when collecting data through questionnaires and when the researcher needs to group participants based on characteristics (Hair Jr. et al., 1998).

Cluster analysis was used in this study to cluster the research participants based on their lifestyle factors.

One-way Analysis of Variance (ANOVA)

One-way Analysis of Variance, commonly called one-way ANOVA, is used to analyze the differences between two or more group averages or means

(Diekhoff, 1992). This essentially is evaluating to what degree two groups differ on one specified independent variable (Diekhoff, 1992). One-way analysis of variance was used in this study to validate the output of the cluster analysis.

Repeated Measures Analysis of Variance (ANOVA)

The Repeated Measure Analysis of Variance (ANOVA), is used to analyze to what degree results within a group of repeating data differs between them

(Diekhoff, 1992). Repeated measures analysis of variance was used to determine the statistical significance of preferred urban forms and of perceived population density.

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Factorial Repeated Measures Analysis of Variance (ANOVA)

Factorial Repeated Measure Analysis of Variance, also called a split-plot

ANOVA or mixed design ANOVA, combines the two ANOVA functions, the within groups analysis of variance and the between groups analysis of variance

(Diekhoff, 1992). Factorial repeated measures analysis of variance was used to determine the statistical significance of levels of preference across the lifestyle clusters.

Correlational Statistics

Correlation statistics is a measure of the strength of a relationship that exists between variables (Diekhoff, 1992). Correlational procedures are bivariate, evaluating the relationship between two variables. Correlational statistics were used in this study to determine the relationship between preferred urban form and perceived density.

Semantic Differential

Semantic differential is a method used to establish a profile of a group based on a number of different variables (Rosenthal & Rosnow, 1984). It is commonly used to provide a summarized understating of a defined group’s perspective on a specified set of issues (Rosenthal & Rosnow, 1984). Sematic differential was used for two purposes, the first to describe the preferences of each of the lifestyle clusters, and the second to complete the comparison of the

percieved characteristics of each of the visual stimuli.

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The statistical analyses were completed using IBM Satistics Package for

Social Sciences (SPSS) 21.0.

4.11. Summary

This chapter provided a detailed description of the methods used in the

study to understand what urban forms, all presented at constant densities, are

preferred by residents and to understand the characteristics of both the residents

and the preferred forms in order to inform strategies for increasing density. As noted in the literature, there was not an existing tool to evaluate how preferences for urban form affect perception of spatial and population density and how this relates to stated housing preferences, lifestyles and previous living experience.

For this reason, an instrument was designed based on the review of previous

studies related to perceptions of visualizations and architectural representation,

housing preference, and lifestyles. The chapter discussed how the data was

analyzed using statistical analysis techniques and how the final synthesis was

completed.

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5. Results: Profiling Participants and Exploring Lifestyles

A total of 63 cases from part 1 of the research study were included in the

analysis of the participant profiles. Initially a number of descriptive analyses were

reported to give a general overview of the participants included in the study.

Further descriptive analyses were undertaken to identify the current housing

situation and housing and neighbourhood preferences of the participants. A series of factor analyses were used to analyze the activity, interest and opinion

(AIO) statements to explore the lifestyle elements of the research participants.

This was followed by a series of hierarchical and non-hierarchical cluster analyses in order to group participants based on the lifestyle element. Lifestyle clusters were profiled using one-way analysis of variance (ANOVA), descriptive statistics, and semantic differential charting of housing and neighbourhood preferences.

5.1. Overview of Participants’ Housing Characteristics

Participants were asked a series of questions to understand their

perspectives of their homes and what is important to them in regards to housing.

This section provides a general overview of these results.

5.1.1. General Housing Characteristics of Participants

Among the participants 51% identified as currently living in a single

detached dwelling and 21% identified as living in a semi-detached dwelling or

duplex (Table 27). There was a small group of participants who lived in

138 apartment style dwellings; 13% lived in a rental apartment and 6% lived in an apartment style condo (Table 27).

Table 27: Type of Current Home Total Current Home Type n % Rental apartment 8 (13) Single detached dwelling 32 (51) Student dormitory 1 (2) Semi-detached dwelling (duplex) 13 (21) Condominium (apartment style) 4 (6) Townhouse/Row house 4 (6) Basement apartment 1 (2) Total 63 (100) Note: The total percentages may not be 100 due to rounding

On average, participants identified as having lived in their current home for between 3-4 years, while the largest group, 22%, identified as having lived in their current home for less than 1 year (Table 28).

Table 28: Length of Current Residency Mean Length of Residency across participants 3-4 Years Total Length of Residency n % Less than 1 year 14 (22) 1-2 years 11 (18) 3-4 years 12 (19) 5-10 years 11 (18) 10-20 years 12 (19) More than 20 years 2 (3) Undetermined 1 (2) Total 63 (100) Note: The total percentages may not be 100 due to rounding

Among the participants that had identified as having moved in the last 5 years, 19% identified as having moved from a rental apartment and 27% identified as having moved from a semi-detached or duplex dwelling (Table 29).

One percent of participants identified as having moved from a single family home

139 in the last 5 years and only 1% of participants identified as having moved from a townhome or row home (Table 29).

Table 29: Previous Living Accommodation if Changed in the Last 5 Years Total Previous Housing Situation n % Rental apartment 12 (19) Single detached dwelling 1 (2) Student dormitory 4 (6) Semi-detached dwelling (duplex) 17 (27) Condominium (apartment style) 5 (8) Townhouse/Row house 1 (2) I have lived my current residence for the last 5 years 25 (37) Total 63 (100) Note: The total percentages may not be 100 due to rounding

When asked about future housing plans more than 50% of participants identified as planning to stay in their current home for the foreseeable future. Of those who identified as planning to relocate in the future the largest group, 18%, stated the intent to downsize to a smaller apartment condo or town home (Table

30). Eleven percent of participants identified as planning to upsize to a larger single family home and 8% of participants identified as planning to move to a smaller single family home (Table 30).

Table 30: Future Housing Plans Total Future Housing Plans n % I plan to stay when I am for the foreseeable future 33 (52) I plan to upsize to a larger apartment/condo (townhome) 7 (11) I plan to upsize to a larger single detached dwelling 7 (11) I plan to downsize to a smaller apartment/condo (townhome) 11 (18) I plan to downsize to a smaller single detached dwelling 5 (8) Total 63 (100) Note: The total percentages may not be 100 due to rounding

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5.1.2. Ownership Importance

Participants were asked about how important property ownership was to

them. Forty-eight percent of participants stated that home ownership was ‘very

important’ whereas 30% of participants stated that land ownership was ‘very important’ (Table 31 and Table 32). Overall more than two thirds, 69%, of

participants identified home ownership as ‘important’ or ‘very important’ (Table

31). In comparison, less than half, 46%, of participants identified land ownership

as ‘important’ and ‘very important’ (Table 32). A significant proportion of

participants, 19%, consider land ownership ‘not at all important’ (Table 32).

Table 31: Importance of Home Ownership Total Importance of Home Ownership n % Very Important 30 (48) Important 13 (21) Somewhat Important 13 (21) Not at all Important 7 (11) Total 63 (100) Note: The total percentages may not be 100 due to rounding

Table 32: Importance of Land Ownership Total Importance of Land Ownership n % Very Important 19 (30) Important 10 (16) Somewhat Important 22 (35) Not at all Important 12 (19) Total 63 (100) Note: The total percentages may not be 100 due to rounding

5.1.3. Lifestyle Activities Interests and Opinions (AIO) of Participants

The lifestyle activities, interests and opinions (AIO) items were evaluated using a 5 level agree – disagree Likert Scale ranging from 1 “strongly disagree” to “strongly agree.” Among the 35 activity and interest items seven had the

141 largest grouping of people identify as “strongly disagree.” Sixty percent of participant strongly disagreed with “I have a second dwelling where I spend much of my free time” and 57% of participants strongly disagreed with “I spend a lot of time in my garage” and “I regularly take a dog out for walks” (Table I1 in

Appendix I). “I usually walk to work/school”, “I spend a lot of time driving family members around”, “I often have overnight guests” and “I often check out local sporting events or trade shows” had 41%, 40%, 30% and 30% of participants

“strongly disagree” respectively (Table I1 in Appendix I). When both “strongly disagree” and “disagree” are combined, a number of statements become more significant. Sixty-four percent of participants disagreed with “I spend my free time doing physical activities in the mountains” and 63% of participants disagreed with

“I am regularly at the local yoga studio or fitness center.” Also “I like the latest trends but I always go online to get them” and “I am often checking out the latest bar or restaurant” had 60% and 58% of participants disagree with those statements (Table I1 in Appendix I).

Among the positive responses to the activity and interest items the largest percentage of participants, 25%, ‘strongly agreed’ with “I frequently spend time surfing the internet at home.” Participants strongly agreed with “I usually drive to work/school”, “I cook my evening meal at home almost every day” and “I usually walk to work/school” 23%, 18% and 15% respectively (Table I1 in Appendix I).

When both strongly agree and agree are combined as a single level of agreement some statements stand out as more positive. Sixty-one percent of participants agreed with “I cook my evening meal at home almost every day” and

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56% agreed with “I spend very long hours at an office/ workplace outside my

home” (Table I1 in Appendix I). Fifty-five percent of participants agreed “I often

spend time at home reading (newspapers, books, RSS Feeds, etc.) and 53% of participants agreed with each of these three statements “I frequently spend time

watching TV and movies at home”, “I often work/study from home” and “Besides

sleep, I spend a lot of time indoors in my home” (Table I1 in Appendix I). See

Figure 66 in Appendix I for additional details.

Among the 38 opinion items only three had a significant portion of

participants’ state that they strongly disagreed with the statements. Thirty-seven

percent of participants strongly disagreed with “My home is only a place to sleep

and get dressed” and 18% of participants strongly disagreed with “I get bored

when I stay at home” (Table I2 in Appendix I). When the strongly disagree and

disagree scales are combined into a single disagree rating four additional

statements show as more significant. Eighty-nine percent of participants

disagreed with “My home is only a place to sleep and get dressed”, 62% of

participants disagreed with “I get bored when I stay at home” and 48% of

participants disagreed with both “I think owning a home leaves too little money

for other things” and “I want a home that can be a project for me to work on”

(Table I2 in Appendix I).

Generally, the opinion items had more participants agree with the

statements. Sixty-six percent of participants strongly agreed with “I want a home

where I feel secure” and 93% overall agreed with the statement. Sixty-five

percent of participants strongly agreed with “I want a home that is easy to keep

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clean” and 92% agree overall. Fifty-seven percent of participants strongly agreed

with “I want a home where I can rest and relax” and 96% overall agreed (Table I2

in Appendix I). Fifty-nine percent of participants strongly agreed with the

statement “I want a home where my family can spend time together” and 89%

agreed overall. Fifty-four percent of participants strongly agreed with “I want a

home in a convenient location” and 95% agreed with the statement overall (Table

I2 in Appendix I). When the strongly agree and agree scales are combined, a

number of statements have a large proportion of participants agreeing with the

statements. Ninety-three percent of participants agreed with the statement

“Keeping a house clean is important to for the health of the occupant”, 90% of

participants agreed with the statement “I want a home where the needs of all

household members are balanced” and 92% of participants agree with “I want to

live in a home which is pleasant for me to look at” (Table I2 in Appendix I).

5.1.4. Housing and Neighbourhood Location Preferences of Participants

During part 1 of the study participants were asked a series of questions to

determine their housing and neighbourhood preferences.

Among the participants of part 1, the largest proportion, 54%, identified an

inner city location the ideal geographic location for the neighbourhood they would chose to live in. Twenty-two percent of participants selected an established suburb, and 13% selected a downtown neighbourhood (Table 33).

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Table 33: Ideal Geographic Neighbourhood Location Total Ideal Home location n % Downtown 8 (13) Inner City 34 (54) Established Suburbs 14 (22) New Suburban Developments 6 (10) Rural Area 1 (2) Other 0 (0)

Total 63 (100) Note: The total percentages may not be 100 due to rounding

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Participants were given a list of fifteen factors that may be considered when selecting a home and neighbourhood, a fillable selection was also given.

Participants were asked to select from these factors the 5 most important factors and 5 least important factors they consider when choosing a home or neighbourhood. A summary of the results can be found in Tables I3 and I4 in

Appendix I.

Most Important Location Factors in Choosing the Ideal Home (%)

Place where I can have A yard, not necessarily… In an area that is attractive - either new or… Clean environment (air, water, etc). In an area where the housing styles match my… In an area where I can get a large yard In an area where the people are just like me In a location where I can walk to everything I… Close to parks and pathways Close to grocery stores and daily shopping needs Close to the physical activities I participate in… Close to shopping, entertainment, and other… Close to major roads or highways In an area where most of the housing options… Close to night life Close to cafes and restaurants Close to public transportation In a good school district In a location to minimize commute time to work…

(0) (10) (20) (30) (40) (50) (60) (70) (80) (90)

Most Important Choice 1 Most Important Choice 2 Most Important Choice 3 Most Important Choice 4 Most Important Choice 5

Figure 40: Most Important Location Factors in Choosing the Ideal Home (%)

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In choosing the ideal home, the location factor that is most important to

the most participants was “In a location to minimize commute time to

work/school” with 52% of participants indicating it is the most important factor

and 78% of participants placing it in their top 5 factors. The second largest group

indicated “In a location where I can walk to everything I need” was the top

location factor with 19% of participants and 51% of participants placing it in the

top five (Table I3) . Both “Close to public transportation” and “Close to parks and

pathways” received a significant number of participants ranking them as the

second most important factor with 23% and 15% respectively. Each also had a

high proportion of participants rank these factors in the top 5 with 56% and 61%

respectively. Two other factors also had a high proportion of participants indicate them in the top 5 most important factors “Close to grocery stores and daily shopping needs” and “Close to shopping, entertainment, and other personal services” had overall scores of 52% and 46% respectively (Table I3). See Figure

40 for a summary of all the location factors indicated as most important.

Figure 41: Least Important Factors in Choosing the Ideal Home (%)

The factor that the most participants indicated as the least important factor

in selecting a home location was “Close to night life” with 23% of participants

identifying it as the first least important factor and 42% of participants ranking it in

the bottom five. The second factor that many participants considered least

important was “In an area where I can get a large yard.” With 20% of participants

ranking it least important and 46% of participants ranking it in the bottom five

(Table I4). Other factors that had a significant portion of participant rank in the

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top five least important location factors are: “In an area where most of the

housing options are similar” with 68% of participants, “In an area where the

people are just like me” with 65% of participants and “In an area where most of

the housing options are similar” with 67% of participants (Table I4). See Figure

41 for a summary of all the location factors indicated as most important.

5.1.5. Housing and Neighbourhood Item Preferences of Participants

During part 1 of the research study participants were give a series of 36 five

point semantic differential scale questions in order to determine the housing and neighbourhood preferences of participants. Semantic scales typically are either

five or seven point scales with opposite descriptors or adjectives on either end

(Rosenthal & Rosnow, 1984). The semantic differential scale was developed as a

graphic based scale to measure subjective meaning from given stimuli (Osgood et al., 1957). It has often been used in marketing research and the measurement of opinions on particular social constructs (Brinton, 1961; Rosenthal & Rosnow,

1984). In this case, participants were given a series of items that people generally have an opinion on when choosing a neighbourhood. On either end of the semantic differential scale an opposite opinion of that particular item was given.

On some of the items it was clear that many of the participants tended to have very similar preferences. In particular “Access to Bike paths” 67% of participants wanted well connected bike paths and 93% were leaning towards well connected bike paths; “Being able to commute to school or work by transit”

61% of participants said that was important in their neighbourhood and 90% were

148 leaning towards important; and 59% of participants said that “Sense of Safety” was important and 90% overall leaning to toward important (Table I5). Two characteristics that participants generally agreed on which stood out as particularly high were: visually stimulating neighbourhoods (85% of participants with 54% strongly agreeing), and low traffic (84% with 42% strongly agreeing)

(Table I5 and Figure 42). Other items that participants generally agreed on included: more of a age range (79% with 53% strongly agreeing), grocery stores within walking distance (74% with 44% strongly agreeing), lower noise levels

(73% with 31% strongly agreeing), and street parking for visitors (73% with 30% strongly agreeing) (Table I5 and Figure 42). While there are a number of factors on which many participants agree there were also factors which were much more polarizing. In particular “distance to entertainment and cultural activities” and

“distance to fitness centres and yoga studios” were had some of the greatest differences among participants. Forty-one percent of participants wanted entertainment and cultural activities within walking distance and 49% wanted it within driving or transit distance; while the participants who strongly agreed were

16% and 21% respectively (Table I5 and Figure 42). Thirty-seven percent of participants wanted fitness centres and yoga studios within walking distance while 42% preferred driving or transit distance; those who strongly agreed were

13% and 15% respectively (Table I5 and Figure 42). Other items that were polarizing among participants included: parking for private residents, public

149 spaces and sidewalks, the size of homes, lot sizes, and the importance of being able to commute by car (Table I5 and Figure 42).

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Participants' Housing and Neighbourhood Preferences

Important AccessBeing major able corridors to access and highways major corridors andNot highways… important Important CommuteBeing toable work/school commute by totransit work/school by transitNot important from… Important CommuteBeing to work/schoolable commute by bike to work/school byNot bike important from… Commute to work/school by car Important Being able commute to work/school by carNot from important my… Important CommuteBeing to work/sc ablehool commute by walking to work/school byNot walking… important Inside access Access toTo car what degree are you willing toOutside go outside to access to… Provided VisitorThe parking visitor parking for my neighbourhoodStreet should Parking be Provided Parking for private Theresidents parking for private residentsStreet ofParking my… Range of life stages StageThe in life sta ge in life (e.g. single, family, retired,Similar etc.)life stage of… High diversity of Diversity The diversity (age, income, ethnicity, etc.)ofLow diversity the… residents Wide range of age Range of ageThe groups range of age groups in my neighbourhood…Similar ages groups Sense of safety Important The sense of safety that my neighbourhood…Not important High Traffic AmountThe of amount vehicle traffic of vehicle traffic in my neighbourhood…Low Traffic High degree of VisualThe privacy visual privacy in my neighbourhoodLow visualand home… privacy visual privacy Daily Willingness toTo hear what your degree neighbours are you willing to hearNot your… at all High noise Noise level in my neighbourhoodNoise level in my neighbourhoodLow noise Designed and full of Streets and public spaces Streets and publicSimple spaces and art uncluttered Active with lots of Public spaces and sidewalks Public spacesQuiet and and sidewalks uncrowded people Active with lots of Quiet and SecludedParks Parks people The accessThe to accessbike paths to bike paths in myAway neighbourhood from residences Highly accessible and connected The lot size of my home The lot size of my homeSmall Large Size of my outdoor space or yard Large (sports Size of my outdoor spaceSmall or (balcony yard activities) /terrace) Large (2500sqft) Size of my home Size ofSmall my (600sqft) home Mostly multi-family AmountAmount of multi-family of multi‐family homes homes in my neighbourhoodLess multi-family homes homes Amount of single family homes in my neighbourhood Mostly single family Amount of single-family homes Less single family homes homes More of a mix of Mix of housingMix types of housing types in my neighbourhood Less of a mix housing types Uniform in The visual Theappearance visual appearance of the neighbourhoodVisual stimulating appearance DistanceDistance to municipal to municipal community community centres centre andWalking team… Driving/Transit Distance to daycares Driving/Transit Distance to daycares Walking Distance to schools Driving/Transit Distance to schools Walking Distance to personDistance care and physiciansto person care and physicians Driving/Transit Walking Distance to boutiques,Distance salons toand boutiques, spas salons and spas Driving/Transit Walking Distance to entertainment and cultural activities Driving/Transit Distance to entertainment and Walking Distance to fitness and yoga studios Driving/Transit Distance to fitness and yoga studios Walking Distance to cafes, restaurants and bars Driving/Transit Distance to cafes, restaurants and bars Walking Distance to grocery stores and shopping Distance to grocery stores and shopping Walking Driving/Transit 0% 20% 40% 60% 80% 100% ‐2 ‐1 0 1 2 151

5.2. Analysis of Lifestyle Dimension

The lifestyle component of questionnaire part 1 included 73 activity interest and

opinion (AIO) statements within two questions. The first question consisted of 35

activity and interest related variables and the second included 38 opinion related

variables. Among the variables none were missing more than three values so all

variables were included at the beginning of the analysis.

In order to determine the lifestyles of the participants the data was

analyzed in two steps. The AIO variables were analysed for latent factors and

then grouped using a factor analysis. The participants were then clustered based

on the lifestyle factors identified in the factor analysis using a series of cluster

analyses.

5.2.1. Lifestyle Factors: Factor Analysis

Factor analysis is a statistical technique often referred to as a data reduction technique. However, its function is to analyze and identify the

underlying structure and interrelationships among multiple variables and define a

smaller subset of highly interrelated or correlated variables which are called

factors (Hair Jr., Black, Babin, Anderson, & Tatham, 2006). Factor analysis is

used to define a dimension that cannot adequately be described by a single

measure but rather by several measures that are interrelated (Hair Jr. et al.,

2006). In this case, factor analysis was used to identify the key dimensions of

participant’s lifestyles in order to reduce the variables for further analysis. Since

the research questions that will be solved with this analysis are exploratory, and

152 what is being grouped are a series of variables, this is an R-type factor analysis

(Hair Jr. et al., 2006).

Within statistical theory for factor analysis, the basic assumption is that all variables should be metric and normal (Hair Jr. et al., 2006; H. Lee, 2005). The lifestyle (AIO) variables were measured using a 5-level Likert scale and therefore can be assumed to be metric. When tested for normality using graphic analyses in the form of histograms, and tests of skewness and kurtosis, it was indicated that within some variables the normality assumption was violated. However, when a series of data transformation techniques were attempted, including logarithmic and square root transformations, it was determined that the original data was determined to be the best format for additional analysis.

One of the most important considerations in factor analysis design is the observation-per-variable ratio. It is recommended to have at least five observations per variable with an ideal ratio of ten observations per variable (Hair

Jr. et al., 2006). In this study 73 activity interest and opinion (AIO) statements were used and there were 63 respondents in part 1 of the research. However, the number of missing values reduced the number of valid observations to 45.

Due to this a Missing Value Analysis was undertaken using Little’s Missing

Completely At Random (MCAR) test in order to determine if imputation can be a recourse for the missing values. For the activity and interests AIO variables

Little’s MCAR was not significant ࣲଶ(540, n=63) = 551.672, p = .355, therefore the missing variables were determined to be missing completely at random. For the opinion AIO variables Little’s MCAR was not significant ࣲଶ(468, n=63) =

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490.235, p = .230, therefore the missing variables were determined to be missing completely at random. Scheffer states that when data is missing completely at random, imputation methods can be used to replace missing data (2002). In this case the Estimation Maximization algorithm was determined to be the best approach for replacing missing values. The algorithm was undertaken to replace the missing values.

In order to achieve the recommended observation-per-case ratio a series of analyses were done to test the measure of sampling adequacy (MSA) and reduce the number of variables. Hair et al. state that the “MSA values must exceed .50 for both the overall test and each individual variable; variables with values less than .50 should be omitted from the factor analysis one at a time, with the smallest one being omitted each time” (2006, pp.115). Using this test the lifestyle activity/interest variables and the lifestyle opinion variables were each analyzed separately to reduce the number of variables to be analysed. In the case of activity and interest AIO variables, ten were removed prior to combining them with the opinion variables. Of the opinion AIO variables, six were removed prior to combining all AIO variables. Once all AIO variable were combined, an additional 41 variables were removed from the analysis, leaving 12 lifestyle variable remaining related to activities, interests and opinions. This resulted in a ratio of 5.25 cases per variable which meets the criteria for further factor analysis.

The first factor analysis was conducted using the maximum likelihood method with a VARIMAX rotation, and then extracting factors with eigenvalues

154 greater than 1.00 in order to estimate the number of factors to extract. An orthogonal rotation method was selected since it is the more widely accepted method and with the small sample size an oblique rotation could become too specific to the sample and non-generalizable (Hair Jr. et al., 2006). A VARIMAX rotation was selected because, in comparison to other orthogonal methods, it has been shown to provide a clearer separation between factors (Hair Jr. et al.,

2006). The first factor analysis resulted in a factor solution with 4 factors, however factor analysis design recommends that there should be five variables for every factor indicating the maximum number of factors extracted should be three (Hair Jr. et al., 2006). Review of the scree plot further indicated that a three factor solution was most viable.

A second and final factor analysis was completed using the same methods as previously discussed with a predetermined three factor solution.

Overall 44% of the variances across the 12 variables were explained by the three factor solution. According to Hair et al. when the sample size falls into the 60 to

70 range the required rotated factor loading should at minimum be between ±.65 and ±.70 (2006). Bartlett test of sphericity of the final 3 factor was significant at p

< .01 and the Kaiser-Meyer-Olkin Measure of Sampling Adequacy was .823.

Table 34: VARIMAX-rotated Extraction of Final Three-factor Solution

Number of Factors Extracted Eigenvalues % of variance Cumulative % of Variance 1 4.518 27.185 27.185 2 1.461 12.463 39.648 3 1.066 7.629 47.277

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The first factor consisted of factor loadings that were related to the aesthetic,

family, and features of the home with a strong focus on the quality of the living

environment. This first factor was named “Space Quality” (Table 35). The second

factor specifically was related to items of low maintenance living and

convenience, such as keeping the home clean and convenient maintenance.

While this factor included being a convenient location, the focus was

maintenance and was therefore named “Easy Maintenance” The third factor was

mostly composed of one variable which was on balancing family needs and was

named “Balancing Needs” (Table 35). The Cronbach’s Alphas for the three

factors ranged from 0.546 to 0.966 (Table 36). While two factors have less than five items and one factor had a Cronbach Alpha lower than .70 the factors were

determined to be sufficient for further analysis.

Table 35: VARIMAX-rotated Factor Loadings of Final Three-factor Solution

Factor 1 Factor 2 Factor 3 Space Easy Variable Quality Maintenance Balancing Needs I want my home to have up-to-date features 0.727 I want a home where my family can spend time together 0.724 I want to live in a home which is pleasant for me to look at 0.679 Having a beautifully landscape outdoor space adds much to the joy of living 0.442

I want a home that is easy to keep clean 0.655 I want a home in a convenient location 0.620 I spend a lot of time in my garage -0.556 A condo is convenient because there is minimal outdoor maintenance 0.492 I want a place that is easy and safe to lock up and leave for extended periods to travel (for work or pleasure) 0.364

I want a home where the needs of all household members are balanced 0.901

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Table 36: Inter-item Reliability of Lifestyle Factors: Cronbach’s alpha Factor Number of items Included Cronbach’s Alpha Factor 1: (Space Quality) 4 0.854 Factor 2: (Easy Maintenance) 5 0.546 Factor 3: (Balancing Needs) 1 0.966

5.2.2. Lifestyle Clusters: Cluster Analysis

Cluster analysis is a multivariate analysis technique used to group or

classify cases based on the characteristics that they possess (Hair Jr. et al.,

2006). Cluster analysis is a commonly used method in market research for the

purposes of market segmentation and specifically lifestyle segmentation (Punj &

Stewart, 1983). In this study, a series of K-mean cluster analyses were used to

classify respondents based on lifestyle.

Three composite variables that were created based on the output of each

of the final three factor solution of the factor analysis (Functional + Aesthetic,

Convenience, and Balancing Needs) were created. These standardized Z scores

were used as the independent variables in the cluster analysis. Hair et al.

identified that both hierarchical cluster analysis and non-hierarchical cluster

analysis have benefits and flaws and recommend combining the two methods to

maximize the benefits of each (2006). In this analysis, hierarchical cluster

analysis was used to determine the appropriate number of clusters and to identify

outliners, while a non-hierarchical cluster analysis was used for the final solution.

A hierarchical cluster analysis was done using the complete linkage

method and the squared Euclidean distance measure on the complete set of

data (n=63). This first analysis identified three cases that were significant outliers;

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these outliers were removed from further clustering. Further analysis was done

for two- three- and five- cluster solutions on the remaining cases (n=60). The

general rule of thumb is to look at the agglomeration schedule to identify where

the distance coefficients have the most significant increase (Hair Jr. et al., 2006).

In this case, the five-cluster solution mark had the most significant increase.

Non-hierarchical cluster analysis was performed using the K-mean

method with the number of clusters indicated by the hierarchical cluster analysis.

The number of cases for the final five clusters were 6, 11, 6, 12 and 25. In order

to validate the clusters, an additional cluster analysis with a reduced sample was

completed. The cluster memberships of the reduced sample were compared with

those of the original clusters. In this study, two separate reduced sample sizes of

42 cases (70% of total sample) were used in a K-mean cluster analysis using the

previously identified five cluster solution. Ninety percent and 89% of the cases in the reduced cluster analyses were assigned to the original cluster (Table 37).

Therefore five lifestyle clusters where confirmed.

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Table 37: Validation of Cluster with Reduced-sample Clustering Original Cluster Cluster A Cluster B Cluster C Cluster D Cluster E Reduced Sample n (%) n (%) n (%) n (%) n (%) 70% sample 1 Cluster A 5 (100) 0 (0) 0 (0) 0 (0) 0 (0) Cluster B 0 (0) 6 (67) 0 (0) 0 (0) 2 (15) Cluster C 0 (0) 3 (33) 4 (100) 0 (0) 0 (0) Cluster D 0 (0) 0 (0) 0 (0) 11 (100) 0 (0) Cluster E 0 (0) 0 (0) 0 (0) 0 (0) 11 (85) Total 5 (100) 9 (100) 4 (100) 11 (100) 13 (100)

70% sample 2 Cluster A 3 (100) 0 (0) 1 (20) 2 (25) 0 (0) Cluster B 0 (0) 8 (100) 0 (0) 0 (0) 0 (0) Cluster C 0 (0) 0 (0) 4 (80) 0 (0) 0 (0) Cluster D 0 (0) 0 (0) 0 (0) 6 (75) 2 (11) Cluster E 0 (0) 0 (0) 0 (0) 0 (0) 16 (89) Total 3 (100) 8 (100) 5 (100) 8 (100) 18 (100) Note: Percent’s are valid with each original cluster

5.2.3. Profiling Lifestyle Clusters

Lifestyle factors that were mean scores of transformed variables in each lifestyle cluster were compared using one-way analysis of variance (ANOVA) to identify lifestyle characteristics of the four lifestyle clusters. One of the critical assumptions of one-way ANOVA is homogeneity of variance among groups and violation of this assumption can influence hypothesis testing (Hair Jr. et al.,

2006). Levene’s equal variance test is one of the techniques used to test the equal variance assumption (Howell, 2002). In this case, Levene’s test of homogeneity for the “Space Quality” and the “Easy Maintenance” factors were both not statistically significant with p >.05 at .476 and .308 respectively.

However, factor three, the “Balancing Needs” factor was statistically significant at

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p < .05 meaning the test of homogeneity was violated. When the equal variance

assumption is violated, either Welch’s method or Brown-Forsythe’s method is

recommended as an alternative way to measure mean differences among groups

(Howell, 2002). In this case the Brown-Forsyth test was used.

Table 38: One-way ANOVA, Brown-Forsythe Test, and Fitch’s LSD Comparisons: Lifestyle Clusters and Lifestyle Factors

Cluster A Cluster B Cluster C Cluster D Cluster E Convenient Space Neutral Care Free Family Clustering Variables Family Seekers Cluster Living Focused n 6 11 6 12 25 Factor 1: -1.374449 0.297243 -0.668851 0.395609 0.384409 (Space Quality)A Factor 2: 0.528676 -0.646711 -0.53887 0.676264 0.164591 (Easy Maintenance)B Factor 3: 0.024864 -0.313916 -0.782835 -0.73725 0.837683 (Balancing Needs)C A F (4, 55) = 29.083, p = .000 (p<.001) Compared using Brown-Forsythe method. B F (4, 55) = 21.777, p = .000 (p<.001) Compared using Brown-Forsythe method. C F (4, 55) = 33.120, p = .000 (p<.001) Compared using Brown-Forsythe method.

The results of the ANOVA did not provide sufficient information to

adequately describe the five clusters. A series of cross tabulation frequency

analyses against the original variables used in the factor analysis were used to

help name each cluster. Cluster A showed the highest mean score of the three

lifestyle factors on the Easy Maintenance factor and was named the “Convenient

Family” cluster. Cluster B showed the highest mean scores on the space quality factor with negative scores on the other two factors, however, further analysis showed that the focus of this cluster was the amenities associated with living in a single family home and in a community with amenities associated with suburban living thus was named “Space Seekers” cluster. Cluster C showed negative scores on all three factors. As a result, further analysis was required to describe

160 the cluster. When compared to all of the AOI statements it was shown that

Cluster C did not care strongly about any of the statements showing a level of neutrality within housing lifestyles. Further demographic analysis did not provide any additional clarification. As a result, Cluster C was named the “Neutral” cluster. Cluster D showed high mean scores on both the Easy Maintenance and the Space Quality factor and upon further analysis was shown to have a focus on convenience, therefore was named the “Care-free Living” cluster. Cluster E showed the highest mean scores on the Balancing Needs factor with a high mean score on the Space Quality factor; further analysis showed strong focus on family and was therefore named the “Family Focused” cluster.

Each of the lifestyle clusters were further profiled against participants’ stated neighbourhood and housing preferences. The neighbourhood and housing preferences were measured using a five point semantic differential scale. When comparing multiple groups using the semantic differential scale, means can be plotted on a graphical chart to indicate the significance of the differences

(Brinton, 1961; Rosenthal & Rosnow, 1984). Figure 43 and Figure 44 are a combined representation of the housing preference profiles of each of the five lifestyle clusters.

The first nine preference items indicate participants’ preferences in regards to traveling distance to specific amenities. The Care-free Living cluster generally preferred to have amenities within walking distance of their homes with the exception of schools and daycares. The Space Seekers generally preferred to have amenities within driving/transit distance. The Convenient Family and

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Family Focused clusters had a number of similarities, however, the Convenient

Family cluster had a stronger preference for walking to a number of amenities including grocery stores, schools and daycares (Figure 43).

Housing and neighbourhood preference items 10 to 16 indicate participants’ preferences for neighbourhood structure and housing size. In terms of visual appearance all five of the lifestyle clusters agreed on a more visually stimulating neighbourhood. The Space Seekers cluster tended to prefer more single family homes, less of a mix of housing types, and generally larger personal spaces. The Family Focused cluster followed a similar pattern to that of the

Space Seekers with preferences tending to be less strong on the amount of single family homes and the size of personal spaces. The Convenient Family cluster general preferred more of a mix housing types, with less single family houses and more multi-family housing; this cluster also preferred smaller personal spaces. The Care-free Living cluster was more neutral in comparison to the other clusters but generally had preferences for smaller personal space and more of a mix of housing types.

Housing and neighbourhood preference items 18 to 20 are public realm preference factors. Participants’ in all lifestyle clusters agreed that bike paths should be highly accessible and connected and that neighbourhood parks should be somewhat active with lots of people. Where the cluster diverged was the preferences for the design of street and public spaces. The Care-free Living cluster preferred designed and art filled public spaces while the neutral cluster preferred simple and uncluttered public spaces (Figure 44).

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Housing and neighbourhood preference items 21 to 25 were related to privacy and safety. Generally, in terms of privacy, the Family Convenience cluster has a greater preference/tolerance for noise levels within the neighbourhood and within the home. The Neutral cluster has the lowest tolerance for noise levels. The Space Seekers cluster and the Family Focused cluster also have a lower tolerance for noise. Meanwhile, Space Seekers, Family Focused and the Family Convenience clusters have a greater preference for visual privacy. The Family Convenience cluster places lower importance on safety while the Family Focused cluster, the Neutral cluster and the Space Seekers clusters place a great deal of importance on safety. All clusters prefer low traffic.

Previous research has shown that diversity has an effect on neighbourhood preferences (Lu, 1999b). Housing and neighbourhood preference items 26 to 28 were related to neighbourhood diversity. Generally, the Neutral cluster and the Care-free Living cluster have a greater preference for diversity and for a greater range of age groups and life stages. The Single Family cluster and the Family Focused cluster had the lowest tolerance for all three diversity items with lowest tolerance for the diversity (age, income, and ethnicity) item.

Housing and neighbourhood preference items 29 to 30 were related to parking. The Convenience Family cluster had the greatest preference for on- street parking for residents while the Care Free Living cluster had the greatest preference for provided private parking. Generally all five clusters felt that street parking for visitors was acceptable, as was outside parking access (Figure 44).

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Housing and neighbourhood preference items 31 to 55 were related to commuting. The Care Free Living cluster and the Neutral cluster have the greatest preference for commuting by walking and the Space Seeker has the least preference for commuting by walking. The Space Seeker cluster and the

Family Focused cluster had the greatest preference for commuting by car, while the convenient Family cluster indicated that this was not important (Figure 44).

In summary, the neighbourhood preference semantic differential analysis indicates the lifestyle clusters were effective in summarizing the differences between participants.

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Lifestyle Clusters - Housing Preferences Semantic Differential Chart (Part 1)

1. Distance to grocery storesTraveling and distance to groceryWalking stores Driving/Transit shopping

2. Distance to cafes, restaurants and Traveling distance to cafes, restaurants Walkingand bars Driving/Transit bars

3. Distance to fitnessTraveling and yoga distance to fitness and yogaWalking studios Driving/Transit studios

Driving/Transit 4. DistanceTraveling to entertainment distance to entertainment and and cultural activitiesWalking cultural activities

5. Distance to boutiques,Traveling distance salons to and boutiques, salons andWalking spas Driving/Transit spas

6. Distance to person care and Walking Traveling distance to person care and physicians Driving/Transit physicians

7. Distance to schools Traveling distance toWalking schools Driving/Transit

8. Distance to daycares Traveling distance to daycaresWalking Driving/Transit

9. DistanceTraveling to municipal distance to municipalcommunity community centre andWalking team Driving/Transit centre and team activities activities

10. Visual appearanceThe visual appearance of the of the neighbourhood shouldVisually be Uniform in Descriptors neighbourhood stimulating appearance More of a mix of The mix of housing types in my neighbourhoodLess of should a mix be of 11. The mix of housing types housing types housing types

The amount of single family homes in my neighbourhoodLess should single be Mostly single 12. Amount of single family homes family homes family homes

Less multi-family Mostly multi-family The amount of multi‐family homes in my neighbourhood should be 13. Amount of multi-family homes homes homes

14. The size of my home The size of mySmall home (600sqft)should be Large (2500sqft)

Small (balcony Large (sports 15. Size of my outdoorThe size space of my outdooror yard space or yard should be /terrace) activities)

Small (minimize Large (enhanced 16. The lot size of my home The lot size of my homeyard should space) be back yard)

Highly accessible 17. AccessThe to access bike pathsto bike paths in my neighbourhoodAway should from be and connected residences across the city

18. The parks in my neighbourhoodThe parks in my neighbourhood Quiet should and be Active with lots of should be Secluded people

12345 Values Convenient Family Cluster SingleSpace FamilySeeker Dwellers Cluster Cluster Neutral Cluster Care‐Free Living Cluster Family Focused Cluster Figure 43: Lifestyle Clusters - Housing Preferences Semantic Differential Chart (Part 1)

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Lifestyle Clusters - Housing Preferences Semantic Differential Chart (Part 2)

The public spaces and sidewalks in my neighbourhoodQuiet and uncrowded should be Active with lots 19. Public spaces and sidewalks of people

The streets and public spaces in my neighbourhoodSimple should and be Designed and 20. Streets and public spaces uncluttered full of art

21. The noise level in my High noise neighbourhood shouldThe noise be level in my neighbourhoodLow should noise be

To22. what Willingness degree are youto hearwilling your to hear your neighbours in yourNot home at all Daily neighbours

Low degree of visual High degree of 23. VisualThe visual privacy privacy in my neighbourhood and home should be privacy visual privacy

The amount of vehicle traffic in my neighbourhoodLow should Traffic be 24. Amount of vehicle traffic High Traffic

The sense of safety that my neighbourhoodNot provides important me is 25. Sense of safety Important

The range of age groups in my neighbourhoodSimilar should ages be Wide range of 26. Range of age groups ages

The diversity (age, income, ethnicity, etc.)of the residents of my 27. The diversity (age,neighbourhood income, should be Low diversity High diversity ethnicity, etc.)of the residents The stage in life (e.g. single, family, retired, etc.) of the residents of my

Descriptors 28. The stage in neighbourhoodlife of the should be Similar life stage Wide range of residents life stages

29.The The parking parking for private for private residents of my neighbourhoodStreet should Parking be Provided residents Parking

Provided 30. Visitor parkingThe visitor parking for my neighbourhoodStreet should Parking be Parking

31. CarTo what access degree are you willing to go outside Outsideto access yourAccess car Inside Access

32. CommuteBeing toable work/school commute to work/schoolby by walking from my Not important Important walking neighbourhood is

33. Commute to work/school by Being able commute to work/school by car from my neighbourhoodNot important is Important car

34. Commute to work/school by Beingbike able commute to work/school by bike from my neighbourhoodNot important is Important

Being35. able Commute commute toto work/school by bytransit from my neighbourhood Important is Not important transit

Being able to access major corridors and highways from my 36. Access major corridors and Not important Important highways neighbourhood is

12345 Values Convenient Family Cluster SingleSpace FamilySeeker Dweller Cluster Cluster Neutral Cluster Care‐Free Living Cluster Family Focus Cluster

Figure 44: Lifestyle Clusters - Housing Preferences Semantic Differential Chart (Part 2)

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5.3. Summary

This purpose of this chapter was to provide an overview of the research participants and to position a profile of the participants of the research including lifestyles, housing and neighbourhood preferences. It explored the lifestyle element of participant’s activities, interests and opinions as they relate to their living and housing situation. In this profile, a total of 63 cases were used in the descriptive analysis of participants. These cases were collected using the part 1 online Fluid Survey. The majority of participants were under the age of 45 and had a minimum of a bachelor’s degree. The four major household structures included: participant only, participant and roommates, participant and spouse, and participant, spouse and children.

Using a combination of factor and cluster analysis five lifestyle clusters were identified: Convenient Family, Space Seekers, Neutral, Care Free Living, and

Family Focused. These clusters were profiled against a series of stated neighbourhood preferences questions in a semantic differential format. Using a semantic differential graph the neighbourhood preferences of each of the lifestyles determined. While some preferences were similar across the clusters, there were a number that were shown to be significantly different across all clusters. This indicates that the lifestyle clusters were effective in summarizing the differences in each group’s neighbourhood and housing choices.

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6. Results: Urban Form, Density Perceptions and Lifestyle

This chapter aims to explore what urban form is preferred by most

participants and if urban form affects how density is perceived. It also seeks to

determine if there is a relationship between perceived density and preferred

urban form and the impacts, if any, participant lifestyle has on what urban forms

are most preferred by participants. The statistical analysis and assumptions

related to this analysis are described in section 4.10. Section 6.1.1 provides the

results of preferred urban forms, section 6.1.2 provides the results of perceived

density, section 6.1.3 provides the result of the relationship between preferred urban form and perceived density, and section 6.1.4 provides the results of participant lifestyles and preferred urban form. Section 6.2 provides a discussion of the results and their implications, with the final section concluding the chapter.

6.1. Findings: Urban Form and Density Perceptions

6.1.1. Research Question 1: Preferred Urban Form

Q1: Which visual stimuli represents the preferred type of urban form? Least

preferred type of urban form?

Q1a. Which visual stimuli was ranked first overall by most participants? Last

overall by most participants?

Q1b. Were there any visual stimuli that were polarizing amongst

participants?

Q1c. Which visual stimuli was ranked highest when the ranking is grouped?

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Q1d. Which visual stimuli was ranked highest by the majority of participants

when a weighting is applied to each choice?

Q1a. Which visual stimuli was ranked first overall by most participants?

Least overall by most participants?

Overall, most participants, 30% chose visual stimuli 5 (evolutionary density

pattern) as their first preference when ranking them in order. Visual stimuli 6

(transit-oriented development density pattern) was the second highest stimuli to

be ranked as first at 24%. Visual stimuli 2 (corridor density pattern), visual stimuli

3 (nodal/ urban village density pattern) and visual stimuli 4 (evenly distributed /

neotraditional density pattern) are more evenly distributed with 15%, 13% and

11% respectively. Visual stimuli 1 (modern density pattern) received the lowest

number of participants selecting it as their first choice at 4% of participants (Table

39). Second preference amongst participants is evenly distributed across five out

of the six visual stimuli ranging between 11% and 15% across the five. The

largest grouping of participants indicated visual stimuli 3 as their second

preference amongst the neighbourhood types with 31% (Table 39). Figure 45

best represents the spike in the number of participants preferring visual stimuli 3

as their second choice.

Among the six (6) visual stimuli, visual stimuli 1 was ranked last or sixth

place overall by the largest group of participants at 26% or 14 participants. Both

stimuli 5 and stimuli 6 had a large percentage of participants ranking them last overall with 20% and 24% respectively. Stimuli 2 and stimuli 3 had a

169 proportionally low percentage of participants ranking them as their least preferable choice at 7% and 2% respectively (Table 39).

Table 39: Visual Stimuli Preference Ranking

Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6 Preference n % n % n % n % n % n %

1st 2 (4) 8 (15) 7 (13) 6 (11) 16 (30) 13 (24)

2nd 6 (11) 7 (13) 17 (31) 8 (15) 6 (11) 8 (15)

3rd 10 (19) 11 (20) 12 (22) 9 (17) 7 (13) 3 (6)

4th 13 (24) 10 (19) 9 (17) 12 (22) 6 (11) 3 (6)

5th 7 (13) 12 (22) 6 (11) 8 (15) 6 (11) 12 (22)

6th 14 (26) 4 (7) 1 (2) 9 (17) 11 (20) 13 (24)

Missing 2 (4) 2 (4) 2 (4) 2 (4) 2 (4) 2 (4)

Total 54 (100) 54 (100) 54 (100) 54 (100) 54 (100) 54 (100)

Median 4 3.5 3 4 3 4

Mode 6 5 2 4 1 1/6

Note: The total percentages may not be 100 due to rounding.

Participant Ranking of Each Visual Stimuli

18 16 14 12 10 8 6 4 2 0 Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6

Preference 1 Preference 2 Preference 3 Preference 4 Preference 5 Preference 6

Figure 45: Participant Ranking of Each Visual Stimuli

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Q1b. Were there any visual stimuli that were polarizing amongst participants?

Two of the six visual stimuli stand out as being polarizing across all participants. Visual stimuli 5 and 6 had significant percentages of participants selecting them as their first and last choices. Visual stimuli 5 had the largest percentage of participants chose it at their first choice overall with 30%, however, it also had 20% of participant select it as their least preferred. Visual stimuli 6 had a similar level of polarity among participants. When combined, the first and second preferences attribute to 39% of participants while the fifth and sixth preference represents 46% of participants (Table 39). The column chart shown in

Figure 45 best represents this polarity.

Q1c. Which visual stimuli was ranked highest when the ranking is grouped?

Table 40 indicates the ranking when first and second preferences are grouped, third and fourth preferences are grouped and fifth and sixth preferences are grouped. The preference rankings were grouped to determine the level of magnitude to which all participants preferred or did not prefer each visual stimuli.

When there are multiple options, as in this case where there were six, the participants first and second preference ranking have a level of similarity which can be grouped to show this overall magnitude of preference. When the groups are formed, visual stimuli 3 has the largest number of participants indicating it as their top preference with 44%. This is followed by visual stimuli 5 with 41% and visual stimuli 6 with 39% (Table 40). Visual stimuli 1 had the greatest number of

171 people select it as the middle ground stimuli with 43% of participants. Visual stimuli 2, 3, and 4 tied for second in the middle ground with 39% of participants

(Table 40). Visual stimuli 6 had the most ranking in the bottom two choices with

46% of participants, followed by visual stimuli 1 with 39% of participants. Visual stimuli 3 had the lowest ranking in the bottom two ratings with 13% of participants

(Table 40).

Table 40: Visual Stimuli Ranking with Paired Preferences

Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6 Preference n % n % n % n % n % n % 1 + 2 8 (15) 15 (28) 24 (44) 14 (26) 22 (41) 21 (39) 3 + 4 23 (43) 21 (39) 21 (39) 21 (39) 13 (24) 6 (11) 5 + 6 21 (39) 16 (30) 7 (13) 17 (31) 17 (31) 25 (46)

Missing 2 (4) 2 (4) 2 (4) 2 (4) 2 (4) 2 (4) Total 54 (100) 54 (100) 54 (100) 54 (100) 54 (100) 54 (100)

Median 3+4 3+4 3+4 3+4 3+4 3+4 Mode 3+4 3+4 1+2 3+4 1+2 5+6 Note: The total percentages may not be 100 due to rounding.

Visual Stimuli Ranking with Paired Preferences

Stimuli 3

Stimuli 5

Stimuli 6

Stimuli 2

Stimuli 4

Stimuli 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st + 2nd Preference 3rd + 4th Preference 5th + 6th Preference

Figure 46: Visual Stimuli Ranking with Paired Preferences

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Q1d. Which visual stimuli was ranked highest by the majority of participants when a weighting is applied to each choice?

In addition to grouping the preferences in question 1c, a weighting was also applied to the preference ranking to identify the definitive preference order of the visual stimuli. This was done in order to determine which stimuli were identified as more positively ranked across the majority of the participants. In this weighting system a participant’s first preference would receive proportionally more weight than their last. In this case, first preference received a weight of six and decreased in intervals of one to the sixth and last preference which received a weight of one (Table 41). The maximum overall weighting that a visual stimuli could receive with this system is 312 based on the number of participants that completed this question.

When this weighting is applied (shown in Table 41) visual stimuli 3 ranks first with 215 followed by stimuli 5 with 195 and stimuli 2 with 185. The stimuli with the lowest overall weighted ranking is stimuli 1 with 149 (Table 41). See the stacked column chart in Figure 47 for a detailed comparison.

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Table 41: Visual Stimuli Preference Ranking with Weighting Applied

Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6

Preference weight n rank n rank n rank n rank n rank n rank

1 6 2 12 8 48 7 42 6 36 16 96 13 78

2 5 6 30 7 35 17 85 8 40 6 30 8 40

3 4 10 40 11 44 12 48 9 36 7 28 3 12

4 3 13 39 10 30 9 27 12 36 6 18 3 9

5 2 7 14 12 24 6 12 8 16 6 12 12 24

6 1 14 14 4 4 1 1 9 9 11 11 13 13

- 2 2 2 2 2 2

Total 54 149 54 185 54 215 54 173 54 195 54 176

Placement 6 3 1 5 2 4 Note: The total percentages may not be 100 due to rounding.

Visual Stimuli Preference Ranking with Weighting Applied 250

200

150

100

0 Stimuli 3 Stimuli 5 Stimuli 2 Stimuli 6 Stimuli 4 Stimuli 1

1st Preference 2nd Preference 3rd Preference 4th Preference 5th Preference 6th Preference

Figure 47: Visual Stimuli Preference Ranking with Weighting Applied Statistical Significance of Urban Form Preferences

In testing the statistical significance of the results a one-way within subjects or repeated measures analysis of variance (ANOVA) was conducted to compare the effect of urban form on the level of preference across the six visual stimuli. Mauchly’s test indicated that the assumption of sphericity had been violated (chi-square = 23.864, p =.048), therefore degrees of freedom were

174 corrected using Huynh-Feldt estimates of sphericity (epsilon = 0.930). The results show that the preference levels differed significantly, F(4.650, 9.30) = 2.819, p =

024. Multivariate tests also showed there was a significant effect of urban form,

Wilks’ Lambda = 0.686, F (5,47) = 4.303, p = .003. Six paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 1 (M=4.135, SD=.207) and visual stimuli 2 (M=3.442, SD=.215) conditions; t(51)=2.306, p = .025. A second paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 1 (M=4.135,

SD=.207) and visual stimuli 3 (M=2.865, SD=.180) conditions; t(51)=4.165, p <

.000. A third paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 2 (M=3.442, SD=.215) and visual stimuli

3 (M=2.865, SD=.180) conditions; t(51)=2.009, p = .05. A fourth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 3 (M=2.865, SD=.180) and visual stimuli 4 (M=3.673, SD=.224) conditions; t(51)=-2.804, p = .007. A fifth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 3 (M=2.865,

SD=.180) and visual stimuli 6 (M=3.615, SD=.280) conditions; t(51)=-2.095, p =

.041. Finally, a sixth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 1 (M=4.135, SD=.207) and visual stimuli

5 (M=3.250, SD=.272) conditions; t(51)=-2.574, p = .013. The results showed that there were no other significant differences between the results.

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The results indicate that while not all the relationships in results were

statistically significant, the result that visual stimuli 3 was more preferred than

visual stimuli 1, 2, 4, and 6 was statistically significant. The results also showed a

statistical significance that visual stimuli 5 was more preferred than visual stimuli

Summary

Visual stimuli 5 (evolutionary density pattern) represented the overall first

choice preference amongst those who participated in the visualization portion of the research study. However, visual stimuli 5 (evolutionary density pattern) represented one of the more polarizing urban forms with 30% of participants identifying it as their most preferred choice and 20% of participants identifying it as their least preferred choice. Once a weighting was applied to the participants’ preference ranking, it showed that visual stimuli 3 (nodal / urban village density pattern) was more positively regarded across all participants receiving 215 out of a possible weighted score of 312. Visual stimuli 3 (nodal / urban village density pattern) was also identified as the most preferred, when their first and second preferences were combined, with 44% of participants ranking it in their top 2 preferred options. Visual stimuli 3 (nodal / urban village density pattern) also had the lowest number of participants rank it in their bottom two options with only

13% of participants. Overall, visual stimuli 1 (modern density pattern) received the lowest first preference ranking, the highest last preference ranking and the lowest overall weighted score. However, when the scores were paired, shown in

Table 40, visual stimuli 6 (transit-oriented development density pattern) had the

176 largest number of participants rank it in the bottom two choices. Based on this analysis visual stimuli 3 (nodal / urban village density pattern) is representative of the most preferred urban form stimuli and visual stimuli 1 (modern density pattern) is indicative of the least preferred urban form stimuli, which was also shown to be statistically significant. Figure 48 demonstrates a summary of this result.

Visual Stimuli 3: Nodal/Urban Village Density Pattern

Visual Stimuli 5: Evolutionary Density Pattern Most Preferred

Visual Stimuli 1: Least Preferred Modern Density Pattern Figure 48: Summary of Visual Stimuli Preferences

6.1.2. Research Question 2: Perceived Density

Q2. Which visual stimuli and associated form was perceived as the densest? The least dense?

Q2a. Which visual stimuli was perceived to have the densest built form?

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Q2b. Which visual stimuli was perceived to have the least dense / most

spacious built form?

Q2c. Which visual stimuli was estimated as having the highest and lowest

population density by the participants?

Q2a. Which visual stimuli was perceived as the densest to have the densest built form?

Overall, this indicates that generally participants felt all the stimuli were in the “somewhat dense” to “dense” range. Visual stimuli 5 was perceived by the largest proportion of participants as “very dense” with 13% of participants followed by stimuli 1 with 11% of participants. Thirty-five percent of participants rated visual stimuli 6 as “dense” and 33% of participants indicated visual stimuli 5 as “dense” (Table 42). All six visual stimuli have a median perceived density of

“somewhat dense” and all six stimuli had a mode of either “somewhat dense” or

“dense” (Table 42).

When the rankings of “dense” and “very dense” are grouped, as shown in

Table 43 and Figure 50, visual stimuli 5 was perceived as the densest by 46% of participants. This is followed by visual stimuli 6 where 41% of participants indicated it as dense. When all three density levels are grouped, “somewhat dense”, “dense” and “very dense”, visual stimuli 2 had the highest percentage of participants indicate it as dense with 69% of participants. Visual stimuli 1 and 5 were perceived to be second and third densest with 67% and 65% respectively

(Table 43).

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Participant Rating of Built Form 25

0 Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6

Very Spacious Spacious Somewhat Spacious Neutral Somewhat Dense Dense Very Dense

Figure 49: Participant Rating of Built Form Q2b. Which visual stimuli was perceived to have the least dense / most spacious built form?

Visual stimuli 5 is the only stimuli to have been rated by any participant as

“very spacious” with 2% of participants. Thirteen percent of participants rated visual stimuli 3 as “spacious”, while stimuli 1 and 6 are rated as “spacious” by

11% of participants (Table 42). Visual stimuli 2 is the only stimuli to not have any participants rate it as either “spacious” or “very spacious.” When the “very spacious” and “spacious” ratings are combined, visual stimuli 3 has the largest percentage of participants rate it as “spacious” with 13% of participants. This is followed by visual stimuli 1 and 6 each with 11% of participants (Table 43). When

“somewhat spacious”, “spacious” and “very spacious” are combined, visual

179 stimuli 3 is perceived as most spacious with 33% of participants (Table 43).

Participant Rating of Built Form (Grouped)

Stimuli 3

Stimuli 6

Stimuli 5

Stimuli 1

Stimuli 4

Stimuli 2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Spacious Somewhat Spacious Neutral Somewhat Dense Dense

Figure 50: Participant Rating of Built Form (Grouped) When density is considered in reverse, the stimuli that had the lowest number of participants rate them as “very dense” or “dense” could also be considered. Visual stimuli 3 was the only stimuli to not have any ranking of “very dense” (Table 42). When “very dense” and “dense” are combined, visual stimuli 2 and 4 have the lowest dense rating with 28%, followed by visual stimuli 1 and 3 with 31% (Table 43). When “somewhat dense” “dense” and “very dense” are combined visual stimuli 3 has the lowest number of people perceiving it as dense with 50% of participants; this is followed by visual stimuli 4 with 59% of participants.

Table 42: Participant Rating of Physical Density on a Scale Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6 Choice n % n % n % n % n % n % Very Spacious 0 (0) 0 (0) 0 (0) 0 (0) 1 (2) 0 (0)

Spacious 6 (11) 0 (0) 7 (13) 5 (9) 3 (6) 6 (11)

Somewhat Spacious 7 (13) 8 (15) 10 (19) 7 (13) 10 (19) 9 (17)

Neutral 4 (7) 9 (17) 9 (17) 8 (15) 4 (7) 6 (11)

Somewhat Dense 19 (35) 22 (41) 10 (19) 17 (31) 10 (19) 11 (20)

Dense 11 (20) 14 (26) 17 (31) 13 (24) 18 (33) 19 (35)

Very Dense 6 (11) 1 (2) 0 (0) 2 (4) 7 (13) 3 (6)

Missing 1 (2) 0 (0) 1 (2) 2 (4) 1 (2) 0 (0) Total 54 (100) 54 (100) 54 (100) 54 (100) 54 (100) 54 (100)

Median 1 1 1 1 1 1 Mode 1 1 2 1 2 2 Note: The total percentages may not be 100 due to rounding. A. Multiple modes exist. The smallest value is shown.

Table 43: Participant Rating of Physical Density on a Scale (Grouped)

Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6 Choice n % n % n % n % n % n % Spacious 6 (11) 0 (0) 7 (13) 5 (9) 4 (7) 6 (11) Somewhat Spacious 7 (13) 8 (15) 10 (19) 7 (13) 10 (19) 9 (17) Neutral 4 (7) 9 (17) 9 (17) 8 (15) 4 (7) 6 (11) Somewhat Dense 19 (35) 22 (41) 10 (19) 17 (31) 10 (19) 11 (20) Dense 17 (31) 15 (28) 17 (31) 15 (28) 25 (46) 22 (41) Missing 1 (2) 0 (0) 1 (2) 2 (4) 1 (2) 0 (0) Total 54 (100) 54 (100) 54 (100) 54 (100) 54 (100) 54 (100)

Median 1 1 1 1 1 1 Mode 1 1 2 1 2 2 Note: The total percentages may not be 100 due to rounding.

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Statistical Significance of Perceptions of Built Form

In order to determine the statistical significance of the results a one-way within subjects or repeated measures ANOVA was conducted to compare the effect of urban form on perceptions of built form within the six visual stimuli.

Mauchly’s test indicated that the assumption of sphericity had been violated (chi- square = 40.313, p <.000), therefore degrees of freedom were corrected using

Greenhouse-Geisser estimates of sphericity (epsilon = 0.747). The results show that the preference levels differed significantly, F(3.734, 179.232) = 1.474, p =

.030. Multivariate tests also showed there was a significant effect of urban form on perceived physical density and buildings mass, Wilks’ Lambda = 0.717, F

(5,44) = 3.480, p = .010. Six paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 2 (M=4.8679,

SD=1.01976) and visual stimuli 3 (M=4.3774, SD=1.44417) conditions; t(52)=3.465, p = .001. A second paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 3 (M=4.3774, SD=1.44417) and visual stimuli 5 (M=4.8654, SD=1.58459) conditions; t(51)=2.236, p = .030. A third paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 3 (M=4.3774, SD=1.44417) and visual stimuli 6

(M=4.7170, SD=1.54421) conditions; t(52)=1.701, p = .095. The results showed that there were no other significant differences between the remainder of the results.

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The ANOVA indicated that there is statistically significant result in the difference in perceived built form between visual stimuli 3 and visual stimuli 2, 5 and 6. This shows that the result that visual stimuli 3 is perceived to have a less dense built form is a statistically significant result.

Q2c. Which visual stimuli was estimated as having the highest and lowest population density by the participants?

As previously stated each of the six visual stimuli were designed to be the same population density at 400 people /jobs per hectare. In an effort to determine if neighbourhood form affects how residents perceive density, participants were asked to estimate the population density of each visual stimuli.

Visual stimuli 1 was estimated by 22% of participants to be between 800 and 900 people and jobs per hectare. Fourteen percent of participants estimated stimuli 5 as having a population density between 800 and 900 people and jobs per hectare (Table 44). Visual stimuli 1, 2, 3, 4 and 6 all have an estimated median density of 300 people and jobs per hectare, while visual stimuli 5 has a median estimated density of 400 people and jobs per hectare (Table 44). The visual stimuli with the highest mean estimated density is stimuli 5 with 447.06 people and jobs per hectare followed by stimuli 1 with a mean of 407.69 people and jobs per hectare (Table 44).

Eighteen percent of participants estimated the population density of visual stimuli 4 as 100 people and jobs per hectare, followed by stimuli 2 with 15% of

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participants estimating the population density as 100 people and jobs per hectare

(Table 44). Thirty-three percent of participants estimated the population density of visual stimuli 1 as 200 people and jobs per hectare, while 24% of participants estimated stimuli 4 as 200 people and jobs per hectare (Table 44). When the two lowest density levels are factored together visual stimuli 4 has 42% of participants estimating the population density between 100 and 200 people and jobs per hectare followed by stimuli 1 with 41% of participants (Table 44). Visual stimuli 4 has the lowest mean estimated population density at 343.14 people and jobs per hectare followed by visual stimuli 3 with 369.23 people and jobs per hectare.

Table 44: Participants Perceived Population Density Level

Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6 Density n % n % n % n % n % n %

100 People/Jobs per Hectare 4 (8) 8 (15) 7 (13) 9 (18) 3 (6) 2 (4)

200 People/Jobs per Hectare 17 (33) 12 (23) 11 (21) 12 (24) 8 (16) 11 (21)

300 People/Jobs per Hectare 8 (15) 8 (15) 11 (21) 10 (20) 10 (20) 14 (27)

400 People/Jobs per Hectare 5 (10) 3 (6) 8 (15) 8 (16) 9 (18) 5 (10)

500 People/Jobs per Hectare 5 (10) 6 (12) 3 (6) 3 (6) 6 (12) 9 (17)

600 People/Jobs per Hectare 2 (4) 8 (15) 7 (13) 4 (8) 3 (6) 6 (12)

700 People/Jobs per Hectare 0 (0) 5 (10) 1 (2) 0 (0) 5 (10) 2 (4)

800 People/Jobs per Hectare 6 (12) 2 (4) 2 (4) 4 (8) 3 (6) 1 (2)

900 People/Jobs per Hectare 5 (10) 0 (0) 2 (4) 1 (2) 4 (8) 2 (4) Total 52 (100) 52 (100) 52 (100) 51 (100) 51 (100) 52 (100)

Mean 407.69 378.85 369.23 343.14 447.06 398.08 Median 300 300 300 300 400 300 Mode 200 200 200/300 200 300 300 SD 260.35 217.23 214.69 214.71 231.82 194.52 Range 800 700 800 800 800 800 Max 100 100 100 100 100 100 Min 900 800 900 900 900 900

Note: The total percentages may not be 100 due to rounding.

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Participants Perceived Population Density Level 18 16 14 12 10 8 6 4 2 0 Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6

100 People/Jobs per Hectare 200 People/Jobs per Hectare 300 People/Jobs per Hectare 400 People/Jobs per Hectare 500 People/Jobs per Hectare 600 People/Jobs per Hectare 700 People/Jobs per Hectare 800 People/Jobs per Hectare 900 People/Jobs per Hectare

Figure 51: Participants Perceived Population Density Level

Participant Perceived Populations Density Level (Grouped)

Stimuli 5

Stimuli 6

Stimuli 2

Stimuli 3

Stimuli 1

Stimuli 4

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

100 - 300 People/Jobs per Hectare 400 - 600 People/Jobs per Hectare 700 - 900 People/Jobs per Hectare

Figure 52: Participant Perceived Population Density Level (Grouped)

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Statistical Significance of Perceived Population Density

In determining the statistical significance of the results a one-way within subjects or repeated measures ANOVA was conducted to compare the effect of urban form on participants perceived / estimated population density across the six visual stimuli. Mauchly’s test indicated that the assumption of sphericity had been violated (chi-square = 35.385, p =.001), therefore degrees of freedom were corrected using Huynh-Feldt estimates of sphericity (epsilon = 0.856). The results show that the preference levels differed significantly, F(4.280, 209.741) = 5.213, p = .000, with a Huynh-Feldt correction. Multivariate tests also showed there was a significant effect of urban form on perceived population density, Wilks’ Lambda

= 0.645, F (5, 45) = 4.958, p = .001. Six paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 1

(M=418, SD=260.055) and visual stimuli 3 (M=372, SD=217.631) conditions; t(51)=1.684, p = .098. A second paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 1 (M=418, SD=260.055) and visual stimuli 4 (M=338, SD=213.704) conditions; t(50)=2.517, p < .015. A third paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 2 (M=386, SD=217.603) and visual stimuli 4 (M=338,

SD=213.704) conditions; t(50)=1.9, p = .062. A fourth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 2

(M=386, SD=217.603) and visual stimuli 5 (M=450, SD=233.212) conditions;

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t(50)=-2.719, p = .009. A fifth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 3 (M=372, SD=217.631) and visual stimuli 5 (M=450, SD=233.212) conditions; t(50)=-2.899, p = .006. A sixth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 4 (M=338, SD=213.704) and visual stimuli 5 (M=450,

SD=233.212) conditions; t(49)=-4.420, p < .000. A seventh paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 4

(M=338, SD=213.704) and visual stimuli 6 (M=404, SD=195.834) conditions; t(50)=-3.091, p = .003. A eighth paired samples t-test indicated that there was a significant difference in the scores for visual stimuli 5 (M=450, SD=233.212) and visual stimuli 6 (M=404, SD=195.834) conditions; t(50)=2.047, p = .046. The analysis of variance showed that there were no other significant differences between the results.

The analysis of variance indicated that the results are statistically significant between visual stimuli 4 perceived as least dense and visual stimuli 1,

2, 5 and 6. It also showed statistical significant results between visual stimuli 5, perceived as having the highest population density and visual stimuli, 2, 3 4 and

6. Lastly the analysis of variance showed that there was statistically significant result between visual stimuli 3, perceived as having the second lowest population density, and the visual stimuli 1 and 5.

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Summary:

Visual stimuli 5 (evolutionary density pattern) was rated highest overall in both the “very dense” (13%) and “dense” (33%) categories by participants. In terms of estimated population density, visual stimuli 5 (evolutionary density pattern) had the highest overall median at 400 people and jobs per hectare whereas all other stimuli had a median of 300 people and jobs per hectare.

However visual stimuli 2 had a higher overall rankings when “very dense” (2%) and “dense” (26%) and somewhat dense (41%) are all considered whereas visual stimuli 5 only had 19% in the somewhat dense category. This result indicates that there is some difficulty ascertaining the difference between these two in terms of perceived density of built form. In addition the results of the statistical significance test indicate the difference is not statistically significant.

However the analysis of variance did show that both visual stimuli 2 and 5 were statistically different from the least dense, visual stimuli 3. Visual stimuli 2 was had a more statistically significant difference from visual stimuli 3 at p=.001 than the statistically significant difference from visual stimuli 5 at p= .030. Visual stimuli 5 (evolutionary density pattern) additionally had the highest overall mean estimated density with 447.06 people and jobs per hectare. Based on these factors visual stimuli 5 (evolutionary density pattern) was perceived to have the highest estimated population density by participants which was shown to be statistically significant result.

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Visual stimuli 4 (evenly distributed / neotraditional density pattern) had the greatest number of participants estimate the population density between 100 and

200 people and jobs per hectare with as statistically significant result of 42% of participants. Stimuli 4 (evenly distributed / neotraditional density pattern) also received the lowest mean estimated population density, 343.14 people and jobs per hectare. The results of the perceived lowest estimated population density were shown to be statistically significant with exception of the difference between visual stimuli 3 and 4. Visual stimuli 3 (nodal / urban village density pattern) was rated by more participants as spacious and less participants as dense with 33% and 50% respectively. Therefore, visual stimuli 3 (nodal / urban village density pattern) was perceived to be least spatially dense by participants and stimuli 4

(evenly distributed / neotraditional density pattern) was perceived to have the lowest population density. Both results were shown to be statistically significant.

Figure 53 provides a summary of the perceived built form results and

Figure 54 provides a summary of perceived population density results.

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Spaciousness Density

Model 3 Model 3 Most Spacious Least Dense

Model 6 Model 4

Model 5 Model 6

Model 1 Model 5

Model 4 Model 1

Model 2 Model 2 Least spacious Most Dense

Figure 53: Summary of Perceived Built Form results

Density

Model 4 Least Dense

Model 3

Model 2

Model 6

Model 1

Model 5 Most Dense

Figure 54: Summary of Perceived Population Density

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6.1.3. Research Question 3: Urban Form Preference and Perceived Density

Relationship

Q3. Is there a correlation between visual stimuli preference and the perceived

density?

Q3a. Is there a correlation between visual stimuli preference and perceived

physical density?

Q3b. Is there a correlation between visual stimuli preference and perceived

population density?

Q3c. Is there a relationship between most preferred visual stimuli

preference and perceived population or physical density?

Correlation analyses were used to examine the relationship between

participants’ preference for a particular visual stimuli and how dense they

perceived that stimuli to be. The correlations were analyzed against both

perceived population density and perceived physical density. A Spearman rank-

order (rho) correlation analysis was selected in each case since the data was

ordinal in nature (Diekhoff, 1992). A Spearman rho correlation is typically used when the correlation is likely non-linear, this is in comparison to a Pearson correlation which is used to describe linear correlations (Diekhoff, 1992). The results of the correlation are shown in Table 45.

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Table 45: Relationship between Visual Stimuli Preferences and Perceived Physical and Population Density Stimuli 1 Stimuli 2 Stimuli 3 Stimuli 4 Stimuli 5 Stimuli 6 Preference Preference Preference Preference Preference Preference Ranking Ranking Ranking Ranking Ranking Ranking

Spearman rho Correlation Coefficient 0.198 0.140 -0.002 -0.144 0.143 0.042 Physical density Sig. (1-tailed) 0.082 0.160 0.495 0.216 0.158 0.383 n 51 52 52 50 51 52

Spearman rho Correlation Coefficient 0.082 0.160 0.093 -0.213 0.047 0.002 Population Density Sig. (1-tailed) 0.233 0.078 0.257 0.070 0.373 0.494

n 50 50 51 49 49 50 Note. Sig at p < .01

Q3a. Is there a correlation between visual stimuli preference and perceived physical density?

The first correlation test completed was between the participants’ preferred visual stimuli ranking and physical density. In this case, participants ranked density on a seven point Likert scale between “Very Spacious” and “Very Dense.”

Table 45 indicates the results of this test where in all cases p > .01 rejecting the

null hypothesis meaning there is not a statistically significant correlation between

participants’ preferred stimuli and perceived physical density.

Q3b. Is there a correlation between visual stimuli preference and perceived

population density?

A correlation test was also completed between participants’ preferred visual

stimuli ranking and estimated population density. Participants were given nine

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possible densities measured in people and/or jobs per hectare. Each density was in increments of 100 ranging from 100 people and/or jobs per hectare to 900 people and/or jobs per hectare (see Table 44). Table 45 indicates the results of this test where in all cases p > .01 rejecting the null hypothesis meaning there is not a statistically significant correlation between participants’ preferred stimuli and estimated population density.

Q3c. Is there a relationship between most preferred visual stimuli preference and perceived population or physical density?

While there is no direct correlation between physical density and neighbourhood preference, or between estimated population density and neighbourhood preference, Table 46 indicates that the general perceptions of density have some relationship with neighbourhood preference. The summary that Table 46 provides indicates that visual stimuli 3 was the most preferred stimuli, as well as the stimuli which was perceived as most spacious, least spatially dense, and second least in estimated population density. Also, visual stimuli 1 which was the least preferred stimuli overall was also generally perceived to be more dense.

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Table 46: Relationship between Preference Placement and Overall Density Perception

Visual stimuli Preferences Physical Density Population Density Preference Order Stimuli # Order Spaciousness Order Density Order Density

1 Stimuli 3 Most Least Least Spacious Stimuli 3 Dense Stimuli 3 Dense Stimuli 4 2 Stimuli 5 Stimuli 6 Stimuli 4 Stimuli 3 3 Stimuli 2 Stimuli 5 Stimuli 6 Stimuli 2 4 Stimuli 6 Stimuli 1 Stimuli 5 Stimuli 6 5 Stimuli 4 Stimuli 4 Stimuli 1 Stimuli 1

6 Stimuli 1 Least Most Most Spacious Stimuli 2 Dense Stimuli 2 Dense Stimuli 5 Note. Physical density refers to a grouping of “somewhat spacious,” “spacious” and “very spacious” and the grouping of “somewhat dense,” “dense” and “very dense” shown in Figure 50. Population density refers to the mean shown in Table 44

Summary:

In summary there is not a statistically significant correlation between

participants’ preferred visual stimuli rankings and either perceived physical

density or estimated population. This indicates that there is not a direct

relationship between an individual’s preference for a visual stimuli and their

perceptions of density. When evaluating this relationship at an aggregate scale,

(i.e. not a correlational relationship, but rather evaluating based on the number of participants indicating a density level of a stimuli in relation to the number of

people indicating it is their preference) there appears to be some level of

relationship (Table 46). As Table 46 indicates there is a relationship between the

overall perceived density of a given stimuli and the overall preferences of the

participants. Specifically, visual stimuli 3, which was most preferred overall, was

also perceived to be most spacious, least dense and was estimated to have one

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of the lowest population densities. However, the relationships among the other stimuli in Table 46 are less clear which may indicate other personal factors or other neighbourhood characteristics are involved.

6.1.4. Research Question 4: Lifestyle

Q4. Is there a relationship between the determined lifestyle clusters of participants and preferred visual stimuli?

Q4a. Which lifestyle clusters have the greatest and least preference for

each of the visual stimuli?

Q4b. Do participant lifestyle clustering factors indicate what lifestyle

considerations affect density preferences?

In determining if lifestyle is a contributing factor to residents’ preferences for neighbourhood urban forms, the lifestyle clusters determined in Chapter 5 were used. Descriptive statistics were used to identify the number of participants in each of the lifestyle clusters and which had the greatest and least preference for each of the visual stimuli. The clusters previously identified in Chapter 5 are:

Cluster A - Convenient Family, Cluster B - Space Seekers, Cluster C - Neutral,

Cluster D - Care Free Living, and Cluster E - Family Focused. For the purposes of this analysis the preference choices were grouped. The first and second preferences were grouped, the third and fourth preferences were grouped and the fifth and sixth preferences were grouped. The groupings were formed based on the findings from research question 1 in which the groupings demonstrated

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the most definitive representation of the preference levels for each of the visual stimuli. The analysis for the lifestyle considerations was also completed using ungrouped preferences, however, this complicated the results and thus the grouped approach was taken.

Q4a. Which lifestyle clusters have the greatest and least preference for each of the visual stimuli?

Lifestyle Preferences Visual Stimuli 1: Modern Density Pattern

Cluster E: Family Focused Cluster C: Neutral Cluster Cluster B: Space Seekers Cluster D: Care Free Living Cluster A: Convenient Family

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st & 2nd Preference 3rd & 4th Preference 5th & 6th Preference

Figure 55: Lifestyle Preferences Visual Stimuli 1: Modern Density Pattern

While being the least preferred visual stimuli, stimuli 1 was most preferred by the Family Focused cluster at 23%. However, 43% of the family focused cluster also ranked visual stimuli 1 as their fifth or sixth preference (Figure 55,

Table 47). A significant proportion of the participants in the Neutral cluster also ranked visual stimuli 1 in their first and second preference with 20% of the cluster. The Neutral cluster also had a large percentage of members ranking it as their third and fourth preference with 60%, and a smaller group than the Family

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Focused cluster ranking visual stimuli 1 as their fifth or sixth preference with 20%

(Figure 55, Table 47).

Table 47: Lifestyle Cluster Preferences of Visual Stimuli 1: Modern Density Pattern 1st & 2nd 3rd & 4th 5th & 6th Lifestyle Cluster Preference Preference Preference n % n % n % Cluster A: Convenient Family 0 (0) 3 (50) 3 (50) Cluster D: Care Free Living 1 (11) 4 (44) 4 (44) Cluster B: Space Seekers 1 (14) 3 (43) 3 (43) Cluster C: Neutral 1 (20) 3 (60) 1 (20) Cluster E: Family Focused 5 (23) 8 (36) 9 (41) Total 8 (16) 21 (43) 20 (41)

Note: The total percentages may not be 100 due to rounding.

Shown in Figure 55, the Convenient Family cluster has the lowest number of members preferring this neighbourhood form. No members of this cluster ranked this stimuli as their first or second preference and 50% of members ranked this stimuli as their third or fourth preference.

Lifestyle Preferences Visual Stimuli 2: Corridor Density Pattern

Cluster D: Care Free Living Cluster A: Convenient Family Cluster E: Family Focused Cluster C: Neutral Cluster Cluster B: Space Seekers

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st & 2nd Preference 3rd & 4th Preference 5th & 6th Preference

Figure 56: Lifestyle Preferences Visual Stimuli 2: Corridor Density Pattern

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As Figure 56 indicates, the Care Free Living cluster had the greatest preference for visual stimuli 2 with over 56% of the members ranking it as their first or second preference (Table 48). The Convenient Family cluster was also shown to have some preference level for visual stimuli 2 with 33% of members

selecting it as their first or second preference, and 50% of members stating it

was their third or fourth preference (Table 48).

The Space Seekers cluster had the lowest overall preference for visual

stimuli 2 with only 14% of the members ranking it as their first or second

preference. This is further supported by 57 % of this lifestyle cluster ranking

visual stimuli 2 as their fifth or sixth preference (Table 48).

Table 48: Lifestyle Cluster Preferences of Visual Stimuli 2: Corridor Density Pattern 1st & 2nd 3rd & 4th 5th & 6th Lifestyle Cluster Preference Preference Preference n % n % n % Cluster B: Space Seekers 1 (14) 2 (29) 4 (57) Cluster C: Neutral Cluster 1 (20) 3 (60) 1 (20) Cluster E: Family Focused 6 (27) 9 (41) 7 (32) Cluster A: Convenient Family 2 (33) 3 (50) 1 (17) Cluster D: Care Free Living 5 (56) 1 (11) 3 (33) Total 15 (31) 18 (37) 16 (33)

Note: The total percentages may not be 100 due to rounding.

As previously indicated, visual stimuli 3 is the overall most preferred visual

stimuli, which is further indicated in the lifestyle results. There is a significant

proportion of all five of the lifestyle clusters stating that visual stimuli 3 is their first or second preference. The Convenient Family cluster has the greatest

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preference overall with 67% of members ranking visual stimuli 3 as their first or

second preference (Table 49). Both the Care Free Living and the Family

Focused clusters had 50% of the members ranking visual stimuli 3 as their first or

second preference (Table 49). While the Neutral cluster and the Single Family

cluster had less than 50% of members ranking it as their first or second preference, neither had any members rank visual stimuli 3 as their fifth or sixth preference. While not a strong preference, this supports the notion that both the

Neutral cluster and the Single Family cluster have a level of preference for visual

stimuli 3.

Lifestyle Preferences Visual Stimuli 3: Nodal / Urban Village Density Pattern

Cluster A: Convenient Family Cluster D: Care Free Living Cluster E: Family Focused Cluster C: Neutral Cluster Cluster B: Space Seekers

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st & 2nd Preference 3rd & 4th Preference 5th & 6th Preference

Figure 57: Lifestyle Preferences Visual Stimuli 3: Nodal / Urban Village Density Pattern

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Table 49: Lifestyle Cluster Preferences of Visual Stimuli 3: Nodal / Urban Village Density Pattern 1st & 2nd 3rd & 4th 5th & 6th Lifestyle Cluster Preference Preference Preference n % n % n % Cluster B: Space Seekers 2 (29) 5 (71) 0 (0) Cluster C: Neutral 2 (40) 3 (60) 0 (0) Cluster E: Family Focused 11 (50) 7 (32) 4 (18) Cluster D: Care Free Living 5 (50) 5 (50) 0 (0) Cluster A: Convenient Family 4 (67) 1 (17) 1 (17) Total 24 (48) 21 (42) 5 (10)

Note: The total percentages may not be 100 due to rounding.

Shown in Figure 57, only two of the lifestyle clusters had any members rank visual stimuli 3 in their fifth or sixth preference; these were the Convenient Family cluster (17%) and the Family Focused cluster (18%). However, both these lifestyle clusters also have strong rankings for the first and second preferences for visual stimuli 3. The Space Seekers cluster has the largest percentage of members ranking it as their third or fourth preference with 71%. This arguably indicates visual stimuli 3 is least preferred by the Space Seekers lifestyle cluster.

Lifestyle Preferences Visual Stimuli 4: Evenly Distributed / Neotraditional Density Pattern

Cluster C: Neutral Cluster

Cluster A: Convenient Family

Cluster B: Space Seekers

Cluster E: Family Focused

Cluster D: Care Free Living

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st & 2nd Preference 3rd & 4th Preference 5th & 6th Preference

Figure 58: Lifestyle Preferences Visual Stimuli 4: Evenly Distributed / Neotraditional Density Pattern

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Table 50: Lifestyle Cluster Preferences of Visual Stimuli 4: Evenly Distributed / Neotraditional Density Pattern 1st & 2nd 3rd & 4th 5th & 6th Lifestyle Cluster Preference Preference Preference n % n % n % Cluster D: Care Free Living 1 (11) 4 (44) 4 (44) Cluster E: Family Focused 5 (23) 12 (55) 5 (23) Cluster B: Space Seekers 2 (29) 2 (29) 3 (43) Cluster A: Convenient Family 2 (33) 1 (17) 3 (50) Cluster C: Neutral 4 (80) 1 (20) 0 (0) Total 14 (11) 20 (44) 15 (44)

Note: The total percentages may not be 100 due to rounding.

The results for visual stimuli 4 indicate a strong preference by one of the lifestyle clusters and then a dichotomy amongst the four other clusters, depicted in Figure 58. The Neutral cluster had 80% of its membership rank visual stimuli 4 as their first or second preference (Table 50). The other lifestyle clusters have almost equal numbers of members ranking visual stimuli 4 in first and second preference as there are ranking in the fifth and sixth preference. The results show the Convenient Family cluster with 33% and 50% of members respectively, the Single Family cluster with 29% and 43% of members respectively and the

Family Focused cluster with 23% and 23% of members respectively (Table 50).

The Care Free Living cluster has the clearest lower preference ranking for visual stimuli 4 with only 11% of cluster members ranking it as their first or second

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reference and 44% of members ranking it as their fifth or sixth preference.

Lifestyle Preferences Visual Stimuli 5: Evolutionary Density Pattern

Cluster B: Space Seekers

Cluster A: Convenient Family

Cluster E: Family Focused

Cluster C: Neutral Cluster

Cluster D: Care Free Living

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st & 2nd Preference 3rd & 4th Preference 5th & 6th Preference

Figure 59 Lifestyle Preferences Visual Stimuli 5: Evolutionary Density Pattern

As shown in Figure 59, there are two lifestyle clusters that have ranked visual stimuli 5 as their first or second preference. The first, the Space Seekers cluster, had a significant proportion of members ranking visual stimuli 5 as their first or second preference at 71%. The second, the Convenient Family cluster had 50% of members rank visual stimuli 5 as their first or second preference.

Visual stimuli 5 was previously shown to have a strong difference of opinions with a significant number of participants ranking it as their top preference, as well as a significant proportion ranking it as their bottom preference. Two lifestyle clusters had a significant percentage of participants rank visual stimuli 5 in their fifth and sixth preference. The Neutral cluster had

80% of membership ranking visual stimuli 5 as their fifth or sixth preference. The

Care Free Living cluster had 56% of members ranking visual stimuli 5 as their

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fifth or sixth preference, and it also had the lowest percentage of members rank it

as their first or second preference with 11% (Table 51).

Table 51: Lifestyle Cluster Preferences of Visual Stimuli 5: Evolutionary Density Pattern 1st & 2nd 3rd & 4th 5th & 6th Lifestyle Cluster Preference Preference Preference n % n % n % Cluster D: Care Free Living 1 (11) 3 (33) 5 (56) Cluster C: Neutral 1 (20) 0 (0) 4 (80) Cluster E: Family Focused 9 (41) 6 (27) 7 (32) Cluster A: Convenient Family 3 (50) 2 (33) 1 (17) Cluster B: Space Seekers 5 (71) 2 (29) 0 (0) Total 19 (39) 13 (27) 17 (35)

Note: The total percentages may not be 100 due to rounding.

Similar to visual stimuli 5, visual stimuli 6 had a significant range of

preferences. Overall, the Care Free Living cluster had the greatest level of

preference with 67% of cluster members ranking visual stimuli 6 as their first or second preference. As indicated in Figure 60, there are no other lifestyle clusters

that had an overall positive ranking of visual stimuli 6. The Neutral lifestyle and

the Convenient Family lifestyle clusters have the lowest overall preferences for visual stimuli 6. The Neutral cluster had 80% of cluster members rank visual stimuli 6 in their fifth or sixth preference. The Convenient Family cluster had 50% of cluster membership rank visual stimuli 6 as their fifth or sixth preference. The

Convenient Family cluster had the smallest percentage of membership ranking visual stimuli 6 as their first or second preference at 17%. The Convenient Family cluster has the lowest overall preference for visual stimuli 6.

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Lifestyle Preferences Visual Stimuli 6: Transit‐Oriented Development Density Pattern

Cluster D: Care Free Living

Cluster B: Space Seekers

Cluster E: Family Focused Cluster C: Neutral Cluster

Cluster A: Convenient Family

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1st & 2nd Preference 3rd & 4th Preference 5th & 6th Preference

Figure 60: Lifestyle Preferences Visual Stimuli 6: Transit-Oriented Development Density Pattern

Table 52: Lifestyle Cluster Preferences of Visual Stimuli 6 1st & 2nd 3rd & 4th 5th & 6th Lifestyle Cluster Preference Preference Preference n % n % n % Cluster A: Convenient Family 1 (17) 2 (33) 3 (50) Cluster C: Neutral 1 (20) 0 (0) 4 (80) Cluster E: Family Focused 8 (36) 3 (14) 11 (50) Cluster B: Space Seekers 3 (43) 0 (0) 4 (57) Cluster D: Care Free Living 6 (67) 1 (11) 2 (22) Total 19 (17) 6 (33) 24 (50)

Note: The total percentages may not be 100 due to rounding.

Q4b. Do the lifestyle clustering factors indicate what lifestyle considerations affect participants’ preferences?

The lifestyle clusters were developed and described using a factor analysis of a series of AOI statements (Chapter 5). By revisiting the one way analysis of variance between the factors and clusters (Table 38), and the cross tabulation analysis used to describe each cluster, the specific lifestyle

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considerations can be extracted. In general, there were three areas of focus that

came out of the analysis in section “5.2.3. Profiling Lifestyle Clusters.” These

were convenience, family needs, and single family amenities. Table 53 provides

a summary of which lifestyle clusters had strong alignment with each of the

lifestyle considerations.

Table 53: Lifestyle Cluster by Lifestyle Consideration Consideration Included Lifestyle Clusters Convenience consideration Cluster A: Convenient Family Cluster D: Care Free Living Family needs consideration Cluster A: Convenient Family Cluster E: Family Focused Single family amenities consideration Cluster B: Space Seekers Note: Cluster A was the only cluster that had strong alignment on two considerations

Convenience was shown to be a strong consideration for two of the

lifestyle clusters, Cluster A, Convenient Family, and Cluster D, Care-Free Living.

Table 54 provides a graphical summary of how the members of these two

clusters ranked their preferences for each of the visual stimuli. In the first three

stimuli there is clear alignment between these two lifestyle clusters. Visual stimuli

1 (modern density pattern) was not considered to contribute to convenience,

while visual stimuli 2 (corridor density pattern) and visual stimuli 3 (nodal / urban

village density pattern) were shown to contribute to convenience. The other three visual stimuli are where these two lifestyle clusters diverged. This could be the result of Convenient Family, also having a strong family needs consideration alignment. Care Free Living was determined to be mainly focused on convenience and would be a good indicator of convenience. In this case, visual

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stimuli 4 (evenly distributed / neotraditional density pattern) and visual stimuli 5

(evolutionary density pattern) would be assumed to inhibit convenience, while visual stimuli 6 (transit-oriented development density pattern) would strongly support convenience (Table 54).

Table 54: Lifestyle Considerations Analysis: Convenience Consideration Visual Stimuli Most Preferred Least Preferred Visual Stimuli 1 Cluster E Cluster B Cluster D Cluster A Cluster C Family Space Care-Free Convenient Neutral Traditional Density Pattern Focused Seeker Living Family Visual Stimuli 2 Cluster D Cluster A Cluster E Cluster C Cluster B Care-Free Convenient Family Neutral Space Seeker Corridor Density Pattern Living Family Focused

Visual Stimuli 3 Cluster A Cluster D Cluster E Cluster C Cluster B Convenient Care-Free Family Nodal / Urban Village Density Neutral Space Seeker Pattern Family Living Focused

Visual Stimuli 4 Cluster A Cluster B Cluster E Cluster D Cluster C Convenient Space Family Care-Free Evenly distributed / Neotraditional Neutral Density Pattern Family Seeker Focused Living Visual Stimuli 5 Evolutionary Cluster A Cluster E Cluster D Cluster B Cluster C Density Pattern Convenient Family Care-Free Space Seeker Neutral Family Focused Living

Visual Stimuli 6 Cluster D Cluster B Cluster E Cluster A Cluster C Care-Free Space Family Convenient Transit-Oriented Development Neutral Density Pattern Living Seeker Focused Family

The second lifestyle consideration was the family needs consideration.

Two lifestyle clusters strongly aligned with this lifestyle consideration – Cluster A, the Convenient Family cluster and Cluster E, the Family Focused cluster. In this case, the convenience consideration that affects Convenient Family plays more of a role. Visual stimuli 1 (traditional density pattern) is where there is the starkest difference. Convenient Family members least preferred this stimuli due to convenience, while Family Focused members most preferred this stimuli. This

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could partially be due to visual stimuli 1 (traditional density pattern) having the most segregated density with higher density buildings further away from amenities such as the school and the parks. For all other visual stimuli, Family

Focused falls in the middle with cluster members neither strongly preferring nor not preferring these stimuli. Convenient Family, while also having the convenience consideration, had stronger preferences for visual stimuli 4 (evenly distributed / neotraditional density pattern) and 5 (evolutionary density pattern), which is also a divergence from Care-Free Living which was shown to least prefer these two visual stimuli. This would suggest that visual stimuli 4 and 5 may be more effective in supporting family needs. Visual stimuli 6 (transit-oriented development density pattern) was most preferred by Care-Free Living, least preferred by Convenient Family and was in the middle ground for Family

Focused. This supports the notion that stimuli 6 (transit-oriented development density pattern) was perceived to be less likely to support family needs (Table

55).

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Table 55: Lifestyle Considerations Analysis: Family Needs Consideration Visual Stimuli Most Preferred Least Preferred Visual Stimuli 1 Cluster E Cluster B Cluster D Cluster A Cluster C Family Space Care-Free Convenient Neutral Traditional Density Pattern Focused Seeker Living Family Visual Stimuli 2 Cluster D Cluster A Cluster E Cluster C Cluster B Care-Free Convenient Family Neutral Space Seeker Corridor Density Pattern Living Family Focused

Visual Stimuli 3 Cluster A Cluster D Cluster E Cluster C Cluster B Convenient Care-Free Family Nodal / Urban Village Density Neutral Space Seeker Pattern Family Living Focused

Visual Stimuli 6 Cluster D Cluster B Cluster E Cluster A Cluster C Care-Free Space Family Convenient Transit-Oriented Development Neutral Density Pattern Living Seeker Focused Family

The single family amenities consideration only aligned to one of the lifestyle clusters, Cluster B, the Space Seeker cluster. In this case, the cluster had the greatest preferences for visual stimuli 5 (evolutionary density pattern) and 6 (transit-oriented development density pattern), while having the lowest preference for visual stimuli 2 (corridor density pattern) and 3 (nodal / urban village density pattern) (Table 56). This result is of interest since visual stimuli 5 has the lowest number of single family homes of all the stimuli, however the animation path for this stimuli, as well as visual stimuli 6 (transit-oriented development density pattern), would have the most significant number of single family, semi-detached and town homes represented. In contrast, visual stimuli 2

(corridor density pattern) and 3 (nodal / urban village density pattern) have the third and fourth most single family homes, but would have the least number of

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single family, semi-detached and town homes represented along the animation path. Visual stimuli 1 (modern density pattern) had the most available single family, semi-detached and town home dwellings but also had a significant number of taller buildings along the animation path.

Table 56: Lifestyle Considerations Analysis: Single Family Amenities Consideration Visual Stimuli Most Preferred Least Preferred Visual Stimuli 1 Cluster E Cluster B Cluster D Cluster A Cluster C Family Space Care-Free Convenient Neutral Traditional Density Pattern Focused Seeker Living Family Visual Stimuli 2 Cluster D Cluster A Cluster E Cluster C Cluster B Care-Free Convenient Family Neutral Space Seeker Corridor Density Pattern Living Family Focused

Visual Stimuli 3 Cluster A Cluster D Cluster E Cluster C Cluster B Convenient Care-Free Family Nodal / Urban Village Density Neutral Space Seeker Pattern Family Living Focused

Visual Stimuli 6 Cluster D Cluster B Cluster E Cluster A Cluster C Care-Free Space Family Convenient Transit-Oriented Development Neutral Density Pattern Living Seeker Focused Family

Statistical Significance of Lifestyle and Visual Stimuli Preferences

In order to determine the statistical significance of the results a factorial repeated measure ANOVA, also called a mixed design or split plot ANOVA, was used to determine the statistical significance of the lifestyle indicator. The interaction between lifestyle clusters and preferred urban form failed to reach statistical significance F(20,220) = 1.337, p =.158.

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Summary

In summary, while the results did not reach statistical significance the results show that lifestyle can be used to determine differences among participant groups with regards to how neighbourhood form is perceived. In this study it was shown that there are some differences between each of the lifestyle clusters.

6.2. Discussion: Preferred Form, Density, and the Lifestyle factor

This section will provide a discussion of the findings in relation to preferred form and the relationship with both density and lifestyle factor. For the purposes of this discussion, Table 57 was created to summarize the overall differences between the six visual stimuli and density types. It indicates some descriptive differences among the stimuli and provides a street view image of each stimuli.

Table 57: Summary of Discussion Factors Visual Stimuli Number and Name Descriptive Factors Street View Photo

 High number of blocked views  High level of enclosure along corridors  Significantly tall buildings up to 28 storeys Visual Stimuli 1: Modern  Fewer single family homes and semi-detached homes density pattern along the animation path  Tall buildings clustered together

 High level of enclosure  Mostly mid-rise buildings maximum height of 12 storeys  Few views of single family homes along animation path Visual Stimuli 2: Corridor  Little variation in buildings heights density pattern

 Enclosure along the major corridors near the major intersection at the middle  More openness along the major corridors at the outer Visual Stimuli 3: Nodal or edges urban village density pattern  Greater variation and complexity in building height throughout the stimuli  Few blocked views

Note: Continued on the following page

Table 57: Summary of Discussion Factors Continued Visual Stimuli Number and Name Descriptive Factors Street View Photo

 Building heights are generally uniform across the entire stimuli mostly 4 storeys with some 6 storeys Visual Stimuli 4:  Few blocked view Neotraditional or evenly distributed density pattern  Only multi-family buildings along the main corridors

 High levels of variation and complexity amongst building heights and types  Both multi-family and single family along the major Visual Stimuli 5: Evolutionary density pattern corridors  High degree of openness  Some clustering of tall buildings

 Dramatic change between single family homes and tall buildings

Visual Stimuli 6: Transit-  Clustering of taller buildings oriented development  Some blocked views density pattern

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6.2.1. Discussion of Perceived Density

One of the early hypotheses of this research was that neighbourhood form affects how an individual perceives density in neighbourhood developments.

Specifically, the hypothesis was that buildings that are shorter in height and more evenly distributed throughout a neighbourhood would be perceived as less dense, while tall buildings clustered together would be perceived as more dense.

The statistically significant finding that visual stimuli 5 was perceived to be the densest, in terms of both built form and population density, while visual stimuli 3

(nodal / urban village density pattern) was perceived to have the least dense built form and visual stimuli 4 (evenly distributed / neo traditional density pattern) was perceived to have lowest population density, confirms that urban form does affect how density is perceived. In this case, height and distribution are the contributing factors. Visual stimuli 5 (evolutionary density pattern) has 39 mid-rise buildings and 14 high-rise buildings and many of the high rise buildings are in the 15 to 28 storey range. These buildings were distributed throughout the stimuli, however, there was significant clustering in some areas which resulted in pockets of high density buildings. In contrast, visual stimuli 3 (nodal / urban village density pattern), which was perceived as having the lowest physical density has seven

high rise buildings all of which were under 15 storeys. Visual stimuli 4 (evenly

distributed / neo traditional density pattern), perceived to have the lowest population density, had no high-rise buildings and very few mid-rise buildings but had 80 low rise buildings – multi-family buildings under four storeys. These

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findings clearly support the notion that taller buildings are perceived to be more

dense both spatially as well as in terms of population density.

Further supporting this finding, Table 46 on page 195 summarizes that the

three visual stimuli, 1 (modern density pattern), 5 (evolutionary density pattern),

and 6 (transit-oriented development density pattern), with the highest amount of

tall buildings, and the greatest amount of clustering, were estimated to have the

highest population density. In contrast, the three visual stimuli with the most

evenly distributed higher density buildings were estimated to have a lower

population density. Overall this confirms that there is a relationship between

urban form and perceived density and confirms the initial hypothesis that evenly

distributed density buildings would be perceived as less dense.

6.2.2. Discussion of Preferred Urban Form

The statistically significant finding that visual stimuli 3 (nodal / urban village

density pattern) and 5 (evolutionary density pattern) were most preferred and

visual stimuli 1 (modern density pattern) was least preferred by participants proved to be an interesting result. The early hypothesis of the research was that the most preferred neighbourhood form would be the one where there are few

high rise buildings and where the higher density buildings are evenly distributed.

This was in relation to the hypothesis that taller buildings would be perceived as

denser than shorter buildings. In this case, visual stimuli 4, the neotraditional or

evenly distributed density pattern, would have been the most preferred visual

stimuli. The finding that visual stimuli 3, the nodal or urban village density pattern,

216 and visual stimuli 5, the evolutionary density pattern, are the most preferred neighbourhood forms contradicts this hypothesis. In addition, the finding that visual stimuli 4 was the second least preferred visual stimuli further disproved this hypothesis. That being said, the findings do suggest that urban form does inform preference, however, in a way that was unexpected.

In consideration of the common elements of both visual stimuli 3 (nodal / urban village density pattern) and visual stimuli 5 (evolutionary density pattern), the findings suggest alignment with previous findings in literature. As noted in

Table 57, visual stimuli 3 and 5 have the greatest degree of variation and complexity in both height and overall form. These forms also had less clustering of tall buildings, with taller buildings spaced throughout the represented neighbourhood. The greater amount of variation and complexity in the skyline and heights of buildings is consistent with a theory by Berlyne (1971) that suggests that as form complexity is increased so too does the preference levels.

It is further supported by a study from S. Kaplan et al. (1972) which showed a positive linear relationship between complexity and preferences. Both of which have been reconfirmed by Heath et al. (2000) in investigating the complexity of urban silhouettes. The perceived complexity of these stimuli is further supported by the comments provided by participants in which the majority of the positive comments for visual stimuli 3 and 5 were in regards to the diversity in housing types and the variety of building design. Overall, this relationship indicates that in developing and representing higher density neighbourhoods, providing a greater

217 variety of building heights will be more effective in achieving a greater degree of preference.

In addition, previous research has also shown that there is also an inverted

U-shaped relationship between preference and degree of complexity (Akalin et al., 2009; Imamoglu, 2000). While this relationship is not represented in this research, it suggests that while increasing complexity and variation will increase preference of urban environments, too much complexity will make an environment unintelligible and unappealing. Research shows that there is balance between the novelty of an environment and the cohesiveness of the greater whole.

The literature indicates other factors, such as openness, naturalness, enclosure and blocked views, also contribute to the degree to which an environment is preferred or not preferred. These other factors further explain the greater preference levels for visual stimuli 3 and 5 and explain why visual stimuli

1 is least preferred and visual stimuli 2 (corridor density pattern) and 4 (evenly distributed / neotraditional density pattern) were less preferred.

Previous studies have shown that perceived openness and blocked versus unblocked views are contributing factors in environmental preference. In this study, visual stimuli 3 and 5 both had a greater degree of openness than most of the other stimuli, specifically along the animation path. This provided greater views of both green public spaces, as well views of the lower density areas of these particular neighbourhoods. The finding that these two stimuli are preferred

218 more than the other visual stimuli suggests that these factors also were contributing to participants’ preferences. This is supported by studies, such as

Hur et al. (2010) and Kearney (2006), which have shown that the perceived openness and naturalness of an urban environment is more important to neighbourhood satisfaction than perceived density, with perceived openness being most important overall. In contrast, visual stimuli 1, the modern density pattern, had much taller buildings along the major corridors where the animation path was, as well as clusters of tall buildings that created blocked views along that same path. As a result visual stimuli 1 (modern density pattern) created less opportunity for participants to have views to the more open single family and park areas. Supported by Stamps (2005) and Lindal and Hartig (2013) which have shown that while some enclosure is generally good, too much enclosure, and specifically blocking views at the end of a street, can significantly reduce preference levels. This same consideration may also explain why visual stimuli 2, the corridor density pattern, and visual stimuli 4, the neotraditional or evenly distributed density pattern, were less preferred. In both case these stimuli had continuous facades of multi-family buildings along the main corridors creating a sense of enclosure and minimal views into more open areas of neighbourhoods.

This discussion of variation, complexity, and blocked view may also explain the difference in preferences for visual stimuli 3 (nodal / urban village density pattern) and visual stimuli 6 (transit-oriented development density pattern). These two density patterns had the greatest similarity in overall pattern and form. Both

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have a central focal point and both tend to have some of the taller buildings in the

central area. The key differences were that visual stimuli 6 had taller buildings

clustered in the middle with less of transition into the single family areas, while

visual stimuli 3 had a more gradual transition into the single family areas and had

more variation in building heights. Visual stimuli 6 had a significant number of people rank it in their higher preferences as well as a significant number of people rank it in their lowest preferences. While it had some variation, visual stimuli 6 had a significant amount of clustering which increased the sense of enclosure and created blocked views from a number of angle. In contract visual stimuli 3 as already stated, had a greater sense of openness and had an overall greater amount of variation in the build form. These findings confirm that neighbourhood urban form plays a significant role in participant preferences and thereby resident preferences for neighbourhoods of higher population and spatial densities.

6.2.3. Discussion of the Relationship between Preferred Urban Form and

Perceived Density

The finding that there is no statistically significant correlation between how dense participants perceived each visual stimuli to be and their preferences indicates that density is not the determining factor in preference. This disproves an earlier hypothesis that stated perceived density would be a determining factor in neighbourhood form preferences and presents an interesting finding. Rather, this research suggests that the neighbourhood form, and the characteristics that

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each form represents, has more of a role in preference than density. However,

the indication that the stimuli perceived as least spatially dense and estimated to

have one of the lowest population densities was most preferred, does suggest

that a form that appears as less dense would be preferable to more people.

Table 46 on page 195 shows the relationship between preferences for visual

stimuli 3 and how dense it was perceived to be. In general, it could be stated that

an urban form that is preferred by more people and is perceived to be less dense

would be more acceptable in an urban environment.

6.2.4. Discussion of Lifestyles and Preferred Urban Forms

While the results of lifestyle as a predictor to urban form preferences did not

reach statistical significance, the findings are an indicator of the inherent

characteristics of each neighbourhood urban form. The inability to reach

statistical significance has been attributed to the small sample size in regards to

the different lifestyle clusters not so much the differences in the preferences for

the six different visual stimuli. The results also begin to indicate what a statistically significant sample might indicate.

The finding that certain lifestyle clusters prefer different urban forms and

density patterns confirm the hypothesis that lifestyle is an indicator of urban form

preferences. As discussed in Chapter 2, the literature review, the concept of

lifestyle is not new to housing studies, yet this is the first time that this concept

has been applied to environmental aesthetics in relation to urban form. This

finding is illuminating as other studies that have shown that lifestyle differences

221 indicate differences in criteria and preferences for housings location which inherently leads to form (Walker & Li, 2007). The difference in the findings of this research is when density is held constant there are differences among lifestyle groupings in regards to what urban form they prefer.

In this study there were three factors that contributed to the lifestyles that participants were attributed. These were: balancing the needs of the family, convenience, and amenities within the home. These factors led to the identification of five different lifestyle clusters, Cluster A - Convenient Family,

Cluster B - Space Seekers, Cluster C - Neutral Cluster, Cluster D - Care Free

Living, and Cluster E - Family Focused. In comparing these lifestyle clusters to the neighbourhood density pattern – urban form stimuli, it was identified that there is a clear relationship between an individual’s lifestyle indicators and preferences.

For example, Cluster B - Space Seekers, was shown to be mostly interested in the amenities associated with single family living. As shown in Figure 43 and

Figure 44 on pages 164 and 165, this cluster had a greater preference for driving to amenities, having more and larger single family homes, less multi-family homes and placed a much higher importance on driving in general than any other cluster. This lifestyle group showed the highest preference for visual stimuli 5, the evolutionary density pattern, and a higher preference for visual stimuli 6, the transit oriented development pattern. In both of these stimuli there was greater amount of single family and low-rise buildings shown along the animation path

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which may have been associated with more single family housing and a

segregation of the multi-family housing.

Cluster D - Care Free Living, which was primarily focused on convenience,

had a greater preference for walking to amenities, greater diversity, more active streets and parks, preferred smaller homes overall and put more emphasis on walking and transit as their main mode of transportation. This cluster showed a greater preference for the visual stimuli where there was a clear representation of apartment style units with amenities that would be closer together. In this case,

Cluster D - Care Free Living had the highest overall preferences for visual stimuli

2, corridor density, stimuli 6, transit-oriented development, and a higher preference for visual stimuli 3, urban village or nodal density. These three visual stimuli tended to have significantly more mixed use buildings with a lot of street facing retail. The form of these stimuli suggests that the public realm would be fairly active and walkable as this is often the case with street front retail and having higher density within close proximity.

Cluster E - Family Focused, had many similarities to the Space Seekers however there was more of an emphasis on walking to amenities that would be beneficial to children and there was a less extreme emphasis on driving as the main mode of transportation. This cluster has a much greater preference for visual stimuli 1 (modern density pattern) than any other stimuli; in most cases this group was more neutral towards the other visual stimuli. Visual stimuli 1 of all the stimuli actually had the most single family homes, the higher density buildings

223 were clustered but largely off to the side and the main amenities were shown to be mostly accessible by car with some walkability. The interesting part of this finding is this density pattern has been the most common pattern implemented over the last several decades in the development of suburban communities. The finding suggests that developers building suburban communities targeted at families are building communities that people with Family Focused lifestyles prefer.

Cluster A presents the most interesting cluster and the most interesting findings. Cluster A was the Convenient Family cluster and was described as being both convenience focused and family focused. As shown in Figure 43 and

Figure 44 on pages 164 and 165, this cluster preferred smaller homes with all types of amenities within walking distance. There was a very strong emphasis on active communities and streets, and on commuting by walking. This cluster had the highest preference for visual stimuli 3, the nodal or urban village density pattern which was also the most preferred visual stimuli by all participants.

Cluster A - Convenient Family, also had high preference levels for visual stimuli 2

(corridor density pattern), 4 (evenly distributed / neo traditional density pattern) and 5 (evolutionary density pattern) but had extremely low preference levels for visual stimuli 1 (modern density pattern) and 6 (transit oriented development density pattern). These findings would suggest that visual stimuli 2 (corridor density pattern), 3 (nodal / urban village density pattern), 4 (evenly distributed / neotraditional density pattern, and 5 (evolutionary density pattern) would, to

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some extent, be effective in supporting residents with both a family focused

lifestyle as well as those with a convenience focused lifestyle.

Cluster C was called the Neutral cluster as there was little information that

described what this particular group was passionate about. This cluster did not

have high preference levels for most of the visual stimuli with the exception of

visual stimuli 4 (evenly distributed / neotraditional density pattern, which this

lifestyle cluster had the greatest overall preference for. This finding is somewhat

difficult to explain as Neutral Cluster seemingly did not have a particular focus on

any issues or preferences for their neighbourhoods or living environments.

These findings show that there are differences in preferences for

neighbourhood forms based on individual lifestyles. These lifestyles

characteristics also arguably indicate more about the preferred forms than spatial

analysis can convey. Presumably, the visual stimuli that are preferred by each

lifestyle would have many of the characteristics that the members of lifestyle

clusters are looking for in selecting a neighbourhood. Based on this, the findings

show that visual stimuli 2, the corridor density pattern, and visual stimuli 3, the

nodal or urban village pattern, are effective in supporting both families and

convenience. In contrast, visual stimuli 5 (evolutionary density pattern) is

effective in supporting Space Seekers and family focused people, while visual

stimuli 6 (transit-oriented development density pattern) is effective in supporting convenience focused people and people that prefer to live in single family home neighbourhoods. Finally, visual stimuli 1 (modern density pattern) would be

225 effective in supporting family focused people. Figure 61 is a diagram that visually depicts the relationship between the lifestyle considerations and each of the visual stimuli.

Single Family Convenience Amenities 6

3 5 Family + 4 Convenience 1

Indication of where each 1 visual stimuli falls within Family Needs supporting the lifestyle considerations

Figure 61: Lifestyle Considerations Visual Stimuli Relationship Diagram

That being said, the planning and development of cities cannot be a process where there can be customizations based on individual lifestyle. What this information informs is an understanding of the expectations of specific lifestyle groups that may be targeted. It would be expected that the Space Seeker

226 lifestyle is really looking for a single family home and this will likely not change.

As this group had the greatest preference for visual stimuli 5 (evolutionary density pattern), this would explain why this stimuli was more highly preferred overall. This could be attributed to the number of single family homes that are visible along the animations path. In contrast, it is the other lifestyle clusters that would be more interested in living near and in higher density developments.

When looking at these lifestyle clusters there are two urban forms that are identified as being more preferred – visual stimuli 2 (corridor density pattern) and

3 (nodal / urban village density pattern). This finding is not surprising as these stimuli were the first and third most preferred overall. This also suggests that these would be the most effective forms for lifestyles interested in living in and around higher density developments.

Unfortunately the limitation of these findings is the number of participants in the study limits the proportion of participants that are members of each lifestyle cluster. As a result and as stated above the results failed to reach statistical significance. Future research could generate much more in-depth findings in regards to lifestyle by obtaining a much larger sample size. The ideal situation would be to collect a sufficient amount of data to develop a regression stimuli that could accurately predict what neighbourhood form would be preferred based on lifestyle characteristics.

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6.1. Conclusions

It was determined that some urban forms are preferred more than others

and increasing the amount of variation, complexity and openness are the key contributing factors to increasing participant preferences. Further, urban form affects how individuals perceive density as areas of the same measured population density, but of different urban forms, can be perceived differently. It was also determined that there is not a direct relationship between perceived density and preference, indicating there are other characteristics that contribute to preference. Finally, there is a difference in what neighbourhood forms are preferred between participants with different lifestyle characteristics. This indicates that residents with different lifestyles would have differing preferences of urban forms.

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7. Results: Characteristics of Preferred Urban Form

This chapter expands on the results and discussion presented in Chapter 6.

It will expand on how participants perceived key characteristics about each of the visual stimuli and density patterns. The statistical analysis used here is described in greater detail in section 4.10. Section 7.1 provides the results of the perceived neighbourhood characteristics and section 7.2 discusses the implications of the results. The final section concludes the chapter.

7.1. Results: Preferred Urban Form Characteristics

7.1.1. Research Question 5: Perceived Neighbourhood Characteristics

Q5. Do the perceived characteristics of the preferred visual stimuli indicate why one stimuli is preferred over others?

Q5a. Which characteristics were perceived to be most diverse across the

six visual stimuli?

Q5b. Which characteristics were perceived to be most similar across the

six visual stimuli?

Q5c. How were the neighbourhood characteristics perceived in relation to

the most preferred visual stimuli?

Q5d. How were the neighbourhood characteristics perceived in relation to

the least preferred visual stimuli?

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A five point semantic differential scale was used to evaluate participants’ perceptions of specific characteristics in regards to each of the visual stimuli.

Figure 62 provides a summary of the neighbourhood characteristic findings.

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Semantic Differential Chart of Perceived Neighbourhood Characteristics

Traveling Distance to Grocery Stores and Shopping Traveling Distance to Grocery Stores and ShopingWalking Driving/ Transit Traveling distance to commercial amenities Traveling distance to commercial amenities such as cafes, such as cafes, restaurants, boutiques, fitness Walking Driving/ centres etc.restaurants, boutiques, fitness centres etc. Transit Traveling distance community amenities such Traveling distance community amenities such as schools, as schools, daycares, community centres Walking Driving/ and physiciansdaycares, community centres and physicians Transit The visual appearance of the neighbourhood Visually Uniform in The visual appearance of the neighbourhood is is Stimulating Appearance

More of a This neighbourhood’s mix of housing types is Less of a This neighbourhood’s mix of housing typesMix is Mix

The amount of single family homes in this Few Single Mostly Single The amount of single family homes in this neighbourhood is neighbourhood is Family Family

The amount of multi-family homes in this Few Multi- Mostly Multi- neighbourhoodThe amount ofis multifamily homes in this neighbourhoodfamily is family

The average size of the homes in this Small Large neighbourhoodThe average are size of the homes in this neighbourhood(600sqft) are (2500sqft)

TheThe size size of of the the outdoor outdoor space or or yards yards in this neighbourhoodSmall Large (sports, in this neighbourhood are are (Balcony/terrace) activities)

The lot size of the homes in this Small (Minimal Large The lot size of the homes in this neighbourhood are neighbourhood are yard space) (Backyard)

Active with The parks in this neighbourhoodThe parks are in this neighbourhoodQuiet areand Secluded lots of people

The public spaces and sidewalks in this Quiet and Active with The public spaces and sidewalks in this neighbourhood are neighbourhood are uncrowded lots of people

The noise level in thisThe neighbourhood noise level in thisis neighbourhoodLow noise is High noise

The visual privacy in the neighbourhood is High degree of The visual privacy in the neighbourhoodLow degree is of visual privacy visual privacy

The amount traffic in this neighbourhood is The amount traffic in this neighbourhoodLow Traffic is High Traffic

The sense of safety that this neighbourhood A low degree A high degree The sense of safety that this neighbourhood provides is provides is of safety of safety

The diversitydiversity of of residents residents in inthis this neighbourhood wouldLow diversitybe (age, High diversity

neighbourhood would be (age, income, income, ethnic, etc.) of residents of residents ethnic, etc.) People of similar Wide range of The life stages of the residents of this neighbourhood are The life stages of the residents of this life stages life stages neighbourhood are Mostly street The parkingThe parking for private for private residents residents in this in this neighbourhood is Private spaces neighbourhood is parking off the street

Off the street The visitor parking Thein this visitor neighbourhood parking in this is neighbourhood Mostly street is

parking In this neighbourhood how would the majority In this neighbourhood how would the majority of residentsOutdoor Indoor access of residents access their vehicles access their vehicles access

The commute from this neighbourhood Long and Short and The commute from this neighbourhood would likely be would likely be inconvenient convenient

The transportation option in this Many modes of The transportation option in this neighbourhoodFew wouldmodes be of

neighbourhood would be transportation transportation

12345 Values ModelStimuli 1 ModelStimuli 22 ModelStimuli 33 ModelStimuli 4 Model Stimuli 5 Model Stimuli 66

Figure 62: Semantic Differential Chart of Perceived Neighbourhood Characteristics

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Q5a. Which characteristics were perceived to be most similar across the six

visual stimuli?

Figure 62 indicates that there are a number of neighbourhood

characteristics which were perceived to be similar across all six of the visual

stimuli. For example, all six stimuli ranked in the middle range between driving

and walking for access to grocery stores and shopping, as well as access to

community amenities such as schools, daycares, community centres, and

medical support. The parks in all six stimuli were generally perceived to be active with lots of people in them. Also, in terms of parking, all stimuli were perceived as having the majority of visitor parking on the street with residents’ access to parking in the middle range between outdoor access and indoor access.

Q5b. Which characteristics were perceived to be most diverse across the

six visual stimuli?

There were a number of results in the neighbourhood characteristics

section that showed some of the perceived differences among the stimuli. The

neighbourhood characteristics that had the greatest variance included the mix of

housing types, the size of the homes, the size of outdoor space and the diversity

of residents. Visual stimuli 4 was perceived to have the least mix of housing

types while stimuli 5 was perceived to have the most. Visual stimuli 5 was

perceived to have the smallest homes and the smallest personal outdoor space

while visual stimuli 3 was perceive to have the largest in both categories. In

232 terms of neighbourhood diversity, visual stimuli 6 was perceived to be the least diverse while visual stimuli 5 was perceived to be most diverse.

Table 58:Summary of Perceived Neighbourhood Characteristics

Visual Stimuli Extremes Characteristics of Importance

• Most multi-family housing • Higher end of housing mixes • Lowest degree of visual privacy • Smaller home sizes Visual Stimuli • Most street parking for residents • Higher traffic 1 • Most indoor access for resident parking • Higher resident diversity  Walking distance to grocery stores and shopping • Many modes of transportation

• More visually stimulating • Best for walking access to grocery stores, restaurants, • More of mix of housing types Visual Stimuli cafes, and community amenities • Fewer multi-family 2 • Most active public spaces • Smaller lot sizes and outdoor spaces • Shortest and most convenient commute • Higher resident diversity • Many modes of transportation

• Largest homes and outdoor spaces • Higher end of housing mixes Visual Stimuli • Most visually stimulating • Lower noise 3 • Most people of similar life stages • Lower traffic

• The lowest mix of housing types • The fewest single family homes • Fewest multi-family homes

• Lowest traffic

• Highest off street parking for residents Visual Stimuli • Fewest modes of transportation 4 • Lowest noise • Least active parks and public spaces • Most safe

• Greatest mix of housing types • Smallest homes, lots, and outdoor space • Most active parks • Most noise and most traffic Visual Stimuli • Highest degree of diversity • Fewer single family homes 5 • Widest range of life stages • Most visitor street parking • Most modes of transportation • Lowest degree of safety

• Requires the most driving to grocery stores, restaurants, cafes and community amenities Visual Stimuli • Higher degree of visual privacy • Lowest diversity of residents 6 • Fewer modes of transportation • Longest and most inconvenient commute • Most outdoor access to vehicles

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Statistically Significance of Perceived Characteristic Differences

In determining the statistical significance of the results, a series of one- way within subjects or repeated measures ANOVAs were conducted to compare the effect of perceived characteristics within the six visual stimuli. Table 59 provides a summary of the results of the 23 analyses of variance used. Where epsilon was less than .75 Greenhouse-Geisser was reported and where epsilon was greater than .75 Huynh-Feldt was reported. The results shown in green in

Table 59 reached a statistically significant result. These results included: traveling distance to commercial amenities, mix of housing types, amount of single family homes, size of homes, size of outdoor spaces, visual privacy levels, noise levels, sense of safety, diversity of residents, commute length and transportation options. All other results did not reach statistical significance.

Table 59: Statistical Significance of Perceived Characteristics of Visual Stimuli Assumption Statistically Characteristic Tested Mauchly's Test Correction ANOVA result of sphericity significant result Traveling distance to grocery stores chi-square = 32.220, p =.004 Violated Huynh-Feldt F(4.393, 232.839) = 0.819, p = .515 Not Significant Traveling distance to commercial amenities chi-square = 30.180, p =.007 Violated Huynh-Feldt F(4.422, 234.348) = 2.946, p = .021 Significant Traveling distance to community amenities chi-square = 33.153, p =.003 Violated Huynh-Feldt F(4.505, 220.760) = 1.123, p = .348 Not Significant

Visual appearance chi-square = 23.383, p =.055 Confirmed None F(5, 255) = 2.237, p = 0.51 Not Significant Mix of housing types chi-square = 10.728, p =.708 Confirmed None F(2, 260) = 7.160, p < .001 Significant Amount of Single family homes chi-square = 29.341, p =.009 Violated Huynh-Feldt F(4.393, 224.055) = 3.014, p = .016 Significant Amount of multifamily homes chi-square = 33.852, p =.002 Violated Huynh-Feldt F(4.355, 230.838) = 2.049, p = .082 Not Significant Size of homes chi-square = 25.712, p =.028 Violated Huynh-Feldt F(4.728, 236.410) = 3.286, p = .008 Significant Size of outdoor space chi-square = 15.790, p =.327 Confirmed None F(5, 250) = 3.908, p = .002 Significant Lot sizes chi-square = 22.115, p =.077 Confirmed None F(5, 255) = .693, p = .629 Not Significant Park activity chi-square = 19.127, p =.161 Confirmed None F(5, 250) = 1.041, p = .394 Not Significant Public space and sidewalk activity chi-square = 21.132, p =.099 Confirmed None F(5, 250) = 1.902, p = .094 Not Significant Noise level of the neighbourhood chi-square = 24.023, p =.046 Violated Huynh-Feldt F(4.717, 240.551) = 1.938, p = .093 Not Significant Visual privacy chi-square = 27.208, p =.018 Violated Huynh-Feldt F(4.689, 234.447) = 5.001, p < .001 Significant Traffic levels chi-square = 45.035, p < .000 Violated Greenhouse-Geisser F(3.623, 184.756) = 4.318, p = .003 Significant Sense of safety chi-square = 29.243, p =.010 Violated Huynh-Feldt F(4.342, 217.111) = 2.592, p = .033 Significant Diversity of residents chi-square = 17.750, p =.219 Confirmed None F(5,265) = 5.306, p < .001 Significant Life stages of residents chi-square = 20.932, p =.104 Confirmed None F(5, 255) = 2.075, p = .069 Not Significant Parking for residents chi-square = 24.351, p =.042 Violated Huynh-Feldt F(4.598, 234.487) = 1.067, p = .379 Not Significant Parking for visitors chi-square = 25.228, p =.033 Violated Huynh-Feldt F(4.578, 228.878) = .541, p = .730 Not Significant Access to vehicles chi-square = 28.836, p =.011 Violated Huynh-Feldt F(4.601, 234.668) = .786, p = .551 Not Significant Commute length chi-square = 22.739, p =.065 Confirmed None F(5, 245) = 2.463, p = .034 Significant Transportation options chi-square = 24.627, p =.039 Violated Huynh-Feldt F(4.588, 238.562) = 4.664, p = 001 Significant significant at p < .05

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Q5c. How were the neighbourhood characteristics perceived in relation to

the most preferred visual stimuli?

In the previous chapter, visual stimuli 3, the nodal or urban village density

pattern, shown in Figure 63, was determined to be the most preferred of the

visual stimuli followed by stimuli 5, the evolutionary density pattern, shown in

Figure 64. Where stimuli 3 was demonstrated to be different than the other

stimuli may begin to indicate why this stimuli was more preferred. In comparison

to the other six stimuli, visual stimuli 3 was perceived to be the most visually

stimulating with 63% of participants ranking it as visually stimulating or somewhat

visually stimulating (Table K4 in Appendix K). A paired samples t-test indicated

visual stimuli 3 (M=1.66, SD=.979) was statistically more visually stimulating than

visual stimuli 1 (M=2.53, SD=1.030) t(52)=4.058, p < .001. This was followed by

visual stimuli 2 with 52% and visual stimuli 5 with 51% (Table K4 in Appendix K).

Visual stimuli 3 was perceived to have more single family homes with 53% of participants indicating it had more or somewhat more single family homes (Table

K6 in Appendix K). A paired samples t-test indicated that there were statistically

significant differences between visual stimuli 3 (M=3.32, SD=2.87) and visual stimuli 4 (M=2.87, SD= 1.020) t(52) = 3.261, p = .002 as well as visual stimuli 5

(M=2.81, SD=1.128) t(52)=2.685, p =.010.

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Figure 63: Visual Stimuli 3: Nodal or Urban Village Density Pattern - Street View Visual stimuli 3 (Figure 63) was also perceived to have the largest homes with 38% of participants indicating the homes were large or somewhat large, this was followed by visual stimuli 6 with 30% (Table K8 in Appendix K). A series of paired samples t-test indicated that there was statistically significant difference in how participants perceived the size of homes between visual stimuli 3 (M=3.21,

SD=.769) and visual stimuli 1 (M=2.74, SD=.858) t(52) = 2.977, p=.004, between visual stimuli 3 and 2 (M=2.96, SD=.649) t(52)=2.358, p=.022 and between visual stimuli 3 and 5 (M=2.63, SD=.929) t(51)=3.879, p<.001. Less people felt that the personal outdoor spaces in visual stimuli 3 were small with 35% of participants indicating they were somewhat small and no participants indicating the outdoor spaces were small (Table K9 in Appendix K). A series of paired samples t-test indicated that there was statistically significant difference in how participants perceived the size of personal outdoor space between visual stimuli 3 (M=2.96,

SD=.898) and visual stimuli 2 (M=2.53, SD=.749) t(52)=3.395, p=.001, as well as between visual stimuli 3 and 5 (M=2.41, SD=.922) t(53)=3.622, p=.001.

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Visual stimuli 3 was perceived to have one of the lowest noise levels with

24% of participants indicating it would be low noise or somewhat low noise

(Table K13 in Appendix K). This is considerably different than other visual stimuli

which are around 10-15% of participants. Only visual stimuli 4 was greater with

27% of participants. However the only result that was shown to be statistically

significant was between visual stimuli 3 and visual stimuli 5.

Visual stimuli 3 had the greatest range of perceived life stage diversity

levels which resulted in the lowest overall percentage of participants indicating

that it had a high level of diversity with 49% of participants (Table K18 in

Appendix K). Visual stimuli 3 was also shown to have the greatest percentage,

44%, of participants perceiving that residents’ access to parking would be outside

versus through indoor means (Table K21 in Appendix K). However neither of

these results were shown to be statistically significant.

Figure 64: Visual Stimuli 5: Evolutionary Density Pattern - Street View

Visual stimuli 5 (Figure 64), the second most preferred visual stimuli, had

a greater number of differences in the results. This could be largely due to the

dichotomy of preferences, as a significant proportion of participants indicated this

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neighbourhood as their top preference, as well as a significant proportion

indicating it as their last preference. Of the six stimuli, visual stimuli 5 was

perceived to have the greatest mix of housing types with 58% of participants

indicating it was more mixed and a combined 79% of participants indicating it

was somewhat mixed or more mixed. A series of paired t-tests showed the

difference was statistically significant between visual stimuli 5 and visual stimuli

3, 4 and 6. The next closest stimuli, which was not a statistically significant

difference was visual stimuli 1 with 41% of participants indicating it was more

mixed and when combined 72% of participants indicating it was somewhat more mixed or more mixed (Table K5 in Appendix K).

Visual stimuli 5 was also perceived to have the fewest single family houses by participants. Thirteen percent of participants indicated it had less single family homes and a combined 41% of participants indicated it had less or somewhat less single family homes (Table K6 in Appendix K). A series of paired t-tests showed that this result was significantly different from the results of visual stimuli 1, 2, 3, and 6, there were not statistically significant differences with visual stimuli 4. Visual stimuli 5 tied with visual stimuli 1 on the percentage of participants perceiving them as having more multi-family homes with 37% of

participants indicating they had more multi-family homes. However visual stimuli

5 only demonstrated significantly different results from visual stimuli 4.

Thirty-three percent of participants indicated that visual stimuli 5 would

have many modes of transportation in comparison to visual stimuli 1 which 26%

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of participants indicated the same (Table K23 in Appendix K). A series of paired

t-tests showed that this result was significantly different from visual stimuli 4 and

Generally participants indicated that visual stimuli 5 had a smaller amount of private spaces in comparison to the other five visual stimuli. Forty-five percent of participants indicated the homes in visual stimuli 5 were small or somewhat small with 12%, a larger percentage than all other stimuli, specifically indicating

the homes represented where small. A series of paired samples t-test indicated

that there was statistically significant difference in how participants perceived the

size of homes between visual stimuli 5 (M=2.63, SD=.929) and visual stimuli 2

(M=2.96, SD=.649) t(51) = -2.261, p=.028, between visual stimuli 5 and

3(M=3.21, SD=.769) t(51)=3.879, p<.001, between visual stimuli 5 and

4(M=2.96, SD=.871) t(50)=-2.115, p=.039, and between visual stimuli 5 and

6(M=2.94, SD=.895) t(51)=-2.028, p=.048.

Fifty-nine percent of participants perceived the private outdoor spaces in

visual stimuli 5 as being small (15%) or somewhat small (44%) (Table K9 in

Appendix K), which was a statistically significant result. Visual stimuli 5 was also

shown to have the greatest amount of noise with 22% of participants indicating

this neighbourhood would have high noise and 33% of participants indicating it

would have somewhat high noise (Table K13 in Appendix K). A series of paired t-

tests showed that visual stimuli 5 was perceived to have statistically significant

differences in noise levels from visual stimuli 3 and 4. Visual stimuli 5 had the

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largest proportion of participants indicate this neighbourhood would have a low

degree of visual privacy with 22% of participants, which is significantly more than

the next closest result, stimuli 1 with 13% (Table K14 in Appendix K). Paired t-

tests showed the result had statistically significant differences from visual stimuli

4 and visual stimuli 6. Visual stimuli 5 was perceived to have the most traffic with

24% of participants indicating it had high traffic, this was followed by stimuli 6

with 13% of participants. Also, 43% of participants indicated visual stimuli 5 had

somewhat high traffic (Table K15 in Appendix K). A series of paired t-tests

indicated there were statistically significant differences between perceived traffic

levels in visual stimuli 5 and visual stimuli 3 as well as visual stimuli 4. Visual

stimuli 5 was also indicated to have a lower degree of safety than the other visual

stimuli with 26% of participants indicating it had a low or somewhat low degree of

safety (Table K16 in Appendix K). A series of paired t-tests indicated there were

statistically significant differences in perceived degree of safety between visual

stimuli 5 and both visual stimuli 3 and 4. Overall, size of private space, visual privacy, noise, traffic and safety are typically described as negative characteristics of a neighbourhood, yet visual stimuli 5, the second most preferred visual stimuli, is perceived to have a higher degree of each of these factors than any other stimuli.

Visual stimuli 5 also was perceived to have a high level of diversity amongst participants. Forty-eight percent of participants indicated it had high

degree of diversity in terms of age, income, and ethnicity. This was followed by

241 visual stimuli 1 with 33% of participants indicating a high level of diversity and a combined 79% of participants stating it had somewhat high or high diversity

(Table K17 in Appendix K). A series of paired t-tests were conducted and demonstrated that there was statistical significant differences between visual stimuli 5 and visual stimuli 3, 4 and 6. Forty-four percent of participants indicated that visual stimuli 5 had a high diversity in the range of resident life stages, in comparison to the next highest which was visual stimuli 1 with 23% of participants indicating the same (Table K18 in Appendix K).

Visual stimuli 5 was also shown to have the most active parks, with 35% of participants indicating the parks would be active with lots of people. This is 11 percentage points greater than the next closest visual stimuli (Table K11 in

Appendix K). The largest proportion of participants perceived the lot sizes as small in visual stimuli 5, 15% of participants indicated the lots were small and

35% indicated they were somewhat small (Table K10 in Appendix K).

This comparison between the most preferred, visual stimuli 3 – the nodal or urban village density type, and the second most preferred, visual stimuli 5 – the evolutionary density type, provides an interesting dichotomy in the perceived characteristics results. Visual stimuli 3 appears to be perceived as more neutral on many of the neighbourhood characteristics while visual stimuli 5 seems to be perceived to be more extreme on almost all of the neighbourhood characteristics.

That being said, overall more participants ranked visual stimuli 3 in their top two

242 preference, visual stimuli 5 had a more extreme preference differential with large proportions of participant ranking it as their top preference and last preference.

Q5d. How were the neighbourhood characteristics perceived in relation to the least preferred visual stimuli?

In the previous chapter, visual stimuli 1, the modern density pattern, was identified as the least preferred density pattern. Participants perceived that in visual stimuli 1 grocery stores and shopping would be within, or somewhat within walking distance to grocery stores or shopping with 54% of participants indicating this (Table K1 in Appendix K). Also, 45% of participants felt the traveling distance to community amenities such as schools and community centers would be walking or somewhat walking distance, which tied with visual stimuli 5 and came just behind visual stimuli 2 (Table K3 in Appendix K). However none of these travelling distance results were shown to be statistically significant.

Figure 65: Visual Stimuli 1: Modern Density Pattern - Street View

Visual stimuli 1 was perceived to be more uniform in appearance with 36% of participant indicating it was uniform or somewhat uniform in appearance

(Table K4 in Appendix K). Second only to visual stimuli 5, visual stimuli 1 was

243 perceived to have high mix of housing types. Forty-one percent of participants indicated it had more of a mix and an additional 31% identified visual stimuli 1 as having somewhat of a mix (Table K5 in Appendix K). Nineteen percent of participants indicated that visual stimuli 1 was mostly made up of single family homes. However, a large group, 37% of participants indicated visual stimuli 1 was mostly multi-family with 72% indicating it was somewhat more or more multi- family (Table K7 in Appendix K). Forty-one percent of participants indicated they believed the homes in visual stimuli 1 were small (Table K8 in Appendix K).

Interestingly, 26% of participants perceived visual stimuli 1 as having the largest lot sizes (Table K10 in Appendix K). Participants indicated that the parks in visual stimuli 1 would be active, with 21% indicating the parks would be active and 42% indicating they would be somewhat active (Table K11 in Appendix K).

Participants indicated that visual stimuli 1 had the lowest degree of visual privacy with 13% of participants stating low degree and 46% indicating a somewhat low degree of privacy (Table K14 in Appendix K). Visual stimuli 1 was perceived by

11% of participants to have a higher level of traffic than the other stimuli and 57% of participants indicated somewhat higher traffic (Table 15 in Appendix K).

Participants perceived visual stimuli 1 to have a higher degree of diversity, with

33% of participants indicating a high degree of diversity and 44% indicating a somewhat high degree of diversity (Table K17 in Appendix K). Visual stimuli 1 was identified as having more modes of transportation with 26% of participants

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indicating more modes of transportation and 45% of participant indicating

somewhat more modes of transportation.

7.2. Discussion: Influential Characteristics

The results of the key informant interviews discussed in section 4.2.1

indicated that there are a number of factors that industry members feel contribute

to neighbourhood selection and home buying. The results also support what

industry has experienced in regard to the characteristics of development proposals that create the most controversy. In this section the research study explored the perceived characteristics of each of the visual stimuli and specifically explored the perceived characteristics of the most preferred and least preferred visual stimuli. The intent is to identify which characteristics are more

prevalent in in the preferred visual stimuli and indicate if these findings align with

the findings from industry. In a similar fashion this will also be evaluated for the

least preferred urban forms.

As mentioned above, visual stimuli 3 was identified as the most preferred

visual stimuli by the largest group of participants. Visual stimuli 3 was found to be

the most visually stimulating with active streets and parks. Participants felt there

were more single family homes overall and the homes were the largest out of the

six stimuli. The neighbourhood was perceived to have the lowest noise, the most diversity of age, life point, income, and ethnicity and the private outdoor spaces were not too small overall. The stimuli was also believe to have the highest

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amount of outdoor and street parking for residents. These findings show that

characteristics indicated by industry such as low noise and larger homes are

indicative in the most preferred visual stimuli which is largely in line with previous

studies (Mohan & Twigg, 2007). Characteristics such as parking access, location and too much parking on the street was indicated by the key informants as being

a central issue for buyers and community groups. The fact that the most

preferred stimuli has mostly street parking for residents appears divergent from

this thinking. This may suggest that if characteristics that are more important for

individuals are meeting expectations the less important issues become less

significant.

Visual stimuli 5 was shown to be the second most preferred as well as

having a significant number of participants rank it as their least preferred

neighbourhood in Chapter 6. This stimuli was perceived to have the highest

overall housing mix with the fewest single family homes and the most multi-family units in comparison to the other visual stimuli. It was perceived to have the smallest amount of private outdoor space and the smallest homes. The neighbourhood was perceived to have the most traffic, the most noise, the lowest amount of visual privacy and lowest safety. It was also suggested that this neighbourhood had the highest overall diversity in terms of age, income and ethnicity. These finding present an interesting contrast to the results of the key informant interview results. Key informants indicated that having enough personal space, with visual privacy and higher safety were generally preferred in

246 their experience while traffic and noise where two of the most significant challenges the industry faces. Visual stimuli 5 was perceived to have the opposite characteristics to what was expected.

The characteristics presented here identify a contradiction to the results presented in Chapter 6. In Chapter 6 visual stimuli 5 was identified as being most preferred by the Space Seekers cluster, a lifestyle cluster which was attributed as preferring amenities associated with single family homes and suburban neighbourhoods. Meanwhile the characteristics of visual stimuli 5 perceived by participants’ state it was perceived to have less single family homes, more of a mix of housing types, smaller homes, more traffic and more noise which are counter intuitive to the Space Seekers cluster. This contradiction potentially could be attributed to the different lifestyle clusters and the large group of participants that ranked visual stimuli 5 lower in terms of preference. While there is no specific analysis to support this notion, the Space Seekers cluster was one of the smaller lifestyle clusters and there were a number of participants that had higher preferences for the other visual stimuli.

Visual stimuli 1, the least preferred visual stimuli, was seen to be the most walkable in terms of accessing grocery stores and community amenities such as schools and community centers. Visual stimuli 1 was perceived to have the largest amount of single family homes, as well as the largest amount of multi- family homes. It was also suggested that it was the most uniform in appearance with the lowest degree of visual privacy, higher traffic and noise, with high levels

247

of age, income and ethnic diversity. The findings for visual stimuli 1 show some

indication that the perceived characteristics are similar to the results of previous

research.

As the findings suggest, there are definitive differences in how participants

perceived the twenty-three characteristics in regards to each of the visual stimuli

and the urban form density patterns they represent. Also, the characteristics

differ to a significant degree between the most preferred visual stimuli and the

second most preferred stimuli suggesting that these characteristics are not as

significant as expected. This finding disproves the earlier stated hypothesis that

the characteristics and complaints that industry members receive about their

proposed plans and developments would indicate most and least preferred visual

stimuli. This would suggest that other characteristics, or the compilation of other

characteristics, contribute to preferences.

As found in Chapter 6, lifestyle can be a key indicator of preferences for

different urban forms, and as stated in Chapter 5, each lifestyle differs in the their

stated preferences for neighbourhood form and housing. In knowing these results

it would be suggested that the perceived characteristics of each of the visual

stimuli would differ among the lifestyle clusters. Unfortunately, due to a limited

number of cases, a statistical analysis of this relationship was not effective in

describing this relationship. Future research should explore this issue in greater detail by collecting a larger sample size.

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7.3. Conclusions

As expected the participants’ perceived characteristics differed for each of the visual stimuli. The somewhat unexpected finding was that there is not necessarily an alignment between the characteristics that industry has experienced to be challenges with proposed developments and the perceived characteristics. Furthermore, the characteristics that industry had indicated would increase preferences were also not aligned with the perceived characteristics of the most preferred visual stimuli.

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8. Summary and Recommendations

The final chapter is the summation and recommendations that have come

out of this research study. The chapter will discuss the conclusions in relation to

the aims and objectives of the project and its contribution to theory. It includes

implications and recommendations for future policy, as well as the limitations of

the methodology, and will discuss recommendations for future research.

8.1. Introduction

This research study set out to understand how urban form, when presented

at constant densities, affects the perceptions of density and preferences of

residents within the case area of Calgary. The aim of the research was to understand the characteristics of both the residents and the preferred urban forms in order to inform strategies for successfully increasing density in a sprawling urban environment.

The research presented is original. There has been no previous research that has evaluated the effects of urban form on density perceptions at the neighbourhood level. There has also been no previous research that has investigated this issue in relation to resident lifestyles. The hypothesis of the research was that these lifestyles would largely explain resident preferences. The research study was designed around five research questions:

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Q1: Which visual stimuli represents the preferred type of urban form? Least

preferred type of urban form?

Q2. Which visual stimuli and associated form was perceived as the densest?

The least dense?

Q3. Is there a correlation between neighbourhood form preference and the

perceived density?

Q4. Is there a relationship between the determined lifestyle clusters of

participants and preferred visual stimuli?

Q5. Do the perceived characteristics of the preferred visual stimuli indicate

why one stimuli is preferred over others?

8.2. Empirical Findings

The empirical findings of this research are related to the five research questions and are a summary of the findings in “Chapter 6 Results: Urban Form and Density Perceptions and Lifestyle” and in “Chapter 7 Results: Characteristics of Preferred Urban Form.”

Q1: Which visual stimuli represents the preferred type of urban form?

Least preferred type of urban form?

Urban form had a statistically significant effect on how research participants perceived density in urban environments. As a representative sample, it can be said that Calgary residents will accept higher densities when designed with specific, preferred, urban forms. The findings demonstrate that variation,

251 complexity and openness of urban forms are key contributing factors. The empirical findings related to the research question are:

 The nodal or urban village density pattern and urban form was most

preferred overall (visual stimuli 3)

 The modern density pattern and urban form was least preferred overall

(visual stimuli 1)

Q2. Which visual stimuli and associated form was perceived as the

densest? The least dense?

Results show that urban form has a statistically significant effect on how individuals perceive density. When areas with the same measured population density have different urban forms, they are often perceived to have different densities.

 The evolutionary density pattern and urban form was perceived to be the

most spatially dense and estimated to have the highest population density

(visual stimuli 5).

 The nodal or urban village density pattern and urban form was perceived

to be least spatially dense (visual stimuli 3).

 The neotraditional or evenly distributed density pattern and urban form

was estimated to have the lowest population density (visual stimuli 4).

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Q3. Is there a correlation between neighbourhood form preference and

the perceived density?

There is no statistically significant correlation between an individual’s perception of density and their level of preference. This demonstrates that there are other factors that contribute to preferences for neighbourhood forms.

However, it was demonstrated that there is a relationship between overall preferences and perceived density when these results were compared at an aggregate level. The most preferred form overall was also perceived to be least dense, most spacious and was seen to have one of the lowest population densities suggesting an aggregate relationship.

Q4. Is there a relationship between the determined lifestyle clusters of

participants and preferred visual stimuli?

Lifestyle characteristics of participants were confirmed to be an indicator of preferences for each of the visual stimuli. While the results were not statistically significant there appeared to be enough of a difference in the results to indicate what a larger sample size may reveal. Each visual stimuli represented differing neighbourhood density patterns and urban forms. It was found that each lifestyle cluster, defined based on different factors, had greater preferences for each of the visual stimuli. The following outlines those differences:

 Cluster A - Convenient Family had the highest overall preference for visual

stimuli 3 (nodal / urban village density pattern) with some preference for

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visual stimuli 2 (corridor density pattern), 4 (evenly distributed /

neotraditional density pattern) and 5 (evolutionary density pattern). This

cluster had very low preference for visual stimuli 1 (modern density

pattern) and 6 (transit-oriented development density pattern).

 Cluster B - Space Seekers had the highest overall preference for visual

stimuli 5 (evolutionary density pattern) with somewhat high preference for

visual stimuli 6 (transit-oriented development density pattern). This cluster

had the lowest overall preference for visual stimuli 2 (corridor density

pattern) and 3 (nodal / urban village density pattern).

 Cluster C - Neutral had the highest overall preference for visual stimuli 4

(evenly distributed / neotraditional density pattern) with high preference for

visual stimuli 1 (modern density pattern) and low preference for the other

six visual stimuli.

 Cluster D - Care Free Living had the highest overall preferences for visual

stimuli 2 (corridor density pattern) and 6 (transit-oriented development

density pattern) with some preference for visual stimuli 3 (nodal / urban

village density pattern). This stimuli had low preference for visual stimuli 4

(evenly distributed / neotraditional density pattern) and 5 (evolutionary

density pattern).

 Cluster E - Family Focused has the highest overall preference for visual

stimuli 1 (modern density pattern) with low preference for all other visual

stimuli.

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Q5. Do the perceived characteristics of the preferred visual stimuli

indicate why one stimuli is preferred over others?

There are distinctive differences in how the 23 characteristics tested were

perceived in each of the visual stimuli. While not all results were statistically significant, 11 out of the 23 characteristics tested reached statistically significant differences demonstrating that different urban forms can result in differing perceived characteristics depending on the characteristic. It was expected that some characteristics would indicate which visual stimuli were most preferred and which were least preferred. However, many characteristics such as traffic and noise were perceived as high on both the least preferred and more preferred

stimuli. The results show the perceived characteristics do not definitively indicate

the visual stimuli preferences.

8.3. Implications of the Research

The research presented here emphasizes the importance of both urban form and lifestyle in peoples’ perception of density in urban environments. There are implications for the planning and environmental aesthetics research fields and for current and future research in these areas.

8.3.1. Planning and environmental aesthetics research

This research is significant for the planning and environmental aesthetics

research community because it provides results that challenge previous research

that has put significant importance on perceived population and physical density

255 as key indicators of neighbourhood preferences. Since there is no direct correlation between perceived density and preferences it can be argued that urban form is a more significant factor than density in participant acceptance of higher density urban environments. Instead, this research shows that there is a relationship between preferences for urban environments and the form of those environments. In regards to urban form, the study supports previous research showing that the most preferred neighbourhood forms had higher levels of variation, complexity, and openness (Heath et al., 2000; Hur et al., 2010; R.

Kaplan et al., 2004; S. Kaplan et al., 1972; Kearney, 2006; A. E. Stamps, 2002,

2004). The findings also suggest that participants associated the urban forms with their past experiences, and amenities that were important to them. This area was not explicitly investigated so it would benefit from future research.

In addition, the use of lifestyles in this research presents a personal characteristic indicator that has not previously been used in urban based environmental aesthetics research. Lifestyle and specifically the activity, interest and opinion framework is a means to relate a number of characteristics about a person into a single representative variable. As the literature review has shown, lifestyle has long been used in real estate and market research as a means of predicting consumer behaviours and purchase choices with much success. This study, specifically, shows that there are differences among lifestyle groups in regards to their preferences for different urban forms and suggests that lifestyle may be an indicator of other aesthetic preferences. This suggests that lifestyle is

256

effective in representing why participants prefer the varying visual stimuli from

both a physical and psychosocial perspective. The findings of this research

would suggest that environmental aesthetic researchers should use lifestyles as

a psychosocial characteristic and a means of describing and predicting

participant aesthetic preferences. Future research would benefit from the use of

lifestyles in representing participant characteristics and describing aesthetic

choices.

8.4. Recommendations

There are a number of recommendations that are extrapolated from the

results of this research study. These recommendations are primarily focused on the professional planning practice.

8.4.1. Planning Practice Implications

Preferred Urban Form

This research study has demonstrated the importance of urban form in

peoples’ perceptions of density and, by extension, the importance of urban form

in the acceptance of higher density planning and development. The findings from

this study show that variation, complexity and openness are the factors of urban

form that contribute to the acceptability of higher density urban environments.

This information will allow members of the profession to design plans intended to

increase density that would be more palatable to residents of the

neighbourhoods where such plans are proposed. Preferred urban forms can be

257 used in representations of redevelopments, public engagements and can generally be used to create public acceptance.

In more specific terms, the results of this research specified that the most preferred urban was the nodal or urban village density pattern. A pattern that is described as centered on a focal point such as a main street, a park, intersection or transit hub, with higher density buildings around this central focal point.

Typically in this form does not have the tallest of all buildings at the centre with gradual tapering to single family homes, rather it has taller building throughout providing a greater level of variation. The results would suggest that this form would be the most successful approach for increasing density. When comparing this form to some of the major developments currently proposed with Calgary it could be argued that many of the key developments are essentially in line with this density pattern. For example redevelopment projects such as the Bridges, the Currie Barracks Master Plan, the University of Calgary West Campus Master

Plan and even some green field development plans such as Seton and Sage Hill have many of the characteristics of this urban form. This suggests that planning community in Calgary is already heading down the path of implementing this urban form, whether it is intentional based on its acceptability or based on the other benefits. The findings of this research provide additional evidence as to the benefits of implementing the nodal or urban village density pattern.

At a more general level, the results indicate that more importance should be placed on urban form within planning policy and development plans. The results

258 also support development strategies that are designed to achieve specific urban form outcomes such as form-based codes and master planned communities

(Talen, 2013). For example, The Bridges, West Campus and Currie Barracks all demonstrate the benefits of a master planned communities in achieving defined urban forms and specifically forms that are shown to be more acceptable. Further increasing the importance of urban form within planning policy, it is the recommendation of this research to use these tools and others to achieve the urban form outcomes that the research results show are most preferred and would be most acceptable to existing residents.

Lifestyles

This research has highlighted the contribution lifestyle makes in identifying residents’ understanding, preferences and acceptance for higher density urban environments. As municipal governments and planners continue to work to increase density in inner city neighbourhoods across North America, the use of lifestyles presents itself as an opportunity for more targeted and useful engagement. As has been demonstrated, increasing density in established neighbourhoods is often a contentious issue with existing residents and is often brought up at public engagement sessions (Fischel, 1999). Lifestyle is a clear demonstration of the different issues that people care about in the urban environment. Understanding participants’ lifestyles could provide greater insight into which issues would be most contentious for the most residents. Additionally, this information could be used to help develop effective engagement and

259 communication strategies for conducting engagement sessions with the public.

For instance, often in these engagement sessions there is some form of visual representation of what a plan for area redevelopment may look like. Knowing the lifestyles of participant and/or residents and the preferred urban forms of those lifestyles would indicate the best ways to construct these visual representations.

By having a greater understanding of resident lifestyles in existing neighbourhoods, there is an opportunity to evaluate the best strategies (i.e. urban forms) to increase density in that neighbourhood based on the most prevalent lifestyles. This approach creates opportunities to inform land-use planning and policy development for redevelopment zones. Furthermore, the use of lifestyles could be considered in the opposite approach. The results could be used to plan neighbourhoods to attract specific lifestyle groups to areas as part of a larger redevelopment strategy.

The Methodology

The methodology presented in this study can be used by the planning community as an engagement tool to understand the preferences of residents in order to design a redevelopment plan that includes higher density urban forms that would be acceptable to residents. The approach uses a single area and simulates multiple scenarios for the outcome of redeveloping that area. It enables the collection of empirical data about participants’ perceptions and preferences for each of the scenarios. By using this approach, participants, ideally local residents, would give input based on their perceptions and indicate preferences

260

for these hypothetical scenarios. The outcome of this approach would be

empirical results which would show which scenario is most preferred and the

design characteristics related to the preferred scenario. This input from residents

would then be used to develop the redevelopment plan for the area. Along with

that, residents could be shown what percentage of the community preferred

which scenario and the most preferred scenario could be further refined in

conjunction with community members. The result is a plan that was developed

through public engagement, giving planners information to make decisions and

the opportunity for community members to see how their opinions contributed to

these decisions.

8.5. Limitations of the Approach and Methodology

During the execution of the research a few areas were identified as limitations to the overall research approach and methodology. These limitations

are as follows:

1. The largest limitation of the methodology is the logistical aspect of

attracting participants to the study and holding the study at a specific

location at the University of Calgary. This had two impacts on the study.

Firstly, having participants come to a specific location on the University of

Calgary campus limited the demographic and residential range of

participants. This limitation could have been overcome if the technology

had been available in multiple locations across the city. The technology

261

chosen for the study limited the project to The University of Calgary

because of the large format high definition display screens available in

the Taylor Family Digital Library. Secondly, the low number of

participants attracted to the study limits the analysis and results and the

ability to generalize the results.

2. The second limitation was the use of three dimensional models with a

significant amount of detail. The development of these models took a

significant amount of time and resources and ultimately limited the study

to only evaluate one density level. A more robust approach would have

been to include two to three measured density levels using the same six

neighbourhood urban forms. However, this also would have resulted in a

much longer participation period which may have resulted in fewer

participants.

8.6. Reflections on Methodology and Questionnaire Questions

While analyzing the results of the two questionnaire that were used in this

research there were a number of reflections on how the questions were written.

1. When participants where asked what were the most important

characteristics they looked for in choosing a home and neighbourhood,

people generally wanted everything. The choices in the results ranked on

the extremes for most of the characteristics. These rankings may have

ranked differently for each of the lifestyle clusters however there was not a

262

sufficient number of cases to look at this comparison in detail. In reflection

of the methodology future research should ask participants to rank each

characteristic in terms of importance.

2. During the analysis of the part 2 questionnaire, which was used to

evaluate participant perceptions of the six visual stimuli, it was identified

that some of the questions used were not as useful as originally expected.

For example question one, participants were given as series of adjectives

and were asked to choose three to describe each of the six visual stimuli.

The results showed the same six words were used most across the six

visual stimuli, presenting a less than useful result. While there could have

been some differences among the lifestyle groups there was not sufficient

data to investigate or report on this result.

8.7. Future Research

There are three areas that were identified as possible future research contributions. These areas are as follows:

1. In this research the discussion of the variation and complexity in the six

visual stimuli took on a somewhat subjective approach. Future research

may consider the use of entropy in a three dimensional environment to

utilize an objective measure of complexity and variation in the block

facades. In this case it could be considered to measure the entropy of

every block face and aggregate that information across each of the visual

263

stimuli to measure the entropy of the overall visual stimuli. This is an

approach that has been used by Hur et al. (2010) in two dimensional

urban environments.

2. A similar methodology using a much larger sample size would enable an

analysis of how each of the lifestyle clusters perceive specific

characteristics of each visual stimuli. This will provide a greater

understanding as to why different lifestyles prefer different

neighbourhood forms.

3. Future research should also take greater consideration of the personal

experiences and housing history of the research participants. While this

was not the primary focus of this research, some questions were asked

in the questionnaire to this effect. Upon the analysis of this data it was

identified that these questions were not extensive enough to demonstrate

the full effect of life experience on preferences for different urban forms.

8.8. Conclusion and Outlook

The study presented is focused on the residents of Calgary, Alberta,

Canada. At the present time there is no certainty to what degree these findings will apply to the residents of other cities and populations. What is presented is a methodology that, once refined and tested, communities can use in three ways:

(1) to further understand the lifestyles of their populations, (2) to understand how their population perceives density, and (3) to understand what urban forms their population prefers. The availability of this information will provide planners and

264 developers a greater understanding of the expectations of residents. This information can be used to help set guidelines and planning outcomes for future development that will achieve a greater acceptability among residents.

By coincidence, at the time of completion of the academic research, the

City of Calgary is undertaking a city-wide corridor study. The study is looking at how to improve and increase density along key corridors and at major intersections through inner city neighbourhoods. There is opportunity to share this information with the project and contribute to a greater understanding of how to create acceptance for higher density developments in Calgary.

265

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Appendix A: CFERB Approval Letter: Key Informant Interviews

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Appendix B: Key Informant Interview Protocol

Key Informant Interview Guide– City Planners and Professional Planners hired by the City

1. What is your position with your organization? What does this role entail? How long have

you been in this role? If less than 5 years, what was your previous position?

2. In regards to higher density mixed use or residential building, what range of urban form

variations have you had experience planning or proposing, in Calgary? Elsewhere?

(anything above Floor Area Ratio of 1) Can you provide concrete examples that you

would categorize as neighbourhood retrofitting plans?

Prompts: suggest terms and show typology

Prompts: Ask for project name, location, and request description or rendering of the

development

3. Of the plans you have noted. What drove the decision to choose these particular urban

forms over others?

Prompt: See if market preferences or Calgary as location are mentioned

(If not discussed)

4. In the plans you have described, what role did market or resident preferences play in the

selection of specific urban forms? Did neighbouring residents’ preferences play a role?

5. In the plans you have described, what specific features of urban form do you think

address market or resident preferences?

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6. In the higher density planning proposals, which urban forms have been most popular or

least controversial in the public consultation process? Why do you think that is?

Prompt: Give some examples using the typology

7. Are there urban forms that you are aware that the market wants but developers are

unwilling to develop for specific reasons? If so, do you know what are those reasons?

Prompt Questions:

 Were there economic constraints?

 Were there regulatory constraints?

8. What is your understanding of market preferences for urban form based on? Resident

preferences?

Prompt questions:

 Is it mostly professional experience?

 Are there market research studies that you have referenced that indicated these

preferences?

 Are you aware of any companies that do this type of market research? Do you

know if these studies are available for public reference?

9. When proposing these new Transit Oriented Development (TOD) plan or Area

Redevelopment Plans, what are the most common types of concerns you receive from

public consultation regarding densification strategies? [Move to before Q10]

Prompt Questions:

Do you think urban form can address any of these concerns? If so, how?

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10. Based on you experience on these various project, do you think marketing similar

physical density with different urban forms would result in a different response by

members of the public?

11. What is the most important factor in the acceptability of higher spatial densities?

Prompt: How important is urban form?

12. Based on you experience and the projects you have worked on, could you take a look at

the typology of urban form of a 4 and 10 Floor Area Ratio and tell me if there is a form

that I may be missing or that I should explore further?

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Key Informant Interview Guide – Developers, Market Researchers, Privately

Hired Planners

13. What is your position with your organization? What does this role entail? How long have

you been in this role? If less than 5 years, what was your previous position?

14. Which high density mixed use or residential developments (FAR >1) have you been

involved with in Calgary? Elsewhere?

a. What urban form did each of these developments take? Were they all similar, or

was there a range? Please specify what they were, and where they were used.

Prompts: suggest terms and show typology

b. Would you categorize any of these projects as neighbourhood retrofitting

projects? If so, which ones?

Prompts: Ask for project details, or request rendering of the development

15. With any of the projects you have noted. Were there any challenges with regards to

urban form that you experienced while developing these higher density buildings in

Calgary? Elsewhere? If so, what were they?

Prompt Questions:

 Were there challenges selling any of the projects?

 Were there any resident objections/complaints regarding the projects?

Specific examples?

 Were there challenges getting the project approved through the city?

16. Of the projects you have noted, what drove the decision to choose these particular urban

forms over others?

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Prompt:

 Buyer or market preferences?

 Community preferences?

 Location or site context?

 Economic concerns?

 Regulatory concerns?

17. On the projects you have described, how important was the understanding of market

preferences in the selection of specific urban forms?

18. On the projects you have described, what specific features of urban form were most

influenced by expected market preferences? Were these forms chosen to target

particular markets/socioeconomic groups?

Prompt: Give some examples using the typology

19. Overall in the higher density developments you have been involved in, which of your

urban forms have been most popular or easiest to sell? Why do you think that is?

20. Are there urban forms that you are aware the market wants but are unwilling to develop

for specific reasons? If so, which forms and what are the reasons?

Prompt Questions:

 Were there economic constraints?

 Were there regulatory constraints?

21. What is your understanding of market preferences for urban form based on?

Prompt:

 Is it mostly professional experience?

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 Are there market research studies that you have referenced that indicated these

preferences?

Are you aware of any companies that do this type of market research? Do you

know if these studies are available for public reference?

22. What is the most important factor in the acceptability of higher spatial densities by

members of the public?

23. Based on your experience on these various projects, do you think marketing similar

physical density with different urban forms would result in a different response by buyers?

By surrounding community residents?

24. Based on you experience and the projects you have worked on, could you take a look at

the typology of urban forms for 4 and 10 FARs and tell me if there is a form that I may be

missing or that I should explore further?

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Appendix C: Key Informant Interview Summary

Table C1 Summary of Key Informant Interview Participation

Name Position Company

1 Member of the development industry with an urban planning expertise 2 Chris Elkey Development Manager Calgary Vesta Properties 3 George Chahal President and CEO Oxford Properties 4 Jayden Tait General Manager - Inner City infill Brookfield Multi Family Research Lead - 5 Matthew Boukall management of the residential consulting Altus Group services 6 Andrea Mikkelson Marketing and Sales Maple Projects 7 John Purdy Development Manager Streetside Developments 8 Eileen Stan Development Manager - Residential 9 Adrienne McGarvey Manager of Marketing Battistella Developments 10 Member of the development industry with an urban planning expertise Member of the development industry with development management and master planned 11 community expertise 12 Warren Gaul Architect Knightsbridge Developments Development Building 13 Planner at City of Calgary CPAG Leader Approvals City of Calgary Coordinator of Urban Design, City Wide Land Use Planning and 14 David Down Planning Policy City of Calgary 15 Joe Starkman President and CEO Knightsbridge Developments 16 Charron Ungar President Avi Urban Manager in Land Use Planning and Land Use Planning and 17 Manager Policy Policy City of Calgary 18 Paul Battistella VP Development Battistella Developments 19 Jeremy Sturgess President and Founder Sturgess Architecture 20 Mike Bucci VP Development Bucci Developments General Manager Planning 21 Rollin Stanley City of Calgary Development & Assessment

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Interview Findings

The following are the themes as identified through the interview analysis.

The interview analysis is not included as an appendix because according to the

Conjoint Faculties Ethics Review Board Approval, no one is allowed to see the

raw interview data besides the researcher and supervisor

Culture

A key area of discussion in the key informant interviews was the culture of the

real estate market and the general culture of the people who live in Calgary

 Calgary’s culture and real estate market is driven by the ups and

downs of the resource based economy. This has created peaks and

valleys in construction and real estate transactions creating challenges

for the economics of development

 The evolution of Calgary’s culture and economic drivers from the time

of agriculture, to ranching to the current resource based economy has

led to the current situation where there is a lack of ownership over the

city and the communities of Calgary.

Landscape and Climate

 Climate is a major factor in shaping the culture of the city. Cold

climates force a different type of development such as the +15

network, an interior based pedestrian and retail network

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 Altitude shapes landscape and as result shapes the geographic make-

up of the city. The plant life and what can grow is different having an

effect on how the urban environment is constructed.

 Geographic structure of the city. The development patterns related to

the geography of the city. The geographic obstacles such as terrain

have

Value of Time

 Key informants generalization that Calgarians do not value time the way

that people do in other cities. Residents would rather spend their time

driving and have a cheap house.

 Key informants also stated that people who value time tend to choose to

live in inner-city neighbourhoods where they can walk or take transit to

work.

Property Ownership

 In Calgary there is a high percentage of people who desire to own a single

family home roughly around 80% in Calgary. Even in comparison to

Toronto where that percentage is 64%

 In reality “we all want the dream picket house or picket fence and part of

our single family detached house and it’s you know a matter of settling for

what best fits your lifestyle and your budget.” Jayden Tait

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Development Culture

 Calgary is a young city so we have not had to reinvest in our infrastructure

the way that other cities have. There was a general belief that this would

all changes once Calgary sees major infrastructure failure

 Due to Calgary’s low taxes and the lack of understanding around the

impacts of low taxes on funding infrastructure the city has be driven by

development fees as the main source of revenue. Calgary is addicted to

suburban development, financially but generally who live here people do

not want to see the city continue to expand.

 Calgary has built an LRT system and uses it as a commuter rail system

that is going end up significantly over capacity over the next 20 years. The

LRT system allows for continued suburban expansion and it is continually

expanded as the city expands. LRT systems in other cities have been

used to fuel inner city development rather than city expansion

Neighbourhood Structure

Key informants were asked about what neighbourhood characteristics

contributed to the acceptance of opposition to redevelopment proposals. The

interview process probed at specific characteristics.

Key Characteristics

 Street Pattern – street pattern itself is less of an issue than it is in creating

the perception of safety. Key informants indicated that street pattern is

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often associated with safety levels in communities. If you can recreate the

perceived safety of a cul-de-sac through another pattern you can achieve

a lot. Typically people perceive a grid pattern as being less safe. Key

informants also stated that people don’t see the pattern they are looking

for the experience it creates.

 Traffic – Traffic is a major concern of residents. Higher density and

redevelopment is often associated with higher traffic which is one of the

key issues that community members bring up when such projects are

proposed. Ensuring traffic is not perceived as a danger and that it is not

cutting through their part of the neighbourhood is essential

 Neighbourhood Structure – neighbourhood nodes are the most common

way of achieving a mix of uses however they tend to be at the edge of the

neighbourhood and are typically not walkable. This is a commonplace

approach that has been taken for the last several decades but does not

achieve the desired outcomes of higher density development

 Mix of uses Horizontal – being able to achieve a horizontal mix of uses is

a challenge as it depends on the neighbourhood. Key informants have had

some success in new urbanist communities as it was an easy sell with the

particular planning outcomes. In other neighbourhoods it is harder to both

achieve and

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 Mix of uses Vertical – Residential/retail vertical mixed use is often more

difficult to develop and sell to buyers. It generally has narrower market and

is a more niche demographic in Calgary.

 Amenities – in the inner-city amenities are already in place resulting in

opportunities for a lesser need for personal space. There needs to be

more work to educate residents in the direct linkages between density and

amenities. The more development that occurs the greater the investment

in amenities

 Rear Laneways – Rear laneway make achieving higher densities in

smaller forms much more efficiently. It allows for higher densities in single

family, duplexes and row homes which many argued this is where most

buyers wish to purchase. Key informants also suggested that there needs

to be some level of encouragement in making rear laneways more

desirable such as paving laneways and standardizing them to some

extent.

 Street Trees – Street trees and their maturity can bring the scale of the

buildings down to a human scale reducing the overall height impact of the

buildings on the street. Street trees also create a feeling of safety on the

street separating from cars and creating a canopy. “The average Lay

person can tell something is different but they usually cannot tell what it is”

– Chris Elkey

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 Buildings Size – It is important when densifying not to insert a building

that is overwhelming for the community. The building size should to some

extent fit in with the scale of the existing community. As the community

evolves tall buildings can be added.

 Public Green Space – When increasing density both the amount and

quality of public green spaces need to be considered. For density to work

public spaces need to provide the outdoor spaces for the community.

Building Form

Key Characteristics

 Mass – The overall mass “loomingness” of the building at the street level

as an negative affect on the community. It is necessary to make sure that

the street level still feels comfortable.

 Height – Building Height is most often cited as a concern especially when

the height is out of context with the neighbourhood. “I definitely think that

in Calgary there’s a distinct Difference in people’s Perception about higher

density when you go from even like townhouse styles into a 4 to 6 storey-

those are 2 very distinct types of building" – Member of the Planning

Industry.

 Façade Variation – Façade variation has a significant impact on how

people perceive the streetscape. The greater the variation the more

people see it as similar to single family houses which is what people

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prefer. Increasing things like entry ways, windows, articulation, and

material changes will increase this perception.

 Lot width and Setback ratio – There is a balance between lot width and

proximity to the street and further the front of the lot line to the actual

house entrance. Can impact the perception of the building on the street.

This balance changes depending on if the building is only residential use

or mixed uses. The acceptability of shorter setbacks is greater with

commercial space on the ground floor.

 Image and Prestige – Mass media and the larger society has created a

culture where compare themselves to their neighbours. It is the “keeping

up with the Jones’s” or “I'm doing better than you" culture. Based on this

the image of one’s home on the outside in comparison to those in

proximity is important people.

 Culture of Space - People generally have an assumption of how big they

need a house to be to accommodate their uses and their stuff. However in

many cases people don’t need a bigger house they are really looking for

an attached garage.

 Aesthetic – Depending on Style – Infill products in Calgary tend to be

more contemporary or modern which is not everyone’s style and some

people see that as a connection to life style and cannot fit themselves into

that.

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 Parking – It really depends on location in inner city locations there is

generally an assumption that parking is either detached, behind or

underground. Often people are looking for an attached garage but accept

something different based on location or availability. The major issue with

proposed density increases is the belief that not enough parking is

provided and the excess will end up on the street.

 Private Green Space – People generally are looking for their own private

outdoor space preferably a green space, however they are not looking for

as big of a space as they have in the past.

 Climate – Due to the cold climate generally people want to be able to

access their vehicles internally and as result it is something to which

people aspire. Climate also is a major factor in shaping building form. It

increases interior spaces for use during winter months.

 Ground Oriented – There is a compelling story in Calgary for ground

oriented multi-family and having your own front door. There is a culture of

ownership and having your own front door plays into the into this culture

Thought on the right form for Calgary

Key informants were asked what they thought the most effective urban from would be fore Calgary.

 Common Theme From about 60-70% of the interviewees: the most

effective form for Calgary is the 3-4 storey townhome/rowhome

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o Street facing façade

o Rear, Attached, or detached parking

o Provides personal green space

o Provides personal front door

o Can be developed as private ownership or condo

o Scale fits with almost all neighbourhoods

o Has a level of scalability between affordable and higher end

o Can be achieved with any lot size

o People like the perception of not having to pay for common space

 Above 4 storeys in Calgary is where complicated issues begin to arise.

Community Complaints of Development

Key informants were asked what the most common complaint and challenges that they had received from community members.

 In general “people are very emotional and passionate about where they

live, be it their home, be it their yard, be it their street, be it their

community whatever scale it happens to be and they get very personally

connected with the debate about what should happen in their space” –

Jayden Tait

 Generally people do not like change in their personal environment

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Key Factors

 Traffic – Consistently the most significant complaint developers get about

projects that will increase density is the issue of increased traffic.

Residents automatically associate more people with more cars on the

road every day.

 Parking – Not enough parking is often seen as an issue. In many cases

even when there is a 1:1 or more parking ratio there is still a concern that

residents and visitor will park on neighbourhood streets. For Example “the

city mandates us to provide a certain amount of parking per project, which

we do. So based on what the city says, we are accommodating the

number of stalls, but the community in a lot of cases says, well we believe

that’s not [going] be enough.” – George Chahal. The public believes that

there is going to be more than one vehicle per household and those extra

cars will end up on the street.

 Height – Residents have concerns that when a taller building is proposed

near an established single family neighbourhood the building will not fit in

with the context. The building will affect the privacy of the community.

 Shadowing – As a direct result of height, shadowing of private space

such as backyards is a significant concern of residents.

 Design Aesthetic – A comment that is brought up by both residents and

community groups is that the design of the building does not fit with the

current aesthetic of the neighbourhood.

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 Land Value Impacts – There is a perception that increased density will

negatively impact land values particularly the land values of parcels

directly adjacent. This is particularly around the attractiveness of the

homes in the area in terms of resale.

 Privacy – There is often a concern over the impact of overlooking higher

rise buildings into people’s personal space, such as backyards and into

homes.

 Stigma of Multi-Family – Multi-family residential is perceived as a less

expensive built form than stand-alone single family and as a result it is

seen as affordable. This issue is less so vocalized but can often be an

underlying issue. People also often see multi-family leading to rental

properties and meaning there is less ownership over the community and