A Covariance Structural Analysis of a Conceptual Neighborhood Model

Home , Chapel Hill Mall, History of the city, Rolling Acres Mall

A COVARIANCE STRUCTURAL ANALYSIS OF A CONCEPTUAL NEIGHBORHOOD MODEL

A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Kevin A. Butler

August 2008

Dissertation written by Kevin A. Butler B.S., The University of Akron, 1995 M.S., The University of Akron, 2003 Ph.D., Kent State University, 2008

Approved by

______, Chair, Doctoral Dissertation Committee Milton Harvey, Ph. D. ______, Members, Doctoral Dissertation Committee Michael Hu, Ph. D. ______David Keller, Ph. D. ______Mandy Munro-Stasiuk, Ph. D. ______Scott Sheridan, Ph. D.

Accepted by

______, Acting Chair, Department of Geography Mandy Munro-Stasiuk, Ph. D. ______, Dean, College of Arts and Sciences Timothy S. Moerland, Ph. D.

TABLE OF CONTENTS

LIST OF FIGURES ...... viii

LIST OF TABLES ...... xi

ACKNOWLEDGMENTS ...... xiii

Chapter

1. Consequences of Metropolitan Fragmentation: The Neighborhood Concept ...... 1

Introduction ...... 1 Regions ...... 2 Uniform Regions ...... 3 Functional Regions ...... 4 Planning Regions ...... 4 Fluidity Among Approaches to Regions ...... 5 The Neighborhood Concept ...... 6 Goals of the Dissertation ...... 11 Organization of the Dissertation ...... 12

2. History and Evolution of Akron Neighborhoods ...... 13

Introduction ...... 13 A Brief History of Akron ...... 15 The Canal Era ...... 15 Industrialized Akron ...... 17 Akron’s Current Land Use Patterns...... 18 Neighborhood Profiles ...... 26 A. Original Settlement and Early Expansion [EE] ...... 26 1. Downtown ...... 27 2. Middlebury ...... 29 3. East Akron ...... 32 4. South Akron ...... 35 5. Summit Lake ...... 37 B. Annexation Neighborhoods [ANX]...... 40 1. Ellet ...... 41 iii

TABLE OF CONTENTS (Continued)

2. Kenmore ...... 43 3. Merriman Valley...... 45 C. Industrial Neighborhood Development [IND] ...... 48 1. Goodyear Heights ...... 49 2. Firestone Park ...... 52 D. Neighborhood Retail Centers [RET] ...... 53 1. Highland Square ...... 54 2. North Hill ...... 56 3. Wallhaven ...... 59 E. Neighborhoods As Large Retail and Service Centers [LRG] ...... 61 1. Chapel Hill ...... 61 2. Rolling Acres ...... 64 3. University Park ...... 66 F. Neighborhoods based on Socio-Economic Status [SES] ...... 69 1. Lane Wooster ...... 69 2. Fairlawn Heights...... 72 3. Northwest Akron ...... 74 4. Elizabeth Park Valley ...... 76 5. West Akron ...... 79 Summary ...... 82

3. Conceptual Framework: Galster ...... 83

Introduction ...... 83 Galster’s Definition of Neighborhood ...... 84 Varying Spatial Scales of Neighborhood ...... 87 Degree of Presence of Neighborhood ...... 90 Toward the Empirical Evaluation of Galster’s Model ...... 92

4. The General Factor Model (GFM) ...... 94

Introduction ...... 94 The General Factor Model (GFM) ...... 96 Principal Component Analysis (PCA) ...... 103 Exploratory Factor Analysis (EFA) ...... 104 Confirmatory Factor Analysis (CFA) ...... 105 iv

TABLE OF CONTENTS (Continued)

Summary ...... 107

5. Data Sources ...... 109

Introduction ...... 109 Data Sources ...... 109 Operationalizing Galster’s Constructs ...... 110 Sample Size ...... 112

6. Results: Descriptive Statistics and Data Transformations ...... 114

Introduction ...... 114 Measurement Scale of the Observed Variables ...... 114 Statistical Distribution of the Data...... 116 Analysis Method ...... 118 Missing Data ...... 119

7. Results: Verifying Galster’s Constructs ...... 120

The Measurement Model ...... 120 Empirical Fit...... 121 Factor Loadings for Measurement Models...... 129 Discussion...... 137 Item Reliability ...... 140

8. Results: Interrelationships Among Galster’s Concepts ...... 142

Introduction ...... 142 Confirmatory Factor Analysis of Galster’s Model ...... 142 Relationships Among Factors at the Construct Level ...... 148 Environmental—Infrastructure...... 150 Class—Demographics ...... 151 Class—Tax/Public Service ...... 151 Patterns of Linkages between Concepts in Galster Model ...... 152

9. Results: Examining Galster’s Model at the Micro-Scale ...... 156 v

TABLE OF CONTENTS (Continued)

Introduction ...... 156 Method ...... 156 Calculating Latent Variable Scores for Akron, Ohio ...... 158 Structural ...... 160 Infrastructure ...... 163 Demographic ...... 165 Class ...... 167 Tax and Public Service ...... 169 Environment...... 172 Proximity ...... 175 Political ...... 177 Social Interactive ...... 179 Summary ...... 182

10. Post-hoc Assessment of Galster’s Conceptual Model ...... 183

Introduction ...... 183 Positive Aspects of the Operationalization of Galster’s Model ...... 184 Shortcomings of the Operationalization of Galster’s Model ...... 188 Summary ...... 189

11. Results: Extending Galster to Produce An Alternate Regionalization of Akron Neighborhoods ...... 191

Introduction ...... 191 Method ...... 191 Summary ...... 196

12. Discussion and Future Research ...... 199

Empirical Evaluation of Galster’s Model ...... 199 Spatial Distribution of the Conceptual Neighborhood Model ...... 201 Understanding the Vernacular Neighborhoods of Akron, Ohio ...... 202 Future Research Considerations ...... 203 Measurement Variable Strength and Parsimony ...... 203 Model Parsimony ...... 204 vi

TABLE OF CONTENTS (Continued)

Galster’s Model at Varying Spatial Scales ...... 204 Spatial Bounds of Vernacular Neighborhoods ...... 205 Neighborhood Delineation (Region Building) ...... 205

APPENDIX ...... 206

BIBLIOGRAPHY ...... 217

vii

LIST OF FIGURES

Figure 1 -- The Neighborhood Continuum (Blowers 1973) ...... 7 Figure 2 -- Akron in its Regional Context ...... 14 Figure 3 -- Concentric zone model (After Park, et al., 1925) ...... 18 Figure 4 -- Example of Hoyt's sector model for Minneapolis, MN...... 21 Figure 5 -- Akron Land Use Patterns ...... 21 Figure 6 -- Multiple Nuclei model of the internal structure of a city...... 23 Figure 7 -- Akron Neighborhoods as defined by the City Planning Commission ...... 25 Figure 8 -- Downtown Akron...... 27 Figure 9 -- Downtown Akron from the West...... 28 Figure 10 -- Middlebury Neighborhood...... 29 Figure 11 -- Summa Health Systems' Akron City Hospital ...... 31 Figure 12 -- Goodyear Headquarters ...... 31 Figure 13 -- East Akron Neighborhood ...... 33 Figure 14 -- South Arlington Street ...... 34 Figure 15 -- South Akron Neighborhood ...... 36 Figure 16 -- Aging, rental property in the northern section of South Akron ...... 36 Figure 17 -- Limited recreational opportunities available from Summit Lake...... 38 Figure 18 -- Summit Lake Neighborhood ...... 40 Figure 19 -- Ellet Neighborhood ...... 42 Figure 20 -- Newer, well-kept homes typical of the Ellet Neighborhood...... 42 Figure 21 -- Kenmore Business District...... 44 Figure 22 -- Kenmore Neighborhood ...... 45 Figure 23 -- Merriman Valley Neighborhood ...... 47 Figure 24 -- The Merriman Valley Neighborhood...... 48 Figure 25 -- The Goodyear Heights Neighborhood...... 50 Figure 26 -- Goodyear Heights Neighborhood ...... 51 Figure 27 -- Firestone Park Neighborhood ...... 52 Figure 28 -- Well maintained properties viewed from Firestone Park...... 53 Figure 29 -- Highland Square Retail Area...... 55 Figure 30 -- Highland Square Neighborhood ...... 56 Figure 31 -- North Hill Neighborhood ...... 57 Figure 32 -- The North Hill Retail Area (Temple Square)...... 58 Figure 33 -- Wallhaven Neighborhood ...... 59 Figure 34 -- The intersection of Market St. and Hawkins Ave. in Wallhaven ...... 60 Figure 35 -- Chapel Hill Neighborhood ...... 62 Figure 36 -- Recently renovated Chapel Hill mall...... 63 viii

Figure 37 -- Modest homes typical of the Chapel Hill neighborhood...... 63 Figure 38 -- Rolling Acres Neighborhood ...... 65 Figure 39 -- The dated and dilapidated Rolling Acres Mall...... 66 Figure 40 -- University Park Neighborhood ...... 67 Figure 41 -- Densely built housing in disrepair typical of the University Park neighborhood. University of Akron buildings are visible in the background...... 68 Figure 42 -- Lane Wooster Neighborhood ...... 71 Figure 43 -- Rt. 59 separates the Lane Wooster neighborhood (visible on the right) from Downtown...... 71 Figure 44 -- A stately home typical of the Fairlawn Heights neighborhood...... 73 Figure 45 -- Fairlawn Heights Neighborhood ...... 73 Figure 46 -- Northwest Akron Neighborhood ...... 74 Figure 47 -- Estate-homes in the affluent Northwest Akron neighborhood...... 75 Figure 48 -- Elizabeth Park Neighborhood ...... 77 Figure 49 -- Newly constructed housing in the Elizabeth Park neighborhood...... 78 Figure 50 -- West Akron Neighborhood ...... 80 Figure 51 -- Affordable housing in the West Akron neighborhood...... 81 Figure 52 -- Suttles' Spatial Scale of Neighborhood ...... 888 Figure 53 -- Example of How Galster Conceptual Model Varies Across Space...... 91 Figure 54 -- Linking Galster’s model to Suttles’ spatial scales of neighborhood ...... 93 Figure 55 -- Framework for Conceptualization (After Adams, Abler and Gould, 1971) ... 95 Figure 56 -- A single congeneric model...... 98 Figure 57 -- Two Factor Congeneric Measurement Model...... 99 Figure 58 -- Types of Factor Analysis ...... 102 Figure 59 -- Confirmatory Factor Analysis with Corresponding Matrices ...... 107 Figure 60 -- Illustrating House Condition Thresholds ...... 115 Figure 61 -- Congeneric Measurement Model for Structural Component ...... 122 Figure 62 -- Path Diagram of Galster's Conceptual Model of Neighborhood...... 147 Figure 63 -- Spatial Patterns of the Structural Component ...... 160 Figure 64 -- Average Factor Scores by Neighborhood and Genesis for the Structural Component ...... 163 Figure 65 -- Spatial Patterns of Infrastructure Construct ...... 164 Figure 66 -- Average Factor Scores by Neighborhood and Genesis for the Infrastructural Component ...... 165 Figure 67 -- Spatial Patterns of Demographic Construct ...... 166 Figure 68 -- Average Factor Scores by Neighborhood and Genesis for the Demographic Component ...... 167 Figure 69 -- Spatial Patterns of Social Class Construct ...... 168

Figure 70 -- Average Factor Scores by Neighborhood and Genesis for the Class Component ...... 169 Figure 71 -- Spatial Patterns of Tax/Public Service Construct ...... 170 Figure 72 -- Average Factor Scores by Neighborhood and Genesis for the Tax/Public Service Component ...... 172 Figure 73 -- Spatial Patterns of Environmental Construct ...... 173 Figure 74 -- Average Factor Scores by Neighborhood and Genesis for the Environmental Component ...... 175 Figure 75 -- Spatial Patterns of Proximity Construct ...... 176 Figure 76 -- Average Factor Scores by Neighborhood and Genesis for the Proximity Component ...... 177 Figure 77 -- Spatial Patterns of Political Construct ...... 178 Figure 78 -- Average Factor Scores by Neighborhood and Genesis for the Political Component ...... 179 Figure 79 -- Spatial Patterns of Social/Interactive Construct ...... 180 Figure 80 -- Average Factor Scores by Neighborhood and Genesis for the Social Component ...... 181 Figure 81 -- Spatial Distribution of the Typology of Akron Neighborhoods ...... 198 Figure 82 -- Squared Multiple Correlations (R2) for the East Akron Neighborhood ...... 206 Figure 83 -- Squared Multiple Correlations (R2) for Middlebury Neighborhood ...... 207 Figure 84 -- Squared Multiple Correlations (R2) for South Akron Neighborhood ...... 207 Figure 85 -- Squared Multiple Correlations (R2) for Summit Lake Neighborhood ...... 208 Figure 86 -- Squared Multiple Correlations (R2) for Ellet Neighborhood ...... 208 Figure 87 -- Squared Multiple Correlations (R2) for Kenmore Neighborhood ...... 209 Figure 88 -- Squared Multiple Correlations (R2) for Merriman Valley Neighborhood ... 209 Figure 89 -- Squared Multiple Correlations (R2) for Goodyear Heights Neighborhood . 210 Figure 90 -- Squared Multiple Correlations (R2) for Firestone Park Neighborhood ...... 210 Figure 91 -- Squared Multiple Correlations (R2) for Highland Square Neighborhood .... 211 Figure 92 -- Squared Multiple Correlations (R2) for North Hill Neighborhood ...... 211 Figure 93 -- Squared Multiple Correlations (R2) for Wallhaven Neighborhood ...... 212 Figure 94 -- Squared Multiple Correlations (R2) for Chapel Hill Neighborhood ...... 212 Figure 95 -- Squared Multiple Correlations (R2) for University Neighborhood ...... 213 Figure 96 -- Squared Multiple Correlations (R2) for Fairlawn Heights Neighborhood ... 214 Figure 97 -- Squared Multiple Correlations (R2) for Northwest Akron Neighborhood ... 214 Figure 98 -- Squared Multiple Correlations (R2) for West Akron Neighborhood ...... 215 Figure 99 -- Squared Multiple Correlations (R2) for Lane Wooster Neighborhood ...... 215 Figure 100 -- Squared Multiple Correlations (R2) for Elizabeth Park Neighborhood ...... 216

LIST OF TABLES

Table 1 -- Middlebury Neighborhood Profile ...... 32 Table 2 -- East Akron Neighborhood Profile ...... 34 Table 3 -- South Akron Neighborhood Profile ...... 37 Table 4 -- Summit Lake Neighborhood Profile ...... 39 Table 5 -- Kenmore and Ellet Neighborhood Profile ...... 43 Table 6 -- Merriman Valley Neighborhood Profile ...... 46 Table 7 -- Goodyear Heights and Firestone Park Neighborhood Profiles ...... 51 Table 8 -- Highland Square Neighborhood Profile ...... 55 Table 9 -- North Hill Neighborhood Profile ...... 58 Table 10 -- Wallhaven Neighborhood Profile ...... 60 Table 11 -- Chapel Hill and Rolling Acres Neighborhood Profile ...... 65 Table 12 -- University Park Neighborhood Profile ...... 68 Table 13 -- Lane Wooster Neighborhood Profile ...... 70 Table 14 -- Fairlawn Heights and Northwest Akron Neighborhood Profile ...... 75 Table 15 -- Elizabeth Park Neighborhood Profile ...... 79 Table 16 -- West Akron Neighborhood Profile ...... 81 Table 17 -- Components of A Neighborhood (After Galster 2001) ...... 85 Table 18 -- Adaptation of Suttles’ Spatial Scales of Neighborhood ...... 89 Table 19 -- Summary of Matrices Used in Factor Analysis ...... 97 Table 20 -- Collection Level and Measurement Scale for Observed Variables ...... 111 Table 21 -- Genesis and Number of Observations in each Neighborhood ...... 113 Table 22 -- Test of Univariate Normality for Continuous Variables ...... 117 Table 23 -- Test of Multivariate Normality (Before Transformation) ...... 117 Table 24 -- Test of Multivariate Normality (After Transformation) ...... 118 Table 25 -- Recommendations for Acceptable Levels of Model Fit ...... 124 Table 26 -- Empirical Fit of Measurement Models ...... 126 Table 27 -- Measurement Model Fit for All Neighborhoods ...... 127 Table 28 -- Factor Loadings and Significance for Neighborhoods Developed During the Early Expansion of Akron ...... 130 Table 29 -- Factor Loadings and Significance for Neighborhoods Developed Through Annexation ...... 131 Table 30 -- Factor Loadings and Significance for Industrial Neighborhoods ...... 132 Table 31 -- Factor Loadings and Significance for Neighborhood Retail Centers ...... 133 Table 32 -- Factor Loadings and Significance for Large Retail/Service Neighborhoods . 134 Table 33 -- Factor Loadings and Significance for Neighborhoods Defined by Socio- economic Status ...... 135 xi

Table 34 -- Factor Loadings, Standard Errors and Significance of Measurement Models for the City-wide Model ...... 139 Table 35 -- Fit Indices for Neighborhood Confirmatory Factor Analysis Models ...... 146 Table 36 -- Most Common Factor Interrelationships ...... 150 Table 37 -- Summary of Factor Covariances by Neighborhood ...... 154 Table 38 -- Squared Multiple Correlation Coefficients for Measurement Variables ...... 159 Table 39 -- Fit of Measurement Models by Neighborhood Genesis ...... 186 Table 40 -- Extent of Significant Correlation among Factors ...... 188 Table 41 -- Percentage of Neighborhood Parcels Falling into each Cluster...... 193 Table 42 -- Typology and Genesis of Akron Neighborhoods ...... 194 Table 43 -- Selected Socio-Economic Variables for Neighborhood Groups...... 196

xii

ACKNOWLEDGMENTS

A la bella memoria de Iraida Galdón Soler ( 23 de Noviembre de 1978 – 5 de

Febrero de 2006). La tierra no tiene lamentos que el cielo no pueda sanar.

My sincerest appreciation to my advisor, Dr. Milton Harvey. Your steadfast professionalism, keen intellect, encouragement and patience provided a solid foundation even during the rough spots of my research.

Drs. Mandy Munro-Stasiuk and Scott Sheridan, your focus on the geography and patience with all of the numbers has made my research more meaningful. Thank you for helping me rise above a sea of numbers and encouraging me to focus on the broader geographical message.

xiii

Chapter 1

Consequences of Metropolitan Fragmentation: The Neighborhood Concept

Introduction

As observed by Knox and Pinch (2000, p. 136) “modern metropolitan areas are characterized by a complex partitioning of space into multi-purpose local government jurisdiction and a wide variety of special administrative districts.” One such multi- purpose metropolitan partitioning is the urban neighborhood. Briefly defined, neighborhoods are “territories containing people of broadly similar demographic, economic, and social characteristics but without necessarily displaying of close community interaction” (Knox & Pinch, 2000, p. 416). Akron, Ohio consists of 21 neighborhoods with differing historical trajectories, and mixtures of racial/ethnic, economic, and social attributes. The aim of this dissertation is to extent the understanding of neighborhood by empirically evaluating the conceptualization by

Galster. His conceptualization will be evaluated using the neighborhoods in Akron,

Ohio. He conceptualizes neighborhood as a complex commodity (i.e. something that is produced and consumed). This complex commodity is comprised of ten component quantities which are described in detail in Chapter 3.

Since neighborhoods are spatially bound, they are examples of regional units. In the next section types of regions delimited, and used by geographers are briefly discussed (Haggett, 1972). This is followed by a detailed discussion of the neighborhood concept. Finally the specific goals of the dissertation are articulated.

Regions

Regionalization is the process of dividing larger geographic areas into smaller units for more detailed analysis and understanding. Following the period of Great

Exploration, circa 250 B.C. to the 1800s, regionalization was the primary focus of geography. The early explorers gathered vast amounts of information describing and locating features of the Earth. Between 1800 and the 1950s, geographers set about grouping and classifying areas of the Earth into regions in order to lend meaning to the vast amounts of information previously gathered (Abler, Adams & Gould, 1971). This regional approach to geographic thought was dominant until the quantitative revolution of the 1950s but still has a strong presence in the discipline today.

Harvey (1969) likens the role of the region in geography to the role of the atom in the physical sciences – a theoretical entity that cannot be seen but whose presence can be determined from its effects. Despite much historical opposition to this regional approach at the time, Haggett, Cliff and Frey (1977, p. 451) argued that “regions perform much the same role as a class in any science and that regionalization may be approached as a special form of classification.”

Three major types of regions can be defined for geographic space; uniform, functional and planning (Haggett et al., 1977). Each type will be briefly discussed.

Uniform Regions

Uniform regions are areas which are homogenous in one or more attributes.

Hagget et al. (1965, p. 451) define uniform regions as “contiguous areas within which, conditional upon the purpose for which the region is being defined, place-to-place variations may be regarded as trivial.” Examples of uniform regions in geography include physical regions based on topography, cultural regions based on language or religion and social regions based on ethnicity or race. Uniform regions are primarily descriptive in nature and address the form of the area under study. Examples of formal delineations of uniform regions appear as early as 1905 when Herbertson delineated ecoregions of the United States. Austin (1972) delineated land resources of the conterminous U.S. to aid in decision making for allocation of resources for agricultural production. A contemporary and widely used example of uniform regions is the United

States Department of Agriculture’s plant hardiness map which divides the United States into 11 regions based on average annual minimum temperature (USDA, 2007).

Examined from the perspective of political geography a city could be a uniform region because it falls under a single political jurisdiction.

Functional Regions

Functional or nodal regions are organized around a central node or focal point with the surrounding areas linked to the central node through associations and activities

(Haggett et al., 1977). Unlike uniform regions, nodal regions may be non-contiguous and overlapping. Examples of nodal regions in geography include commuting catchment areas in transportation geography, trade areas in marketing geography and watersheds in physical geography. Nodal regional may or may not be homogeneous throughout but are defined based on the functional relationships in the study area.

Classical examples of the delineation of functional regions include Berry (1966) where, through cartographic analysis, he delineated new functional regions based on the flow of 63 commodities across 36 regions in India and Berry and Garrison (1958) who delineated trade areas in Snohomish County, Washington using accessibility measures derived from local transportation routes. Harvey (1972) used a hierarchical clustering scheme to identify development regions in Sierra Leone.

Planning Regions

Although not as prominent in geography as the uniform or nodal region types,

Haggett et al. (1977) identify a third type of region, the planning region. These authors define a planning region as an “area, contiguous or non-contiguous, delimited on an ad hoc basis for purposes of administration or organization” (p. 453). The delineation of planning regions is a balancing act between recognizing naturally occurring uniform or

functional regions while still fulfilling the administrative or organizational purpose of creating the region. U.S. Census Metropolitan Statistical Areas (MSA) are an example of planning regions. MSAs contain a uniform region – typically a central city containing at least 50,000 people – and a functional region where there is a high degree of economic and social integration with that core (US Census Bureau, 2007). The US Census Bureau integrates the uniform and function regions to delineate larger areal units for the purpose of gathering census data. Neighborhood itself is another example of a planning region. Keating and Krumholz (2000) report that urban renewal and revitalization programs have been operationalized at the neighborhood scale for over one hundred years.

Fluidity Among Approaches to Regions

A thoughtful examination of the concept of planning regions implies that formal and functional regions are interrelated. Although separating form and function for academic purposes is useful, Cohen and Lewis (1967, p. 1) state that “few geographers would question the thesis that form and function are so thoroughly interrelated that each has to be adjudged in the light of its being a product of or direct response to the other.” Eichenbaum and Gale (1971, p. 526) argue that form and function “taken individually, they each admit only an intangible and restricted portion of reality” and that “form itself provides a measurable geometric description of phenomena at a given instant in time. However, when form and function are taken together as a composite

form-function, we are able to go beyond this description and define spatial qualities with reference to characteristic traits.” Symanski and Newman (1973) take great exception to the popular dichotomy between uniform and function regions. They equate this dichotomy to the long standing process-product debate in the discipline of philosophy. This debate interpreted from a geographical perspective views functional regions as the process and uniform regions as the product of some process. Blaut

(1961) argues that “every empirical concept of space *uniform region+ must be reducible by a chain of definitions to a concept of process *functional region+” (p. 2). Blaut’s strong argument that “structures of the real world are simply slow processes of a long duration” (p. 2) further supports the assertion that there is a strong interdependence between formal and function regions. Nowhere else in urban geography is the interrelationship between form, function and planning more apparent than in the complex mosaic of urban residential areas known as neighborhoods.

The Neighborhood Concept

While there is little broad agreement on the concept of neighborhood, few geographers would contradict the idea that neighborhood is a function of the interrelationships between people and the physical and social environments” (Knox & Pinch,

2000, p. 8). Soja (1980, p. 211) coined the term sociospatial dialectic for this phenomenon where “people create and modify urban spaces while at the same time being conditioned in various ways by the spaces in which they live and work.” This

codependence between the spatial and social aspects of neighborhood is arguably one of the main reasons why the concept is so difficult to define. One rather lucid explanation was advanced by Blowers (1973). Blowers conceptualizes neighborhood not as a static spatial entity but as existing along a continuum yielding five neighborhood types (Figure 1). Proceeding left to right in the continuum additional characteristics or dimensions are cumulatively added yielding more complex neighborhoods.

Neighborhood Type Arbitrary Physical Homogeneous Functional Community

Territory

Environment

acteristics acteristics Social Group

Functional or Dimensions or Interaction

Social Common Char Common Interaction Figure 1 -- The Neighborhood Continuum (Blowers 1973)

At the far left of the continuum lies the arbitrary neighborhood. Blowers (1973, p.55) describes these neighborhoods as having “no integrating feature other than the space they occupy.” At best these neighborhoods may be known as vernacular areas of the city but have few homogeneous qualities and exhibit low social interaction. The borders of arbitrary neighborhoods are often ill defined.

The next stage of the continuum is the physical neighborhood. The boundaries of physical neighborhoods are delineated by natural or built barriers such as major roads, railways, waterways or large tracts of non-residential land use (e.g. industrial parks, airports, etc.). The inhabitants residing within the boundaries of a physical neighborhood may share few characteristics in common. Blowers cautions that occupying the same physical area does not automatically imply a high degree of social interaction.

Perhaps the most familiar type of neighborhood in Blowers typology is the homogeneous neighborhood. These neighborhoods have distinct spatial boundaries and the residents share common demographic, social or class characteristics. The genesis of the homogeneous neighborhood concept is the Chicago school of urban sociology.

Based on concepts borrowed from plant and animal ecology, the Chicago school viewed the community as a result of competition for scarce land resources. This competition served to “segregate people and their businesses into relatively homogeneous residential and functional sub-areas within the community” (Greene & Pick 2006, p.12).

Another familiar type of neighborhood in Blower’s continuum is the functional neighborhood. Like any functional region in geography these neighborhoods are organized around a central node or focal point with the surrounding areas linked to the central node through associations and activities (Haggett et al., 1977). Blowers (1973, p. 59) describes these neighborhoods as “functional areas are those within which activities such as shopping, education, worship, leisure, and recreation take place.”

The final neighborhood at the right of Blowers’ continuum is the community neighborhood. A fair amount of confusion exists in the literature between the terms community and neighborhood. Chaskin (1997, p. 522) defines neighborhood as “clearly a spatial construction denoting a geographical unit in which residents share proximity and the circumstances that come with it.” He concedes that communities are place based but that they also “are units in which some set of connections is concentrated, either social connections (as in kin, friend or acquaintance networks), functional connections (as in the production, consumption, and transfer of goods and services), cultural connections (as in religion, tradition, or ethnic identity), or circumstantial connections (as in economic status or lifestyle) (Chaskin, 1997, p. 522). Blowers (1972, p. 60) sees the community neighborhood as “close-knit, socially homogeneous, territorially defined group engaging in primary contacts.” Blower (1972, p, 61) contends that the community neighborhood can be seen as a culmination of the characteristics of the preceding neighborhood types on the continuum by stating that “the distinctiveness of the geographical environment, the socio-economic homogeneity of the population, and the functional interaction that takes place will contribute to the cohesiveness of the community neighborhood.”

Blowers’ conceptualization of neighborhood is illustrative and provides a flexible tool (i.e. the continuum) that aids in the understanding of neighborhood. However, some would argue if the concept of neighborhood even matters in an increasingly globalizing society. Forrest and Kearns (2004, p. 2126) state the purported impact of

the information/technological age on neighborhood in this way, “new virtuality in social networks and a greater fluidity and superficiality in social contact are further eroding the residual bonds of spatial proximity and kinship.” Despite Forrest’s argument, there is strong evidence that the concept of neighborhood is of vital interest to the academic community, planners and the public as a whole. A recent special issue of Urban Studies

(2001) was devoted to advancing the understanding of neighborhood. From the public perspective, an entire community planning and development perspective known as new urbanism has developed to combat “the spread of placeless sprawl, increasing separation by race and income, environmental deterioration, loss of agricultural lands and wilderness, and the erosion of society's built heritage” (Congress of the New

Urbanism, 2007). New urbanism development guidelines suggest specific building practices such as mixed land use, human scaled communities, and even front porches on residential structures all aimed at recreating the nostalgic neighborhood. Through all of this contestation surrounding neighborhood one point is clear, neighborhood is a highly complex concept. This dissertation will advance the understanding of neighborhood not by producing a narrow and rigid definition but will apply a conceptual model to neighborhood which incorporates components present in almost all extant definitions.

Goals of the Dissertation

This dissertation has three primary goals. First, a recently published (Galster, 2001) complex conceptual model of the degree of presence of neighborhood will be evaluated empirically. Several scholars (Galster, 2001; Martin, 2003; Kearns & Parkinson 2001) have lamented over the ambiguity of neighborhood and the lack of a meaningful methodology for bounding it. Galster (2001) has proposed a highly flexible conceptual model for what constitutes a neighborhood. Further, this model is intrinsically spatial in that Galster states “in my definition is the notion that the type and even existence of neighborhoods can and often does vary across urban space” (Galster, 2001, p. 2113). To date, no empirical application of Galster’s conceptual neighborhood model appears in the literature. This dissertation will verify Galster’s model using an empirical methodology designed specifically for testing conceptual models.

Galster states “while most of the attributes *of the model+ are present to some extent in all neighborhoods, the quantity and composition of constituent attributes vary dramatically across neighborhoods within a single metropolitan area” (Galster, 2001, p

2113). The second aim of this research is to determine which specific components

(structural, demographic, political, etc.) of Galster’s model vary across the 21 vernacular neighborhoods in the study area. This goal will be achieved by applying a covariance analysis. Additional explanation and justification of this methodology is provided in the results chapters of the dissertation.

Although this research is primarily methodological in nature, Galster’s model should lead to meaningful and unambiguous analyses of neighborhoods in an actual case study. The third goal is to evaluate the empirical results of Galster’s conceptual model in Akron, Ohio in order to advance the understanding of the urban geography of the city.

Organization of the Dissertation

This dissertation is divided into four parts. Part One consists of three chapters

(Chapters 2-4) and provides the reader with background on the theoretical, conceptual and methodological approaches employed in this research. Part Two contains two chapters (Chapters 5 and 6) and discusses the data used in the dissertation. Part Three consists of three chapter (Chapters 7-9) which present the results of the research. Part

Four (Chapters 10-12) summarizes the research, extends the conceptual model used and identifies areas of future research.

Chapter 2

History and Evolution of Akron Neighborhoods

Introduction

The empirical verification of the concept of neighborhood was based on the vernacular neighborhoods of Akron, Ohio. Vernacular neighborhoods are those areas of the city that are widely known and recognized by the residents. Akron was selected as the study area for this research because it is a well-established and stable Midwestern city with several easily recognizable vernacular neighborhoods. Also, Akron has the high-quality and easily accessible digital cadastral data necessary for an empirical evaluation of neighborhood. Situated in the heart of Northeastern Ohio, Akron is the seat of Summit County (Figure 2). Unlike many other medium sized cities in the region,

Akron is making a successful transition from a manufacturing economy to an information and service economy. In order to provide the reader with background to place the results of this research in context, a summary of both the historical and contemporary geographical context of Akron is provided. The chapter begins with a brief history of the city. Next, the broad contemporary land use patterns are discussed and profiles are presented for each of the vernacular neighborhoods.

Figure 2 -- Akron in its Regional Context

A Brief History of Akron

The Canal Era

On February 4, 1825 the state legislature authorized the construction of a waterway to connect Lake Erie with the Ohio River – the Ohio Erie Canal. Ohio was sparsely populated and lacked a sufficient market for its agricultural products. The Erie

Canal built across the State of New York was nearing completion at this time. It would connect Lake Erie with the Hudson River and the Port of New York (Grismer, 1952).

Construction of the Ohio canal would provide a much needed boost to the Ohio economy by providing access to the more densely populated markets in the East.

Although several routes for the canal were proposed, Cleveland on Lake Erie and

Portsmouth on the Ohio River were selected as termini. Placing a terminus at Cleveland would allow canal builders to utilize the Cuyahoga and Tuscarawas Rivers. The proximity of these two rivers was not a new discovery. Prior to European settlement native Americans would portage their canoes across an eight mile stretch of land known today as the Portage Path. The exact path of the canal between the Cuyahoga and

Tuscarawas was not specified by the legislature. A political battle between a wealthy land speculator, General Simon Perkins, and the existing town of Middlebury ensued.

Residents of Middlebury fought to have the path of the canal follow the Little Cuyahoga

River and flow through Middlebury, whereas General Perkins lobbied for the canal to flow through Summit Lake. The Summit Lake route would require the canal to flow through large tracts of land owned by General Perkins. General Perkins prevailed and

realized that topography would play an important part in the value of his land. His land was immediately north of Summit Lake in South Akron (Grismer, 1952). Summit Lake would be the highest point on the canal, some 395 feet above Lake Erie. Forty-one locks would be required for boats to descend from Summit Lake to Lake Erie. Seventeen of these locks would be required in just the first two miles of the canal as it descended north from Summit Lake to the Little Cuyahoga River valley. Passage of canal boats through locks is a time consuming process. General Perkins saw tremendous business opportunities to make money because canal passengers are delayed while their boats pass through the locks. Hoping to capitalize on this opportunity, General Perkins and a business partner commissioned a plan for a new town on the proposed path of the canal -- Akron. The town plan was recorded in Portage County on December 6, 1825.

Water power and water transportation both continued to play an influential role in Akron’s growth. In 1833 a hydraulic race, a means of harnessing water power, was built from Middlebury to present day downtown Akron to provide power for what would become an important economic activity for Akron – the milling of flour. On April

4, 1840 a “cross-cut” canal was opened from South Akron, through Middlebury to

Beaver, Pennsylvania thus further opening lucrative markets in the east (Grismer, 1952).

Situated at the intersection of two major water transportation routes and an abundant supply of water power, created a positive climate for manufacturing and Akron prospered and grew during this period. The production of farm machinery, clay products, cereal and oatmeal created a strong manufacturing base for the young city.

Industrialized Akron

The next major chapter in Akron’s economic development occurred in November

1870 with the arrival of B.F. Goodrich from New York City. He was a medical doctor turned businessman. He owned a rubber products plant in New York. Competition was heavy in the East for Goodrich so he decided to find a new location for a plant.

Goodrich had attended medical school in Cleveland and had learned about Akron through a Board of Trade pamphlet. Heavily financed by the local Akron business community, Goodrich opened the first rubber manufacturing facility in Akron on the present day site of the Goodrich plant. Goodrich’s eventual success was followed by

Frank Seiberling’s who established the Goodyear Tire & Rubber Company in 1898 and

Harvey Firestone who founded the Firestone Tire & Rubber Company in 1900. Indeed,

Akron’s history was dominated by the tire and rubber industry until the 1970s when global competition, labor issues and regional inversion resulted in much of the tire manufacturing facilities relocating overseas or the southern United States.

Akron’s industrial past has resulted in interspersed residential and industrial land uses in many parts of the city. The next section summarizes the broad land use patterns present in modern day Akron in order to provide the reader with a context for understanding the neighborhood profiles presented at the end of this chapter.

Akron’s Current Land Use Patterns

Geographers often use three models to describe the land use patterns within a city; concentric zone, sector and multi-nuclei. Patterns produced by all three models are apparent in the city. In the concentric zone model, cities grow in uniform concentric rings outward from a central business district (CBD) except where topographic or other barriers occur (Figure 3). This pattern results because the CBD is the most valuable land in the area and locating near it brings economic benefits. This model of land use was

Central Business District

Zone in transition

Zone of workingmen's homes

Residential zone Commuter zone

Figure 3 -- Concentric zone model (After Park, Burgess & McKenzie, 1925)

advanced by an early member of the Chicago school of urban sociology William Burgess

(1925). Burgess described five concentric zones in his model. At the center is the central business district made up of retail stores, office buildings, banks, theaters and museums (Cadwallader, 1985). The second zone known as the zone of transition

contains an inner area of wholesale businesses and warehouses and an outer area of deteriorating residential housing stock. At least in the case of 1920’s Chicago, the time and study area where Burgess formulated his model, this second zone was the area where immigrants settled. The third concentric ring is the zone of workingmen’s homes and was characterized by second-generation immigrants who were able to move from the poorer transitional zone. The fourth zone was made-up of traditional single-family dwellings with yards and was home to the middle class. The final zone in Burgess’ model is the commuter zone and was outside the city limits. Although the concentric zone model has been criticized for being too simplistic, Ford (1972, p. 59) argues that “it has played an important part in the creation of consensus mental maps of the city

(‘inner city’, ‘suburban ring’, etc.).” Aspects of the concentric zone model are apparent in Akron. Downtown Akron, the CBD, is located at the geographical center of the city and until urban renewal projects in the 1950s was flanked by factories and warehouses.

Contemporary downtown is surrounded by a ring of inferior residential housing typical of the zone of transition. Housing age decreases and housing quality increases as distance from downtown increases.

Land use patterns typical of the sector model are also apparent on the Akron landscape. In the sector model land use patterns are characterized by wedges radiating from the central business district (Figure 4). The sector model is an urban morphology advanced by the work of Homer Hoyt (1939). In this model, the primary process influencing the structure of the city was the price competition for land. Hoyt’s sector

model was based on the analysis of real property inventories at the parcel level from 64 urban centers across the United States (Abu-Lughod, 1991). Hoyt’s research was conducted not in an academic environment but at the Federal Housing Authority (FHA).

Beauregard (2007, p. 257) highlights the importance of Hoyt’s model by stating “this understanding of real estate markets and growth patterns enabled city planners to take into account uneven development, connect land use and transportation planning, and predict the future path of investment and blight. Most important, it provided a scientific way to conceptualize the city, thereby giving planners the ability to “see” it as organized in a logical fashion.” Abu-Lughod (1991, p. 206) summarizes the sector model this way,

“once a small section of the inner city has been selected by the wealthy, this high- income area radiates outward into zones of newer construction when the city grows.”

Low-income and industrial land uses grow in a similar fashion. in the case of industrial land uses, the growth often occurs along river valleys and railways. In Akron, these wedges patterns are clear in three "spokes" of industrial land use (Figure 55) which extend from the CBD southwest to Barberton, east to Mogadore and northward to

Cuyahoga Falls (Noble, 1975).

1900 1915 1936

Figure 4 -- Example of Hoyt's sector model for Minneapolis, MN.

Figure 5-- Akron Land Use Patterns

The final model used to describe land use patterns, the multi-nuclei model, is more useful for describing contemporary land use patterns in Akron. Both the concentric ring and sector models assume one center of growth for a city around which various land use patterns form. In the multi-nuclei model, there are many nodes of commercial, retail and residential activities around which growth can occur. Developed by Harris and Ullman (1945) as a logical extension to the existing concentric zone and sector models, the multi nuclei model accounts for the expansion and resulting conflict among various land uses in the city. The internal structure of a city in the multiple nuclei model can be generalized into the following categories of land use: central business district, wholesale light manufacturing, low-class residential, medium-class residential, high-class residential, heavy manufacturing, outlying business district, residential suburb, industrial suburb and commuter zone (Figure 6). Harris and Ullman

(1945, p. 14) outline four factors which contribute to the development of separate nuclei. First, certain activities require specialized facilities such as adequate transportation accessibility for retail activities or water resources for some types of manufacturing. Second, certain like activities group together because they profit from cohesion such as agglomerations of retail activities to attract more customers or concentrations of financial services for increased collaboration. Third, certain unlike activities are detrimental to each other such as manufacturing land uses and high-class residential land uses in close proximity. Finally, certain activities are unable to afford the high rents of the most desirable sites. The clearest example of the multiple nuclei

model in Akron is the agglomeration of retail activities in several areas of the city.

Examples of strong neighborhood retail centers in Akron include the Chapel Hill Mall area, Waterloo Road and West Akron.

1. central business district 2. wholesale light manufacturing 3. low-class residential 4. medium-class residential 5. high-class residential 6. heavy manufacturing 7. outlying business district 8. residential suburb 9. industrial suburb 10. commuters’ zone

Figure 6 -- Multiple Nuclei model of the internal structure of a city. (After Harris & Ullman, 1945, p. 13)

The current land use patterns in Akron can be linked to the types of regions presented earlier in this chapter. The patterns produced by the concentric zone model, radiating rings of newer housing stock, can be considered uniform regions. They are relatively homogeneous on housing age. The land use patterns produced by the concentric zone model, spokes of industrial land use extending from the center of the city, can be considered functional regions. They are centered around the downtown and have a common association or function (i.e. industrial activities). However, neither of these regionalizations would meet the need for political and/or administrative regions within the city. The construction of political/administrative uniform/functional

regions is a basic requirement for efficient public administration. However, the third type of region presented earlier, the planning region, could serve to divide the city into useful functional-uniform regions. Recall that a planning region is defined as an “area, contiguous or non-contiguous, delimited on an ad hoc basis for purposes of administration or organization” (Haggett et al., 1977, p. 453). The municipal Division of

Planning has created a regionalization for the city by defining 21 vernacular neighborhoods (Figure 7).

Figure 7 -- Akron Neighborhoods as defined by the City Planning Commission

Although there may not be universal agreement on the names or even existence of these government defined areas, several of them are well established in the common vernacular and are easily identifiable by almost all Akronites. The neighborhoods, as defined by the City of Akron Planning Department, are; Chapel Hill, Downtown, East

Akron, Elizabeth park Valley, Ellet, Fairlawn Heights, Firestone Park, Goodyear Heights,

Highland Square, Kenmore, Lane Wooster, Merriman Valley, Middlebury, North Hill,

Northwest Akron, Rolling Acres, South Akron, Summit Lake, University, Wallhaven and

West Akron. A brief introduction to the location and demographics of these neighborhoods is provided next.

Neighborhood Profiles

The neighborhoods in the city of Akron are de facto regions. There is no definitive description of how or when they came into existence. The residents of the city simply agree that they exist. However, some of the neighborhoods share common thematic or evolutionary characteristics which hint at how these specific vernacular neighborhoods may have formed their identities. These common genesis are; (a) original settlement and expansion, (b) annexation, (c) industrial neighborhood development, (d) neighborhood business centers, (e) retail/service centers and (f) socio- economic status. The following neighborhood profiles are organized around these six categories.

A. Original Settlement and Early Expansion [EE]

Downtown Akron marks the spot of the original settlement of Akron. From this origin, the city slowly expanded as the canal fostered growth in the area. This early expansion of the city encompassed or encroached on areas where the contemporary neighborhoods of Middlebury, East Akron, Summit Lake and South Akron are located.

1. Downtown

Although not a traditional residential neighborhood, downtown Akron is perhaps the one region in Akron whose boundaries are most clearly defined (Figure 8). It is presented here to show its functional relationship to the more traditional residential areas of the city and to provide context for the other neighborhoods. From its original

Figure 8 -- Downtown Akron.

Figure 9 -- Downtown Akron from the West.

economic boom as a result of its location at the intersection of two prominent water

transportation routes up through the early 1950s, Downtown Akron was the vibrant

retail and banking center of the city. As was the case throughout the Midwest the

processes of urban abandonment and the rise of the suburban shopping mall took its

toll on downtown Akron. In the late 1950s, Downtown Akron was as odd mixture of

failed businesses and abandoned factories (Kent & Butler, 2007). Since then, downtown

has undergone several transformations. Large urban renewal projects in the 1960s

replaced aging factories on Main Street with a modern high-rise office complex, Cascade

Plaza. Private investment in the 1970s changed the skyline of downtown once again

with the addition of the Akron Centre office and retail complex (Figure 9).

Redevelopment efforts are continuing in downtown on a large scale. Specifically, the

area has benefited from a 1.3 billion dollar transformation through the 1990s (Akron

Beacon Journal, 2004, p. 71).

Contemporary downtown Akron is no longer the retail hub of the city but has been redeveloped into a commercial and entertainment center. The attractions now include a baseball park, public parks, performing space and other entertainment venues.

Recently, the city has embarked on projects that make downtown a residential neighborhood. Construction has recently been completed on an up-scale multistory building of 63 condominiums.

2. Middlebury

Long before Akron was platted as a new settlement, the town of Middlebury to the East was a thriving trading center. Adequate water resources and political maneuvering which directed the lucrative canal through Akron led to Middlebury voting for annexation to the growing City of Akron in 1872. This neighborhood still retains the name of the original annexed town. Middlebury is located to the east of the Goodyear

Heights neighborhood and to the west of The University Park neighborhood (Figure 10).

East Market Street and Arlington Street bisect this neighborhood. It is racially diverse with 65% of its population being White and 29% being Black (Table 1). These proportions closely follow the city-wide averages. Large portions of this neighborhood are commercial or industrial. A very large medical facility, Summa Care-Akron City

Hospital, falls within this neighborhood (Figure 11).

Figure 10 -- Middlebury Neighborhood.

The world headquarters of the Goodyear Tire and Rubber Company is also in

Middlebury (Figure 12). Other commercial/industrial establishments include car dealerships, a scrap yard and plastics manufacturing. Middlebury comprises 3.5% of the city’s households. The housing stock is the oldest in the city with the typical house having been built in 1940. Very few new homes have been constructed in the area.

Figure 11 -- Summa Health Systems' Akron City Hospital -- A Major Landmark in the Middlebury Neighborhood.

Figure 12 -- Goodyear Headquarters viewed from the Center of the Middlebury Neighborhood.

Table 1-- Middlebury Neighborhood Profile Middlebury Akron Race White 65.4 % 69.1% Black 28.9% 26.6% Income Median HH income $25,924 $34,563 % Persons in Poverty 26.5% 16.8% Housing Median Year Built 1940 1949 Renters 60.1% 37.3% Average Rent $382 $425 Median House Value $43,678 $79,809 Educational Attainment High School Diploma 38.7% 36.3% Bachelors Degree 5.9% 11.4% Graduate or Professional 6.0% 11.4%

Degree 3. East Akron

East Akron is a large neighborhood comprising over 6% of the total households in the city. It is an ethnically diverse area with almost equal proportions of Whites and

Blacks. It is viewed primarily as a “blue collar” neighborhood with only 3.8% of the residents holding a bachelors degree (Table 2). The median household income for East

Akron is $25,326 which is well below the city average of $34,536. Excluding Downtown

Akron and the transient University of Akron neighborhood, East Akron has one of the highest percentages (27.2%) of people living at or below the poverty line. The western side of the neighborhood is bisected by South Arlington Street (Figure 11) which serves as the hub of the neighborhood and is densely populated with retail and service establishments (City of Akron 2007).

Figure 11 -- East Akron Neighborhood

South Arlington Street is also the social center of the community (Figure 14). It is home to several prominent churches and the heavily utilized East Akron Community Center that provides social and vocation services and organizes a variety of neighborhood activities. Areas in the southeastern section of the neighborhood adjacent to the Akron

Municipal Airport are primarily industrial.

Figure 12 – South Arlington Street -- the Major Retail and Social Center of the East Akron Neighborhood.

Table 2-- East Akron Neighborhood Profile East Akron Akron Race White 47.9% 69.1% Black 47.2% 26.6% Income Median HH income $25,326 $34,563 % Persons in Poverty 27.2% 16.8% Housing Median Year Built 1946 1949 Renters 41.8% 37.3% Average Rent $385 $425 Median House Value $55,133 $79,809 Educational Attainment High School Diploma 42.5% 36.3% Bachelors Degree 3.8% 11.4% Graduate or Professional 3.9% 11.4%

Degree

4. South Akron

Bounded on the north by Interstate-76 and on the west by Interstate-77, the

South Akron neighborhood is the quintessential blue collar neighborhood of Akron

(Figure 15). Like the Middlebury neighborhood, the housing stock in South Akron is the oldest in the city. The vast majority of the homes were built between 1910 and 1930 in response to a severe housing shortage in Akron caused by the rapid expansion of the tire and rubber industry. The racial makeup of the residents very closely follows that of the city as a whole with 67% White population and 26% Black population (Table 3). The poverty rate of South Akron (20.7%) is higher than the city average (16.8%). Although the neighborhood appears to closely match the demographics of the overall city, there is much variation in socio-economic factors within the neighborhood. The northern section of the neighborhood adjacent to Interstate-76 has deteriorated and has a noticeable number of vacant homes (Figure 16). This section is comprised primarily of renter-occupied houses.

Figure 13 -- South Akron Neighborhood

Figure 14 -- Aging, rental property in the northern section of the South Akron neighborhood.

The southern section of the neighborhood is adjacent to the more affluent

Firestone Park neighborhood and properties are better maintained. A large portion of the northwestern corner of the neighborhood is light industry or commercial land use.

Table 3-- South Akron Neighborhood Profile South Akron Akron Race White 67.1% 69.1% Black 25.9% 26.6% Income Median HH income $30,058 $34,563 % Persons in Poverty 20.7% 16.8% Housing Median Year Built 1940 1949 Renters 37.9% 37.3% Average Rent $460 $425 Median House Value $54,633 $79,809 Educational Attainment High School Diploma 38.2% 36.3% Bachelors Degree 6.0% 11.4% Graduate or Professional 5.0% 11.4%

Degree 5. Summit Lake

Summit Lake neighborhood, named for the large natural lake located on the west side of the neighborhood, is Akron’s poorest neighborhood. At over 83 acres in size, Summit Lake is largest body of water within the city limits yet it provides only limited recreational activities (Figure 16). Low-speed boating and fishing are permitted but swimming is prohibited by the state. Although the water quality of Summit Lake has

improved over the past 25 years the sediment in the Lake is highly polluted. The lake was used a dumping ground during Akron’s industrial age.

Figure 15 -- Signs indicate the limited recreational opportunities available from Summit Lake.

Adjacent to the Lake is a group of heavily used baseball fields and the Summit Lake

Community Center. The Ohio & Erie Towpath Trail, a hiking and biking recreational corridor constructed on the towpath of the Ohio and Erie Canal currently ends at

Summit Lake (Figure 18). However, its connectivity to the more popular northern portion of this trail is hampered by having to travel through downtown. Despite these extensive recreational opportunities, the Summit Lake neighborhood remains depressed. The east side of the Lake is home to a large complex of public housing managed by the Akron Metropolitan Housing Authority. Over 36% of the population

lives at or below the poverty line. Other than the transient neighborhoods of

Downtown and the University of Akron, Summit Lake has the lowest median household income in the city ($20,519). Interestingly Summit Lake is the most racially diverse neighborhood in the city. Approximately 43% of the residents are White and 41% are

Black (Table 4). The remainder of the residents reported more than one race in the

2000 Census. The west side of the Lake along Summit Lake Drive has fared much better and is lined with well maintained single family homes.

Table 4 -- Summit Lake Neighborhood Profile Summit Lake Akron Race White 43.6% 69.1% Black 41.3% 26.6% Income Median HH income $20,519 $34,563 % Persons in Poverty 36.2% 16.8% Housing Median Year Built 1943 1949 Renters 58.7% 37.3% Average Rent $346 $425 Median House Value $51,014 $79,809 Educational Attainment High School Diploma 41.6% 36.3% Bachelors Degree 2.1% 11.4% Graduate or Professional 1.9% 11.4%

Degree

Figure 16 -- Summit Lake Neighborhood

B. Annexation Neighborhoods [ANX]

As a result of the demand for rubber and other goods for World War I, Akron experienced unprecedented growth between 1910 and 1920. Severe housing shortages prompted the expansion of city through annexation of surrounding communities. The neighborhoods of Ellet and Kenmore were once self-sufficient municipalities on the outskirts of Akron. Both municipalities petitioned Akron for annexation. Kenmore was annexed in 1928 and Ellet in 1929. Interestingly, both neighborhoods today have very strong identities and residents are more likely to identify themselves as being from Ellet

or Kenmore than from Akron. Although both neighborhoods have strong identities they differ significantly in economic status and character. Although much more recent in

Akron’s history, the formation of the Merriman Valley neighborhood was also influenced through the process of annexation.

1. Ellet

Ellet is physically separated from the rest of the city by an interstate highway and the municipal airport (Figure 17). It has a vibrant commercial area and enjoyed significant housing development through the 1990s as the rest of the city was losing population. One in twelve houses in Ellet was built after 1990 (City of Akron, 2007)

(Figure 20). Median home value in 2000 for Ellet was $87,900, notably higher than the overall city average of $79,809 (Table 5). Ellet is the most racially homogenous neighborhood in the city with 94% of the population being White.

Figure 17 -- Ellet Neighborhood

Figure 18 -- Newer, well-kept homes typical of the Ellet Neighborhood.

2. Kenmore

Once a vibrant and self-sufficient city Kenmore has not fared well as part of the

City of Akron. The neighborhood was bisected by Interstates 277 and 224 in 1970s and a once vibrant commercial district is in disrepair (Figure 20). The median home value in

Kenmore is $66,905 far less than that Ellet (Table 5). Kenmore is also racially homogeneous with 93% of the population being White. This neighborhood is primarily single-family residential with some retail areas centered on Kenmore Boulevard and

Manchester Road (Figure 22). The area between Wilbeth and Waterloo Roads is primarily light industrial or commercial.

Table 5-- Kenmore and Ellet Neighborhood Profile Kenmore Ellet Akron Race White 93.3% 94% 69.1% Black 3.9% 4% 26.6% Income Median HH income $33,106 $36,147 $34,563 % Persons in Poverty 13.1% 8.9% 16.8% Housing Median Year Built 1946 1958 1949 Renters 30.6% 28.3% 37.3% Average Rent $465 $462 $425 Median House Value $66,695 $87,900 $79,809 Educational Attainment High School Diploma 45.4% 41.5% 36.3% Bachelors Degree 6.1% 8.3% 11.4% Graduate or Professional 4.8% 7.1% 11.4%

Degree

Figure 19 -- A portion of the Kenmore Business District showing the lack of patrons and buildings in disrepair.

Figure 20 -- Kenmore Neighborhood

3. Merriman Valley

The northwest Akron Merriman Valley is the fastest growing area of the city and many residents of Akron still think of this area as belonging to the adjoining city of

Cuyahoga Falls. The city boundary is very irregular in the Merriman Valley as a result of piecemeal annexation of Northampton Township in the 1970s and 1980s (Schleis &

Byard, 2005). Northampton was voluntarily annexed by the City of Cuyahoga Falls in

1986 ending any additional annexation by Akron. The Merriman Valley sat undeveloped for the first 140 years of Akron’s history due to its topography. The neighborhood sits

deep in the valley of the Cuyahoga River and is only accessible by three steep roads;

Merriman, Portage Path and Portage Trail (Figure 23).

This topography isolates the Merriman Valley from other city neighborhoods.

The Merriman Valley is home to sprawling condominium and apartment complexes with very few traditional single family homes (Figure 24). The median year built for homes is

1979, the newest in the city. The residents of the valley are predominately white (80%), have a high median household income ($44,000) and are well educated with 25% of the residents holding a graduate or professional degree (Table 6). Over half of the residents

(52%) are renters. These factors contribute to the vernacular title of "yuppie Mecca" for this neighborhood (Schleis & Byard, 2005).

Table 6-- Merriman Valley Neighborhood Profile Merriman Akron Race White 80.1% 69.1% Black 15.9% 26.6% Income Median HH income $44,061 $34,563 % Persons in Poverty 9.4% 16.8% Housing Median Year Built 1979 1949 Renters 52.0% 37.3% Average Rent $583 $425 Median House Value $112,820 $79,809 Educational Attainment High School Diploma 18.1% 36.3% Bachelors Degree 29.6% 11.4% Graduate or Professional 25.1% 11.4%

Degree

Figure 21 -- Merriman Valley Neighborhood

Figure 22 -- The Merriman Valley Neighborhood. Examples of the expansive rental properties prevalent in this neighborhood can be seen in the background.

C. Industrial Neighborhood Development [IND]

The same population pressures and housing shortages which lead to the annexation of surrounding communities also motivated local rubber magnates to build communities of affordable houses for their employees. The construction of these new neighborhoods was necessary in order to attract and retain qualified employees. These neighborhoods are Goodyear Heights and Firestone Park.

1. Goodyear Heights

In 1912, Goodyear was adding 1,400 employees per month but losing 400 to 900 per month primarily due to the overcrowded housing conditions in the city (Frazier,

1994). One such community for example, Goodyear Heights was started in 1910 by

Frank Seiberling, President of Goodyear Tire and Rubber. Phase one of Goodyear’s efforts to provide affordable housing was located just one-half mile from the Goodyear plants and consisted of approximately 200 lots. The community was designed by

Warren Manning an apprentice of the famous landscape architect Fredrick Law

Olmstead, the chief architect of New York’s Central Park. Manning designed the community with curved and sweeping streets following the hilly terrain of the available land (Figure 24). These wide curved streets give Goodyear Heights its unique character today (Figure 26). This phase was quite successful and an additional 350 acres were developed containing 1500 building lots (Frazier, 1994). Contemporary Goodyear

Heights is predominantly a working class neighborhood (Table 7).

Figure 23 -- The Goodyear Heights Neighborhood. Curving streets and well maintained properties are typical in this neighborhood.

Figure 24 -- Goodyear Heights Neighborhood

Table 7-- Goodyear Heights and Firestone Park Neighborhood Profiles Goodyear Firestone Akron Race White 80.4%Hts. 87.5%Park 69.1% Black 16.1% 9.4% 26.6% Income Median HH income $36,744 $37,652 $34,563 % Persons in Poverty 11.6% 10.8% 16.8% Housing Median Year Built 1947 1948 1949 Renters 25.5% 25.3% 37.3% Average Rent $404 $454 $425 Median House Value $70,275 $81,689 $79,809 Educational Attainment High School Diploma 42.8% 39.0% 36.3% Bachelors Degree 8.3% 12.6% 11.4% Graduate or Professional 7.1% 10.6% 11.4%

Degree 52

2. Firestone Park

Seeing the success of his competitor, Harvey Firestone began construction of a six hundred acre residential community of affordable homes called Firestone Park. Built on farmland immediately adjacent to the Firestone factories, the community opened in

1916. The community was built around a sixteen-acre park built in the shape of the

Firestone corporate shield (Figure 27). The majority of homes were built from company plans and any outside plans had to be approved by an architectural review board

(Finley, 1986). This has resulted in Firestone Park having the most consistent architectural style of any Akron neighborhood (Figure 28). Homes were sold as fast as they could be built.

Figure 25 -- Firestone Park Neighborhood

Figure 26 -- Well maintained properties viewed from Firestone Park.

D. Neighborhood Retail Centers [RET]

Three neighborhoods; Highland Square, North Hill and Wallhaven share the trait of having an easily identifiable neighborhood retail center. In the case of Highland

Square and North Hill, these retail centers date to the 1920s and are made up of small, non-chain retail and service establishments. In contrast, the neighborhood retail center in the Wallhaven neighborhood is made up of a mixture of local and national chain retail and service outlets.

1. Highland Square

Highland Square is the most diverse of Akron’s neighborhoods. The neighborhood is centered on a successful business district located at the intersection of

West Market St. and Portage Path (Figure 29). This gentrified neighborhood has a movie theater, coffee shop, banks, restaurants and other small retail within easy walking distance for most residents. Located just west of downtown, this neighborhood is one of the most vibrant in the city (Figure 30). It has some of the few remaining outdoor café dining options in the city and a recently renovated and heavily used public library.

It has a White population of 66%, close to the city average of 69% (Table 8). Residents of Highland Square are well educated with 20% of the residents having Bachelor degrees and 22% having a graduate or professional. Highland Square is a unique mixture of stately homes and apartment buildings from a variety of eras. Many low-rise brick apartment buildings were constructed in Highland Square during Akron’s housing crisis in the early 20th century. In the 1960s and 1970s several mid-rise (8 to 10 floors) buildings were constructed which capitalize on the neighborhoods location on a high hill overlooking downtown. As a result of these two phases of apartment construction, this neighborhood has the highest concentration of multi-story apartments building in the city. Local accounts state that many of these apartment complexes were build on the west side of the city in order to be “upwind” from the industrial pollution of the rubber factories.

Figure 27 -- Highland Square Retail Area.

Table 8-- Highland Square Neighborhood Profile Highland Sq. Akron Race White 66.3% 69.1% Black 29.4% 26.6% Income Median HH income $33,669 $34,563 % Persons in Poverty 14.6% 16.8% Housing Median Year Built 1941 1949 Renters 54.9% 37.3% Average Rent $524 $425 Median House Value $88,313 $79,809 Educational Attainment High School Diploma 26.8% 36.3% Bachelors Degree 20.4% 11.4% Graduate or Professional 22.0% 11.4%

Degree

Figure 28 -- Highland Square Neighborhood

2. North Hill

Once isolated from the central city, the North Hill neighborhood sits atop a large plateau opposite downtown across the Little Cuyahoga River Valley (Figure 29). In 1922 a bridge spanning the river valley was completed resulting in rapid development of this highly buildable area. A small neighborhood retail center, Temple Square, anchors this neighborhood although it has been in decline recently. North Hill once had large Italian and Polish immigrant populations. Remnants of North Hill’s ethnic past can still be seen on the landscape today. The Polish-American Club and Italian American Club are still located in this neighborhood. Prior to annexation of land in the Cuyahoga (Merriman)

Valley, North Hill was the northern most extent of the city limits and is bounded by

Cuyahoga Falls to the North. The Gorge Metropolitan Park and the Cascade Valley

Metropark both border this neighborhood but there is limited access to these recreational assets due to topography. North Hill follows the racial makeup of the overall city with a slightly higher White population (74.5%) than the overall city average

(69%) (Table 9). North Hill is primarily single family residential with some struggling retail areas located along North Main Street, Tallmadge Avenue and Cuyahoga Falls

Avenue (Figure 32). This neighborhood has easy access to "big box" retail in the adjacent City of Cuyahoga Falls and the Chapel Hill neighborhood.

Figure 29 -- North Hill Neighborhood

Figure 30 -- The North Hill Retail Area (Temple Square). Used furniture and appliance stores dominate the retail trade.

Table 9-- North Hill Neighborhood Profile North Hill Akron Race White 74.5% 69.1% Black 21.6% 26.6% Income Median HH income $34,089 $34,563 % Persons in Poverty 14.9% 16.8% Housing Median Year Built 1942 1949 Renters 34.1% 37.3% Average Rent $378 $425 Median House Value $70,345 $79,809 Educational Attainment High School Diploma 42.6% 36.3% Bachelors Degree 8.3% 11.4% Graduate or Professional 7.7% 11.4%

Degree

3. Wallhaven

The Wallhaven neighborhood is centered on a retail district at the intersection of

West Market and Exchange streets (Figure 33). This high density retail district has coffee shops, grocery stores and services but the large arteries of West Market and

Exchange Street intersect here and make pedestrian traffic difficult (Figure 34). The

Wallhaven neighborhood is home to Hardesty Park which hosts a popular local art festival each year. This neighborhood is predominantly White and enjoys some of the highest housing values in the city (Table 10).

Figure 31 -- Wallhaven Neighborhood

Table 10 -- Wallhaven Neighborhood Profile Wallhaven Akron Race White 88.2% 69.1% Black 9.8% 26.6% Income Median HH income $47,759 $34,563 % Persons in Poverty 5.3% 16.8% Housing Median Year Built 1950 1949 Renters 24.1% 37.3% Average Rent $558 $425 Median House Value $112,950 $79,809 Educational Attainment High School Diploma 19.7% 36.3%

Figure 32 -- The intersection of Market St. and Hawkins Ave. in the Wallhaven Neighborhood. Heavy traffic limits pedestrian traffic in this area.

E. Neighborhoods As Large Retail and Service Centers [LRG]

As vernacular areas of the city, Chapel Hill, Rolling Acres and University Park are identified more as commercial and service areas rather than residential neighborhoods.

Rolling Acres and Chapel Hill were both anchored by large shopping malls however the

Rolling Acres mall has since closed. University Park is anchored by and being encroached upon by the expanding campus of the University of Akron.

1. Chapel Hill

During the 1960s Akron experienced widespread urban abandonment while suburbs to the North, Cuyahoga Falls and Stow, blossomed. Since downtown retail was suffering, local developers realized the potential for a climate controlled modern shopping facility on the periphery of Akron (Figure 35). Summit Mall was established in

1966. It was followed a year later by Chapel Hill Mall (Figure 36). Chapel Hill Mall is located atop a large hill in the northeastern corner of the city and has a prominent

250,000 gallon water tower on its property. This tower was mandated by the city in order to provide sufficient water pressure for sprinklers and hydrants in the event of a fire. Many of the homes were developed after the establishment of the mall. Other than the Merriman Valley area, Chapel Hill has the newest housing stock in the city

(Figure 37). Its population is predominantly White (79%) with a median household income of $28,337; below the city average ($34,563) (Table 11).

Figure 33 -- Chapel Hill Neighborhood

Figure 34 -- The recently renovated mall serves as the anchor for the Chapel Hill neighborhood.

Figure 35 -- Modest homes typical of the Chapel Hill neighborhood.

2. Rolling Acres

Following the success of the Chapel Hill project and responding to growth near the southern portion of Akron, the developers of the Chapel Hill project proposed an even larger mall in the southwestern corner of the city (Figure 38). The mall opened in

1975 and drew large numbers of patrons from adjacent cities to the west. Named for the rolling farmland that the mall replaced, Rolling Acres was once the largest regional mall in a five county area (Akron Beacon Journal, 2004). The areas surrounding the mammoth mall were developed into large box retail establishments. Like the Chapel Hill project, development of the mall spawned some contiguous residential development.

Rolling Acres is primarily viewed as a retail area and not as a residential neighborhood.

Only one percent of the city’s housing units are located in this neighborhood. The future of this area for residential purposes is bleak. After several changes in management, the mall has since closed (Figure 39). Renovations of competing malls in the area and concerns about security at the mall after a series of crimes resulted in fewer customers. Current plans to reuse the mall and surrounding vacant big box retail for light industrial and mixed-use are being considered by City Council.

Figure 36 -- Rolling Acres Neighborhood

Table 11-- Chapel Hill and Rolling Acres Neighborhood Profile Chapel Hill Rolling Acres Akron Race White 79.6% 74.4% 69.1% Black 11.2% 21.6% 26.6% Income Median HH income $28,337 $33,284 $34,563 % Persons in Poverty 14.0% 10.8% 16.8% Housing Median Year Built 1962 1961 1949 Renters 44.5% 44.6% 37.3% Average Rent $424 $426 $425 Median House Value $79,600 $69,100 $79,809 Educational Attainment High School Diploma 41.1% 41.2% 36.3% Bachelors Degree 9.0% 7.8% 11.4% Graduate or Professional 8.5% 6.1% 11.4%

Degree 66

Figure 37 -- The dated and dilapidated Rolling Acres Mall.

3. University Park

University Park is not a traditional residential neighborhood. Dominated by the

218 acre University of Akron campus, the vast majority (84%) of residents are transient renters (Table 12). The remainder of the residents are lower-income blue collar workers. Over 90% of the residents of University Park are under 45 years of age. Other than the Downtown area, University Park has the lowest median household income in the city ($17,204). The land use in the University Park area is primarily single-family homes which have been converted to student housing. In recent years, privately developed low rise student housing has been in-filled into the neighborhood. Service

and retail are the dominant land uses along Market Street and Exchange Streets (Figure

40).

Figure 38 -- University Park Neighborhood

Sponsored by a 10 million dollar grant from the Knight foundation, public and private institutions have formed the University Park Alliance (UPA) with the goal of revitalizing the greater University Park neighborhood. The University neighborhood as defined by UPA extends well beyond the area defined by the City of Akron as University

Park (Figure 41).

Figure 39 -- Densely built housing in disrepair typical of the University Park neighborhood. University of Akron buildings are visible in the background.

Table 12-- University Park Neighborhood Profile University Akron Race White 69.0%Park 69.1% Black 21.2% 26.6% Income Median HH income $17,204 $34,563 % Persons in Poverty 46.0% 16.8% Housing Median Year Built 1946 1949 Renters 84.1% 37.3% Average Rent $427 $425 Median House Value $51,500 $79,809 Educational Attainment High School Diploma 24.1% 36.3% Bachelors Degree 19.4% 11.4% Graduate or Professional 17.7% 11.4%

Degree

F. Neighborhoods based on Socio-Economic Status [SES]

The last thematic category of Akron neighborhoods is based on a common theme for the delineation of neighborhoods, socio-economic or class status. The neighborhoods of West Akron, Elizabeth Park and Lane Wooster have lower socio- economic status when compared to the city as a whole. The Fairlawn Heights and

Northwest Akron neighborhoods have notably higher socio-economic status.

1. Lane Wooster

The Lane Wooster neighborhood is adjacent to downtown and extends westward along Wooster Road (Figure 42). This neighborhood is racially homogenous where 87% of the population is Black. It has always been a predominately Black neighborhood and was usually the first place where Blacks settled when they moved to

Akron from the South in the early 20th century (City of Akron, 2007). The area has not benefited from adjacent downtown renewal projects because it is separated by an underutilized 2.24 mile long highway known as the Innerbelt (SR 59) (Figure 43). It was constructed in the 1970s and was originally intended to serve as a bypass around the congested interchange of two major interstates near downtown Akron. It displaced nearly 3,000 families (Byard & Schleis, 2003). The neighborhood is anchored by a large city-owned track and athletic complex – Lane Field. The neighborhood houses Stewart

Elementary, the only African-centric school in Northeast Ohio (Akron Public Schools,

2007). Although primarily residential there are concentrations of commercial/retail

properties along Wooster Avenue to the west and Exchange Street to the east (Figure

42). The neighborhood is bisected by Wooster Avenue which was recently renamed to

Vernon Odom Boulevard in honor of the late Akron civil rights leader. The area has some of the lowest property values in the city with a median home value of $45,391 significantly less than the city average of $79,809 (Table 13). Lane Wooster’s dominant age group is children under 18 and nearly half of the homes are renter occupied.

Table 13– Lane Wooster Neighborhood Profile Lane Wooster Akron Race White WooWooster9.7% 69.1% Black 87.0% 26.6% Income Median HH income $21,700 $34,563 % Persons in Poverty 31.8% 16.8% Housing Median Year Built 1947 1949 Renters 48.8% 37.3% Average Rent $325 $425 Median House Value $45,391 $79,809 Educational Attainment High School Diploma 36.4% 36.3% Bachelors Degree 2.8% 11.4% Graduate or 1.8% 11.4%

Professional Degree

Figure 40 – Lane Wooster Neighborhood

Figure 41 -- Rt. 59 separates the Lane Wooster neighborhood (visible on the right) from Downtown.

2. Fairlawn Heights

In stark contrast to the Lane Wooster neighborhood, the “far-Westside” neighborhoods of Fairlawn Heights (Figure 45) and Northwest Akron (Figure 46) are the most affluent areas of the city. They differ only in their degree of affluence. Both neighborhoods are situated near West Market Street which provides easy access to retail and services as well as easy access to a regional shopping mall in the adjacent city of Fairlawn. Residents of the far-Westside neighborhoods are predominantly White with Fairlawn Heights being 91% white, Northwest Akron 85% White and Wallhaven

88% White (Table 14). Fairlawn Heights has the highest median household income

($61,216) in the city closely followed by Northwest Akron ($59,057). Similarly these neighborhoods enjoy the highest property values in the city (Figures 44 and 47). City- wide the median house value is $79,809 which pales in comparison to $179,025 for

Fairlawn Heights, $147,320 for Northwest Akron. These neighborhoods lead the city in educational attainment with large portions of the residents having a graduate or professional degree – Fairlawn Heights 41.9% and Northwest Akron 38.8%. These numbers far exceed the overall city average of 11.4%.

Figure 42 -- A stately home typical of the Fairlawn Heights neighborhood.

Figure 43 -- Fairlawn Heights Neighborhood

3. Northwest Akron

The Northwest Akron neighborhood is bordered to the north by the Sand Run

Metropark and is primarily made up of single-family homes however there are a large number of estate homes along Merriman Road and Portage Path (Figure 46). Northwest

Akron is served primarily by the Firestone Cluster of schools which has the reputation of being the best cluster of schools within the city.

Figure 44 -- Northwest Akron Neighborhood

Table 14 -- Fairlawn Heights and Northwest Akron Neighborhood Profile Fairlawn Northwest Akron Race White 91.1%Hts. 85.0% 69.1% Black 4.4% 12.4% 26.6% Income Median HH income $61,216 $59,056 $34,563 % Persons in Poverty 6.8% 4.2% 16.8% Housing Median Year Built 1957 1957 1949 Renters 10.0% 16.2% 37.3% Average Rent $381 $421 $425 Median House Value $179,025 $147,320 $79,809 Educational Attainment High School Diploma 12.4% 14.7% 36.3% Bachelors Degree 30.7% 31.5% 11.4% Graduate or 41.9% 38.8% 11.4%

Professional Degree

Figure 45 -- One of many estate-homes in the affluent Northwest Akron neighborhood.

4. Elizabeth Park Valley

Located along the valley of the Little Cuyahoga River (Figure 48), the Elizabeth

Park neighborhood is the site of successful historic and present day federally subsidized housing projects. In the 1940s, Elizabeth Park was a segregated and dilapidated slum.

Occupied almost exclusively by Blacks, the majority of the slums were razed to build a

276 unit housing development along the Little Cuyahoga River which opened in 1940.

The development almost immediately achieved 100 percent occupancy and there was demand for even more units (Grismer, 1952). This extensive home construction was made possible in large part by the federal government. In order to alleviate the decline in the housing market caused by the Great Depression, the federal government passed legislation to assist local communities in clearing slums and providing low cost housing to the poor (Grismer, 1952). The Akron Metropolitan Housing Authority (AMHA) was formed in 1938 to tackle these goals. The Elizabeth Park neighborhood was the AMHA’s first project.

Figure 46 -- Elizabeth Park Neighborhood

In 2005, the AMHA was awarded a 50 million dollar grant, a HOPE VI project, to replace the high-density deteriorating buildings with single family homes, market rate apartments and 112 subsidized housing units (Schleis & Byard, 2005). HOPE VI is an effort by the federal Housing and Urban Development (HUD) Department to transform the nature of public housing in the U.S. It seeks to make residents self-sufficient and transform the institutional appearance of most public housing facilities. In the Elizabeth

Park neighborhood a covenant was formed with residents where any able-bodied person would either seek employment or enter a job training program. The first phase of the redevelopment of Elizabeth Park has been completed and has physically

transformed the neighborhood. Dilapidated pre-War row houses have been replaced by modern town-house style units constructed at a much lower density (Figure 49). At the time of the 2000 U.S. census, Elizabeth Park was approximately 40 White and 60% Black.

Approximately 27% of the residents live at or below the poverty level (Table 15).

Figure 47 -- Newly constructed low-income housing in the Elizabeth Park neighborhood. The All-American bridge visible at the top of the photo spans the Little Cuyahoga river valley and connects Downtown with the North Hill neighborhood.

Table 15-- Elizabeth Park Neighborhood Profile Elizabeth Akron Race White 39.7%Park 69.1% Black 56.2% 26.6% Income Median HH income $23,047 $34,563 % Persons in Poverty 26.5% 16.8% Housing Median Year Built 1942 1949 Renters 52.6% 37.3% Average Rent $340 $425 Median House Value $51,783 $79,809 Educational Attainment High School Diploma 40.3% 36.3% Bachelors Degree 7.1% 11.4% Graduate or Professional 5.0% 11.4%

Degree 5. West Akron

West Akron is the second largest neighborhood in area in the City. It forms a large portion of the city limits on the west side and is adjacent to the Copley Township

(Figure 50). West Akron residents are primarily African-American (78.8%) a proportion notably higher than the overall city average of 26.6%. However, other demographic variables; median household income ($34,331), poverty level (16.8%), age of housing stock (1948) and median house value ($75,224) closely follow overall city averages

(Table 16). This neighborhood houses several municipal recreational facilities including the Good Park Municipal Golf course, the Akron Public Zoo, Perkins Woods, the Lawton

Street Community Center and several other smaller neighborhood parks. The western portion of the neighborhood is bisected by Interstate-77. The neighborhood is

primarily single family residential with small retail and service shops located along the length of Copley Road (Figure 51). The community is served by the Buchtel cluster of schools anchored by Buchtel High School. The community demonstrates a strong sense of pride in the high school.

. Figure 48 -- West Akron Neighborhood

Table 16 -- West Akron Neighborhood Profile West Akron Akron Race White 17.3% 69.1% Black 78.8% 26.6% Income Median HH income $34,331 $34,563 % Persons in Poverty 17.5% 16.8% Housing Median Year Built 1948 1949 Renters 31.0% 37.3% Average Rent $402 $425 Median House Value $75,224 $79,809 Educational Attainment High School Diploma 34.2% 36.3% Bachelors Degree 9.2% 11.4% Graduate or Professional 9.5% 11.4%

Degree

Figure 49 -- Affordable housing in the West Akron neighborhood.

Summary

This chapter presented summaries of the characteristics of the vernacular neighborhoods of Akron. Even though an attempt was made to organize neighborhoods into meaningful thematic categories, the themes failed to capture all of the intricacies of each neighborhood. Also, the thematic categories used to group neighborhoods were based on the unique historical development of the city and the evolution of its neighborhoods. These thematic categories (early expansion, annexation, etc.) would be virtually meaningless in another city. Returning to the discussion of types of regions presented in Chapter 1, neighborhoods can be seen as functional regions based on shopping activities, or a school; uniform regions based on a set of homogeneous socio- economic or administrative variables or planning regions where they serve as the basis for the control and administration of local government services. Still meaningful linkages between traditional regionalization schemes and urban neighborhoods are very specific to the local geography of the study area. A more general conceptual model is needed in order to have a common yard-stick for conceptualizing neighborhood. The next chapter presents one such conceptual model.

Chapter 3

Conceptual Framework: Galster

Introduction

Galster (1986, p. 243) laments that most approaches/definitions regarding neighborhood have “little operational content since they have not been employed in the specification of boundaries.” Martin (2003) outlines four major approaches or themes to neighborhood research; (1) definition, (2) community, (3) neighborhood effects and (4) local activism. The definition theme of neighborhood research describes neighborhood, the community theme illustrates relationships within neighborhood, the neighborhood effects theme sheds light on the impacts of neighborhood and the local activism theme highlights events within neighborhood.

None of these themes of neighborhood research lend meaning to what a neighborhood is. Galster colorfully describes the ambiguity regarding neighborhood this way, “urban social scientists have treated neighborhood in much the same way as courts of law have treated pornography: as a term that is hard to define precisely, but everyone knows it when they see it” (Galster, 2001, p. 2111). Fortunately, Galster has outlined an alternate conceptualization of neighborhood which attempts to overcome the shortcomings of previous approaches and that is “rigorously quantifiable and about which testable hypothesis can be formulated” (Galster, 2001, p. 2112). Galster’s model 83

captures and integrates many of the aspects present in the other themes of neighborhood research and will serve as the conceptual framework for this dissertation.

Key aspects of Galter’s conceptualization are summarized in this chapter.

Galster’s Definition of Neighborhood

Galster (2001, p. 2112) argues that neighborhood is “the bundle of spatially based attributes associated with clusters of residences, sometimes in conjunction with other land uses.” Building on the work of Lancaster (1966), Galster conceptualizes neighborhood as a complex commodity comprised of ten categories of simpler goods

(Table 17). Each of the ten components of Galster’s definition are spatially based, that is, they can only be quantified at a particular location. Five of the components comprising the definition of neighborhood are explicitly tied to location; structural, infrastructural, tax/public service, environmental and proximity. However, the remaining five components; demographics, class, political, social-interactive and sentimental, are attributes of the individuals who occupy the neighborhood and are measured or agglomerated at a particular location.

Table 17 -- Components of A Neighborhood (After Galster, 2001) Category Description

Structural characteristics of Type, scale, materials, design, state of repair, buildings density, landscaping

Infrastructure characteristics Roads, sidewalks, streetscaping, utility services

Demographic characteristics Age distribution, family composition, racial, of the residents ethnic, and religious types

Class Status of the residents Income, occupation and education

Tax/public service Quality of police/fire services, public schools, characteristics public administration, parks and recreation in relation to locally assessed taxes

Environmental characteristics Degree of pollution (land, air, water, noise), topographic features or views

Proximity characteristics Access to employment, entertainment and shopping

Political characteristics Degree to which local political networks are mobilized

Social-interactive Local friend and kin networks, type and quality characteristics of interpersonal associations

Sentimental characteristics Residents’ sense of identification with place, historical significance of buildings or district

Continuing to frame his definition from the perspective of a complex commodity,

Galster argues that neighborhoods, like all commodities, are consumed and produced.

He outlines four types of consumers of neighborhood: households, businesses, property owners and local government. Households consume neighborhood by utilizing

residential structures and the amenities of the neighborhood resulting in “some degree of satisfaction or quality of residential life” (p. 2113). Businesses consume neighborhood by occupying non-residential structures and benefit from the revenue streams produced by providing retail or service activities. Property owners consume neighborhood through the purchase, resale, rental or lease of portions of the neighborhood. Finally, Galster states that local governments “consume neighborhood by extracting tax revenues, typically from owners based on the assessed values of residential and non-residential properties” (p. 2113). Each of the ten components of

Galster’s model can be linked to at least one of the four types of consumers of neighborhood.

Like all commodities, neighborhoods are also produced. However, the process by which neighborhoods come into existence is more complex than for traditional commodities. Galster (2001, p. 2116) acknowledges that neighborhoods are dynamic and that “the attributes comprising neighborhood at any moment are, in fact, the result of past and (typically) current flows of households and resources – financial, social- psychological and time – into and out of the space in question.” Regardless, Galster argues that the producers of neighborhood are in a large part the same entities which produce neighborhood (i.e. households, businesses, property owners and local governments). Galster summarizes the consumer-producer symbiosis this way (p.

2116):

Households consume a neighbourhood by choosing to occupy it, thereby producing an attribute of that location related to that household’s demographic characteristics, status, civil behaviors, participation in local voluntary associations and social networks, and so forth. Property owners consume a neighbourhood by buying land and/or buildings in it; they subsequently produce the neighborhood’s attributes through their decisions regarding property construction, upkeep, rehabilitation or abandonment. Business people consume a neighbourhood by operating firms there, thereby producing attributes related to structure types, land use, pollution and accessibility. Local governments consume neighbourhood by extracting property tax revenue and, in turn, produce attributes associated with public services and infrastructure.

Galster also acknowledges that there are other constituencies that may contribute to the production of neighborhood such as real-estate brokers, insurance agents and mortgage agencies.

Clearly Galster’s definition of neighborhood is broad and complex and acknowledges that neighborhood is a dynamic. In addition to the components of neighborhood being complex, Galster states that his neighborhood concept can be viewed at different spatial scale.

Varying Spatial Scales of Neighborhood

Implicit in Galster’s definition is the idea that neighborhood can be viewed at a variety of spatial scales. Galster indicates that his conceptual model is consistent with Suttles’

(1972) spatial view of neighborhood where neighborhood can be identified at four distinct spatial scales (Figure 52). The smallest scale is the “block face” extending over one a side of standard city block. The second level is the “defended neighborhood” – the smallest area possessing a corporate identity as defined by mutual opposition or

conflict to another area. The third level is the “community of limited liability” often- representing areas demarcated by local governments. Finally, the “expanded community of limited liability” represents the entire urban area. Kearns and Parkinson

(2001) provide a reinterpretation of Suttles’ spatial scales of neighborhood which is more descriptive and is presented in Table 18 to help illustrate the possible scales at which Galster’s model of neighborhood can be interpreted.

expanded community of limited liability community of limited liability

defended neighborhood

city block face

Figure 50-- Suttles' Spatial Scale of Neighborhood

Table 18 -- Adaptation of Suttles’ Spatial Scales of Neighborhood (Adapted from Kearns & Parkinson, 2001) Scale Suttles’ Category Predominant function Mechanisms

Home area block face Psycho-social benefits familiarity defended (identity, belonging) community neighborhood

Locality community of limited Residential activities Planning liability Social status and service position provision housing market

Urban expanded community Landscape of social and Employment district or of limited liability economic opportunities connection region Leisure interests Social networks

Galster cautions that his model is not the “Holy Grail sought by much neighborhood analysis of the 20th century: a means of unambiguously bounding urban neighborhoods” (Galster, 2001. p. 2113). He argues that the researcher must select the appropriate scale of neighborhood depending on the attributes or neighborhood typology of interest. This assertion is further supported by Gephart (1997, p. 10) who states that “insofar as neighborhood has a geographical referent, its meaning depends on context and function. The relevant units vary by behavior and domain, and they depend on the outcome or process of interest.”

Degree of Presence of Neighborhood

While Galster asserts that most of the ten components of his model are usually present to some degree in all neighborhoods he acknowledges that the quantity and composition of the attributes making up each component will vary dramatically across space. For example, Galster suggests that the environmental component of his model has the attributes of air, water and noise pollution. The degree of noise pollution might vary dramatically over just a few hundred feet while air pollution might not vary much across the entire urban area and water pollution might be non-existent across most of the urban area. This spatial variability in the attributes comprising each component implies that not all of the ten components in Galster’s model will be present across the entire urban fabric. Galster extends this argument further stating that “in instances where a certain dimension (social-interactive or sentimental, for example) of the neighborhood is virtually absent at a certain location, ‘neighborhood’ in this dimension can be thought of as being absent there” (Galster, 2001, p. 2113). A unique aspect of

Galster’s model is that “the type and even existence of neighborhoods can and often does vary across urban space” (Galster 2001, p. 2113) . He describes this as “the degree of presence of neighborhood” (p. 2113) (Figure 53).

Figure 51 -- Example of How Galster Conceptual Model Varies Across Space

Toward the Empirical Evaluation of Galster’s Model

Given that Galster’s model has yet to be evaluated empirically at any spatial scale, this dissertation will focus on the community of limited liability or macro scale

(Figure 54). In the context of Akron, this scale represents the vernacular neighborhoods as defined by the City Department of Planning and Urban Development. This narrowing of the research is necessary in part because the meaning of neighborhood can vary with

“the social forces and/or proposed actions being assessed” (Sawicki & Flynn 1996, p.

169). This premise is further supported by Martin (2003) who states that the meaning of neighborhood is contingent upon the perspective and agenda of the researcher.

Although Galster’s model is purported to be general enough for a range of spatial scales, the availability, selection and aggregation of variables used to operationalize the model are highly dependent on scale (i.e. not all variables are available at all scales). Since one of the goals of this research is to advance the understanding of the urban geography of

Akron, Galster’s model will be operationalized at the macro scale to verify the presence and characteristics of vernacular neighborhoods.

Galster’s model attempts to organize and generalize ecological variables (i.e. things that are visible and measurable at a particular location) into meaningful concepts which, arguably, can’t be seen or measured directly. In order to move toward an empirical evaluation of this model, an analysis framework which can address both the measured and conceptual aspects of Galster’s model is necessary. The next chapter outlines the analysis framework used in this dissertation – the general factor model.

infrastructure demo- mega scale structural graphic expanded community of limited liability

macro scale community of limited class liability sentiment status

defended meso scale NEIGHBOR- neighborhood . HOOD tax public city block social face

service micro scale increasinggeographic scale

environ- political ment

proximity

potential scales at which Galster’s model can be implemented access to access to access to employment shopping entertainment

Figure 52 -- Linking Galster’s conceptual neighborhood model to Suttles’ spatial scales of neighborhood

Chapter 4

The General Factor Model (GFM)

Introduction

One important goal of geography, like all sciences, is the synthesis of knowledge through the process of organizing and modeling empirical data. Given that the domain of geography is literally the entire planet; geographers strive to generalize or create abstractions from vast numbers of possible empirical observations. Building on the work of Margenau (1961) and van Duijn (1965), Abler et al. (1971) provides a framework which describes how geographers abstract or generalize empirical observations into more general constructs or concepts (Figure 55). At the most rudimentary level, geographers measure or observe events or conditions on the empirical plane. In order to ascribe order or meaning to the observations they are generalized into constructs.

Constructs are ideas or a means of grouping patterns of observations into a more meaningful whole. For example, measurements regarding income, education and amount of personal savings can be generalized and viewed to represent the more general construct of economic status. Likewise, occupation, social standing and wealth can be generalized into the construct of social status. Constructs themselves can be further generalized or combined to form high-order constructs or concepts. For example, the constructs of social status and economic status can been

94 95

seen to flow from a more general global construct called socio-economic status. As we move through Abler , Adams and Gould’s framework, progressing from the empirical plane through one or more manifestations of the constuctural plane, the level of generalization and abstraction increases. Conversely, as we move down the hierarchy

Figure 53 -- Framework for Conceptualization (After Adams et al., 1971)

the level of uniqueness and specificity increases. At the top of Abler, Adams and

Gould’s hierarchy lies the conceptual plane. Concepts are universal ideas or generalizations that influence all subordinate constructs on the constructural plane.

Concepts are highly distilled ideas that can stand alone with no influence from higher- level ideas.

Although Abler , Adams and Gould’s hierarchy serves as a useful framework for the construction and refinement of theories, it does not provide a means for testing o r validating those theories. Fortunately a statistical analytic framework called structural equations modeling (SEM) exists which can assist in delineating and quantifying the relationships across the empirical, constructural and conceptual planes. It consists of measurement and causal components. The former emerges from a generalized factor model and the latter from a path model. Each is discussed below.

The General Factor Model (GFM)

In order to demonstrate methods of quantifying the relationships across the empirical, constructural and conceptual planes, the common types of factor analysis and

SEM are briefly discussed. After a brief introduction to terminology, principal components analysis (PCA), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) are summarized as special cases of the GFM. The matrices used in each factor analysis method are provided in Table 19.

Table 19 -- & , 2001)

Λ (lambda) Φ Θδ Notes factor (phi) (psi) Model Type loadings factor error correlation a) Λ is a full matrix because components are functions of every variable. Principal b) Φ is an identity matrix because there Components is no correlation among components in full identity 0 Analysis PCA (PCA) c) Θδ is a matrix of zeros because variables are assumed to be error free in PCA a) Λ is a full matrix because all factors load on every variable. b) diagonal elements of the Φ matrix Exploratory represent factor variances while off Factor diagonal elements are 0 because full diagonal diagonal Analysis there is no correlation among (orthogonal) components in orthogonal EFA c) Θδ is diagonal matrix with each element representing a random measurement error a) Λ is a full matrix because all factors load on every variable. b) diagonal elements of the Φ matrix Exploratory represent factor variances while off Factor sub- full diagonal diagonal elements represent Analysis diagonal correlation among factors (oblique) c) Θδ is a diagonal matrix with each element representing a random measurement error a) the Λ matrix contains some zero elements because not all factors load on every variable. Each column of Λ has a non-zero element to set scale. Confirmatory b) diagonal elements of the Φ matrix Factor researcher researcher researcher represent factor variances while off Analysis specified specified specified diagonal elements represent (CFA) correlation among factors c) Θδ is a symmetric matrix depending on theory with each element representing a random measurement error

Table 19 (continued) -- & , 2001) Symmetric Identity Diagonal Sub Diagonal # 0 0 0 # 1 2 1 0 0 0 # 0 0 1 # 3 0 1 0 0 0 # 0 2 3 # 0 0 1 0 0 0 #

The general factor model can be constructed for a single congeneric model or a number of congeneric models. A congeneric model is the least restrictive of all of the types of measurement models where “each individual item measures the same latent

x1 λ1 δ1

λ2 ξ x2 δ2 λ3 x λ4 3 δ 3

x 4 δ 4 Figure 54 -- A single congeneric model.

variable, with possibly different scales, with possibly different degrees of precision, and with possibly different amounts of error” (Graham, 2006, p. 935). A single congeneric model is illustrated below. In Figure 56 the arrows originate from the factor and end at the measurements. The constructs converge on the items. Algebraically this is

xi i i (1)

where: xi = manifest variable i

λi = magnitude of influence of the factor on manifest variable i

δi = error term for manifest variable i

ξ = factor

For two or more congeneric models, the model is shown in Figure 57.

x1 λ11 δ1

λ12 ξ1 x2 δ2

λ13 x 3 δ 3 Φ

x4 λ21 δ4

λ22 ξ2 x5 δ5 λ23 x 6 δ 6 Figure 55 – Two Factor Congeneric Measurement Model

Equation 1 becomes:

xij ij j i (j 1,2) (2)

where: xi = manifest variable i

λij = magnitude of influence of factor j on manifest variable i

100

δi = error term for manifest variable i

ξj = factor j

Φ = factor intercorrelation

In matrix form Equation 2 becomes:

x (3) where x is a vector of manifest variables, Λ is a matrix of factor loadings, ξ are latent factors, and δ is a vector of errors. Assuming manifest variables are measured as deviations from their means, the population covariance matrix that corresponds to the sample covariance, Sxx is:

(4) xx E( )( )'

Given that (A+B)' = A' + B' and (AB)' = B'A' , equation 4 expands to:

xx E( ( )' ( )' ' ') (5)

Rearranging terms and employing aspects of BLUE condition 3 (coefficients and errors are uncorrelated) yields:

xx E( ' ') E( ' ') E( ' ) E( ') (6)

Given that BLUE (best least unbiased estimate) conditions require that errors cannot be correlated with factor (i.e. cov(δ,ξ) = 0), equation 6 reduces to:

101

xx E[ ( ') '] E[ '] (7)

Represented in matrix notation, equation 8 is the general factor model on which all variations of factor analysis are based.

(8) xx '

where:

Λ represents the factor loadings between the observed variables and

latent factors, contains the covariances among the latent factors and θδ

contains the covariances among the errors.

Based on exact forms of Λ, , and θδ (Table 19), a family of factor models emerge as summarized in Figure 58: from Principal Component Analysis (Figure 58A) to

Confirmatory Factor Analysis (Figure 58, C1). Some of these models are discussed next.

102

A Principal Component Analysis B1 Exploratory Factor Analysis (oblique) B2 Exploratory Factor Analysis (orthogonal)

C1 C2 C1 C2 C1 C2

X1 X2 X3 X4 X5 X6 X1 X2 X3 X4 X5 X6 X1 X2 X3 X4 X5 X6

e1 e2 e3 e4 e5 e6 e1 e2 e3 e4 e5 e6

C1 Confirmatory Factor Analysis C2 Higher Order Factor Analysis K1 C1 C2 C1 C2

X1 X2 X3 X4 X5 X6 X1 X2 X3 X4 X5 X6

e1 e2 e3 e4 e5 e6 e1 e2 e3 e4 e5 e6

Figure 56-- Types of Factor Analysis

103

Principal Component Analysis (PCA)

Primarily a data reduction technique, PCA is the most restrictive of the factor analytic techniques in that the researcher does not specify any specific relationships among the manifest variables. The goal of PCA is to explain the covariance among a set of variables by expressing them as a set of components (Dunteman, 1989). PCA assumes that all factors load on all variables (Figure 58a). Eigenvalues and eigenvectors extraction procedures ensure that the first eigenvalue explains the proportion of the trace of the matrix (Gould, 1967). Components are constructed in such a way that they account for the variance in the observed data. The first factor (or component in PCA terminology) accounts for as much of the variance as possible while the second factor accounts for as much of the remaining variance as possible. This process continues until a threshold is reached; either the desired amount of variance has been explained or a natural break in the importance of the components is detected. Since no a priori theory or model is specified in PCA, the researcher must subjectively assign meaning to the components extracted. The researcher decides what loadings below a certain threshold are set to zero. PCA theory assumes that variables are measured without error and that components are orthogonal (Table 34A) (Jolliffee, 2002). Based of these assumptions, Φ in the General Factor Model (GFM) (Equation 8) is set to the identity matrix and θδ = 0.

Thus the GFM when applied to principal components analysis reduces to:

104

(9) xx '

Components are viewed as linear combinations of the manifest variables. The common input for PCA is the correlation matrix, where the trace (i.e the total variance extracted by all the eigenvalues, equals the number of variables.

Exploratory Factor Analysis (EFA)

EFA is one type of factor analysis where the variables are not assumed to be error free (Figure 58b). Variables are expressed as linear combinations of factor influences and variable errors. It attempts to explain the variance in a set of manifest variables in a more parsimonious way . The input for EFA is a modified correlation matrix with the diagonal elements replaced by the multiple correlations between each variable and the remaining (m-1) variables (Maruyama, 1998). The common (shared) variance among a set of manifest variables is captured by the factor covariance (or correlation) among the factors. The error terms capture any remaining variance unique to the variables. A common purpose of EFA is to find the most parsimonious and interpretable set of variables which explains a factor. This goal is often accomplished through the process of factor rotation in order to achieve simple structure. Dillon and

Goldstein (1984) summarized ‘simple structure’ as follows: First, any column of the factor loading matrix should have relatively small values thus ensuring that each factor is unique. Second, any row of the matrix should have only a few entries far from zero

105

indicating variable uniqueness. Lastly, any two columns of the matrix should have different patterns of high and low values indicating that there is no duplication of factors. Initial factor solutions can be rotated so that factors are either correlated

(oblique) or uncorrelated (orthogonal). EFA is used to link a priori specified constructs from the constructural plane to the empirical plane and is referred to as the measurement model in structural equation modeling terminology. In the orthogonal form of EFA, factors are not correlated with one another (i.e. Φ = I) therefore, the GFM for orthogonal EFA reduces to:

xx ' (10)

In the oblique form of EFA, factors are correlated and are represented in the Φ matrix where off-diagonal elements represent the correlations between the factors. Following, the GFM for the oblique form of EFA is:

(11) xx '

In oblique EFA, all factors are inter-correlated to some degree hence Φ is a sub diagonal matrix.

Confirmatory Factor Analysis (CFA)

CFA is a form of factor analysis where the researcher specifies variable-factor interrelationships (Gorsuch, 1983). As the name implies, CFA is used primarily to confirm a preconceived theoretical model expressed through a path diagram (Figure 58

106

C1). CFA utilizes all three matrices (Λ, Φ and Θ) to represent relationships among factors, manifest variables and error terms ( & , 2001). The Λ (lambda) matrix represents the relationship between latent factors and manifest variables. It indicates the magnitude and direction of the influence of the latent factor on each manifest variable. The structure of the Λ (lambda) matrix is specified by the researcher.

In order to ensure that each of the manifest variables are measured on the same scale, each column of the Λ matrix must contain a 1. The Φ matrix is either diagonal with elements representing the variance of each factor or some factors are allowed to be free (i.e. selected factors Φij are free). Figure 59 represents a two factor, six variable

CFA model. The Λ matrix contains several zeros indicating that not all factors load on all variables. The Φ matrix contains one off-diagonal element (Φ21) representing the covariance between the two latent factors. The Ψ matrix contains two off-diagonal elements (Ψ 32 and Ψ 54) representing the covariance between error terms for variables

X3 and X2 and X5 and X4 respectively.

107

Φ21

Conceptual Plane F1 F2

λ22 λ32 λ41 1 λ21 λ52 1

Empirical Plane X1 X2 X3 X4 X5 X6

e1 e2 e3 e4 e5 e6

ψ32 ψ54

Λ Φ Ψ

1 0 11

21 22 22 0 32 11 32 33 41 0 21 22 44

0 52 54 55

0 1 66

Figure 57 – Confirmatory Factor Analysis with Corresponding Matrices

Summary

The purpose of this chapter was to outline the various forms of the general factor model and to show how it can be used to link the empirical (measured) portion of

Galster’s model with the conceptual portions. A secondary purpose was to provide the reader with background information to aid in the understanding and interpretation of the terminology and results presented later in this dissertation. As with all statistical analysis frameworks, there are operational limitations and assumptions that must be addressed. The next chapter explores the data sources used to operationalize Galster’s

108

model for Akron, Ohio neighborhoods and addresses how these data meet some of the underlying assumptions of the statistical techniques based on the general factor model.

Chapter 5

Data Sources

Introduction

Although structural equation modeling is a fairly robust statistical technique, it still has assumptions about the underlying data used to create the sample covariance.

This very brief chapter will outline the sources for the data and the areal unit of analysis, describe how Galster’s constructs will be operationalized and introduce issues of sample size.

Data Sources

The primary areal units of analysis employed in this research is a land parcel. A parcel is defined as a contiguous piece of land described in a deed or other instrument of conveyance. Spatial information at the parcel level was extracted from the Summit

County Fiscal Office Geographical Information System for all parcels within the jurisdiction of the City of Akron. The city contains approximately 103,000 parcels. After excluding commercial and industrial parcels, 61,014 residential parcels remain and were used in the analysis. Attribute data related to these parcels were extracted from the

Summit County Fiscal Office Computer Aided Mass Appraisal (CAMA) database.

109

110

Demographic and household data were extracted from the US Census of

Population and Housing 2000 at the block level and block group levels. Akron contains

3,943 census blocks and 254 block groups. Galster's model contains a political component designed to measure the level of political involvement. Variables used to approximate the political activity of residents were obtained from the Summit County

Board of Elections.

Operationalizing Galster’s Constructs

Structural Equations Modeling, the primary methodology employed in this dissertation is data intensive. Since SEM evaluates how a set of observed data fits to a theoretical model, relatively large numbers of observations and variables are required.

Marsh, Hau, Balla and Grayson (1989) point out that statistical power in SEM is improved with minimum sample sizes of 100 but samples of 200 or more observations are more desirable. The authors also state that there is broad agreement in the SEM literature that two or more indicators per latent factor are necessary for model identification (Newcomb & Bentler, 1988, p. 66). Given these guidelines, variables were selected from available data sources to measure nine of the ten constructs in Galster's model of neighborhood. The tenth component of Galster's model, sentimental characteristics, purports to capture "residents' sense of identification with place, historical significance of buildings or district, etc" (Galster, 2001, p. 2112).

Sentimentality and personal identification with place are not easily quantifiable and the

111

entire component was excluded from the model. Table 20 summarizes the variables selected to measure each of the components of Galster's model. Specific details on the derivation and transformation of each variable is provided in the section on measurement models.

Table 20 -- Collection Level and Measurement Scale for Observed Variables Collection Measurement Construct Variable Description Level Scale building age parcel continuous Structural story height parcel ordinal building condition parcel ordinal building setback parcel continuous Infrastructural proximity to road parcel continuous tax assessment parcel continuous median resident age block continuous % Black block continuous Demographics % HH one male block continuous % female HH block continuous median HH income block group continuous Class % use public transp block group continuous % bachelor degree block group continuous proximity to school parcel continuous Tax/Public proximity to institution parcel continuous Service tax on land parcel continuous tax on building parcel continuous lot size parcel continuous Environmental % vacant block group continuous proximity to pollution parcel continuous proximity to retail parcel continuous proximity to openspace parcel continuous Proximity proximity to city limits parcel continuous proximity to downtown parcel continuous Political registered voter parcel binary proximity to block club parcel continuous Social proximity to CDC parcel continuous Interactive proximity to other agency parcel continuous

112

Sample Size

The data for assessing Galster’s conceptual model of neighborhood consists of

30 variables gathered from the data sources listed above and summarized in Table 20.

At the very heart of SEM analysis is an asymptotic assumption – that the sample covariance matrix will converge to the population covariance matrix as the sample size grows large (Andreassen et al. 2006). Marsh and Hau (1999, p. 252) in summarizing the work of other SEM researchers point out that “percentage of proper solutions, accuracy of parameter estimates, and the appropriateness of the χ2 test statistic were all favorably influenced by having larger Ns, recommending N > 100, but also noting the desirability of N > 200.” Alternative estimation and model interpretation strategies have to be employed if small sample sizes are to be used. Since the areal unit of analysis for this research is the land parcel, a large dataset is available for analysis. Table 21 shows the number of observations for each of the vernacular neighborhoods. All neighborhoods exceed the minimum number of observations suggested by Marsh and

Hau.

113

Table 21—Genesis and Number of Observations in each Neighborhood Number Genesis Neighborhood of parcels Middlebury 1,823 Original Settlement and East Akron 4,069 Early Expansion South Akron 2,346 Summit Lake 1,186 Ellet 6,470 Annexation Kenmore 5,905 Merriman Valley 673 Industrial Neighborhood Goodyear Heights 6,008 Development Firestone Park 4,979 Highland Square 2,892 Neighborhood Retail North Hill 4,968 Centers Wallhaven 1,152 Chapel Hill 1,393 Large Retail and Service Rolling Acres 632 Centers University Park 1,345 Lane Wooster 2,665 Fairlawn Heights 1,047 Socio-economic Status Northwest Akron 3,901 Elizabeth Park 979 West Akron 6,413

Chapter 6

Results: Descriptive Statistics and Data Transformations

Introduction

As with all statistical techniques, structural equation modeling has underlying assumptions regarding the measurement scale and distribution of the underlying data.

This chapter examines the measurement scales and statistical distribution of the observed variables, describes the data transformations used and introduces aspects of the analysis method which are directly impacted by characteristics of the data.

Measurement Scale of the Observed Variables

An important underlying assumption with maximum likelihood estimators is that the data are measured on a continuous scale. Although the majority of the variables used are measured on a continuous scale, three variables are measured on an ordinal or binary scale; house story height (one or two floors), house condition (very poor, poor, average), and voter status (registered/not registered). It has been common practice to treat ordinal data as if it were continuous. However, (2005) strongly cautions that ordinal data have no origins or units of measurement and “to use ordinal variables in structural equation models requires other techniques than those that are traditionally employed with continuous variables” ( , 2005, p 10). In order to accurately calculate covariance for ordinal data they must be converted to a continuous scale. This

114 115

is accomplished by assuming that the ordinal variable is a crude representation of an underlying continuous variable z*. Through the process of threshold analysis the ordinal variable is mapped onto a continuous normal curve by establishing thresholds for each level of the ordinal variable. As a didactic example, in the dataset for the city of Akron, assume 14% of the houses were of poor condition, 77% were average and 8% were above average. Cumulatively, these percentages are 14, 91 (14 + 77) and 100 (14 + 77 +

8). Two thresholds are needed to define the three levels of the house quality ordinal variable. The first threshold is located where the area under the normal curve to the left of the threshold is 14%. The second threshold is located where the area under the normal curve is 91% (Figure 60).

14%

91%

poor condition average condition above average condition Figure 58-- Illustrating House Condition Thresholds

Mapping these variables to the normal curve assigns a metric to the ordinal variable. Thresholds were established for the variables housing condition, house height, and voter status. Since calculation of covariance requires continuous data, polychoric or

116

polyserial correlations were first calculated for all variables and the covariance matrix was derived by scaling the correlation matrix. According to (2005, p. 26), “the

[resulting] mean vector and covariance matrix can be used in LISREL in the same way as for continuous variables.”

Statistical Distribution of the Data

Initial analysis of skewness and kurtosis of the 25 continuous variables reveals that all of the variables are significantly skewed and have high kurtosis values indicating that they vary significantly from the normal distribution (Table 22). Further, tests of multivariate normality show a very high relative measure of kurtosis (RMK). An RMK of

9.42 indicates that the data are not multivariate normally distributed (Table 23). This high degree of non-normality violates the assumptions of the maximum likelihood estimation method. In order to attempt to normalize the data, all continuous variables were log10 transformed. Test for multivariate normality were repeated on the transformed data and show support that the data are multivariate normal (RMK = 2.01)

(Table 24).

117

Table 22-- Test of Univariate Normality for Continuous Variables Skewness Kurtosis Skewness and Kurtosis Variable Z-Score P-Value Z-Score P-Value Chi-Square P-Value lot size 227.747 0.000 3093.787 0.000 9623387.432 0.000 building age -156.461 0.000 450.951 0.000 227837.120 0.000 special tax 91.717 0.000 106.821 0.000 19822.666 0.000 tax on land -69.246 0.000 1461.694 0.000 2141343.829 0.000 tax on building -263.901 0.000 6352.487 0.000 overflow 0.000 median resident age -200.500 0.000 2825.185 0.000 8021867.943 0.000 % Black -12.329 0.000 -73.227 0.000 5514.219 0.000 % HH one male -112.550 0.000 84.675 0.000 19837.238 0.000 median HH income -32.012 0.000 53.320 0.000 3867.749 0.000 % use public trans 27.560 0.000 -52.045 0.000 3468.225 0.000 % bachelor degree 103.005 0.000 40.814 0.000 12275.881 0.000 % vacant -29.529 0.000 -60.312 0.000 4509.461 0.000 building setback -204.571 0.000 991.010 0.000 1023950.061 0.000 proximity to major -71.246 0.000 13.713 0.000 5263.965 0.000 road proximity to school -24.502 0.000 30.091 0.000 1505.793 0.000 proximity to 0.313 0.754 59.909 0.000 3589.187 0.000 institution proximity to pollution -62.060 0.000 19.596 0.000 4235.471 0.000 proximity to retail -48.399 0.000 15.563 0.000 2584.652 0.000 proximity to -154.505 0.000 592.495 0.000 374921.639 0.000 openspace proximity to city limits -118.874 0.000 152.804 0.000 37480.232 0.000 proximity to -57.079 0.000 19.474 0.000 3637.273 0.000 downtown proximity to block club -47.646 0.000 -1.202 0.229 2271.572 0.000 proximity to CDC -60.739 0.000 1.125 0.260 3690.501 0.000 proximity to other -61.365 0.000 44.973 0.000 5788.197 0.000 social agency

Table 23 -- Test of Multivariate Normality (Continuous Variables Before Transformation) Skewness Kurtosis Skewness and Kurtosis Value Z-Score p Value Z-Score p Chi-Square p 7829.369 3923.203 0.000 12895.929 687.130 0.000 15863671.3 0.000 Relative Multivariate Kurtosis = 9.420

118

Table 24 -- Test of Multivariate Normality (Continuous Variables After Transformation) Skewness Kurtosis Skewness and Kurtosis Value Z-Score P-Value Value Z-Score P-Value Chi-Square P-Value 487.597 1376.795 0.000 1813.769 483.794 0.000 2129620.812 0.000 Relative Multivariate Kurtosis = 2.018

Analysis Method

The underlying goal of SEM is to determine how well a set of observed data fit a theoretical model as specified in a path diagram. Computationally this is accomplished by examining the covariance structure of both the observed data (Sxx)and the theoretical model (Σxx). If is statistically zero then the theoretical model may be a plausible explanation for the relationships among the observed variables. Estimation of

is an iterative process that attempts to minimize the differences between and S. Commercial SEM packages have several options for estimation algorithms depending on the distribution and size of the sample data. A common estimation method in the SEM literature is maximum likelihood (ML). For a measurement model with p variables, this estimation algorithm minimizes the function:

(12)

Although the maximum likelihood estimation method requires multivariate normal data it is fairly robust to violations of non-normality (Tepper & Hoyle, 1996;

Satorra & Bentler, 1994; Satorra & Bentler, 2001) and alternate fit indices are available which account for non-normal data. The most common is the Satorra-Bentler (1999) normalized . Tepper and Hoyle (1996) have shown that provides an excellent

119

minimization under such conditions. Other minimization methods under such conditions include Browne’s (1984) moment procedure. Tepper and Hoyle suggest that the maximum likelihood method is robust when there is moderate kurtosis as in this study. Consequently, the maximum likelihood estimation method was employed for all analyses.

Missing Data

As a statistical technique, structural equation modeling is particularly sensitive to missing data. Although techniques exist to estimate models with missing data – full information maximum likelihood (FIML) – it is always important to evaluate whether there are missing data in the analysis of a model and why. Due to the authoritative data sources used for this research there were no missing data in the analysis. Data collected from the Summit County Fiscal Office is exhaustive – all parcels have an owner and complete cadastral information is on file with the county. Data collected from the US

Census Bureau is spatially extensive for the entire United States and no data were missing. Finally, voter information was collected from the Summit County Board of

Elections where, if data were missing, someone would not be eligible to vote.

Given that the data meet the assumptions of measurement scale and statistical distribution required for factor analysis, they can serve as measurement variables for a series of congeneric and confirmatory factor models to empirically validate Galster’s conceptual model of neighborhood. The next chapter will present these factor models.

Chapter 7

Results: Verifying Galster’s Constructs

The Measurement Model

An important first step in structural equation modeling is to assess the ability of the observed variables to measure the unobserved or latent constructs. In essence, the measurement component of a structural equation model is capturing the degree of convergent validity – the degree to what a construct explains the variability in each item. Verifying the convergent validity (Chapter 8) and the interrelationships among the constructs in the model (Chapter 9) meets the first research goal of this dissertation – the empirical verification of Galster’s conceptual model of neighborhood.

The convergent validity of Galster’s model was assessed in three ways. First, the empirical fit of each measurement model was determined to assess whether the observed data reasonably fit the model. Second, the pattern and significance of the factor loading on each of the observed variables was calculated to determine if each variable is a reasonable indicator of the factor. Finally, the squared multiple correlation coefficient was calculated for each of the observed variables to determine the relative amount of variance each observed variable is capturing. Each of these approaches to verifying Galster’s measurement models will be discussed in turn.

120 121

Empirical Fit

Overall empirical fit is a measurement of the similarity of the sample covariance matrix S to the model implied population covariance matrix Σ (Marsh, Hau, Balla &

Grayson, 1998). Since the population covariance matrix is unknown it must be estimated through an iterative estimation algorithm from Equation 12 on page 119. If a model is misspecified or the data do not reasonably fit the model then the estimation algorithm will not converge. Empirical convergence implies that the model is reasonably identified and that there is some degree of fit between the measurement variables and the constructs. Congeneric measurement models were constructed for each of the eight constructs in Galster’s conceptual model (i.e. structural, infrastructure, demographic, class, tax/service, environment, proximity and social). Graham (2006, p.

935) describes congeneric measurement models which assume that “each individual item measures the same latent variable, with possibly different scales, with possibly different degrees of precision, and with possibly different amounts of error.” Figure 61 shows a measurement model for the structural construct of Galster’s model

122

Structural Construct

building building story age condition height

error error error

Figure 59 -- Path Diagram for Congeneric Measurement Model for Structural Component

Similar models were constructed for the remaining seven components.

Measurement models for each of the eight constructs were estimated for the city as a whole and for 19 neighborhoods for a total of 160 measurement models. All 160 models converged indicating they were indentified and had no gross misspecification.

In order to determine the level of empirical fit SEM software provides a series of goodness of fit indices. There is little agreement in the literature over which of the many fit indices to use, however there is general agreement that more than one index is necessary (Marsh, Balla & McDonald, 1988). A popular measurement of fit used is the

χ2 because its statistical distribution is known. However, the χ2 fit statistic is very susceptible to non-normality and sample size. An alternative to the χ2 goodness of fit measure, the Satorra-Bentler χ2, has been developed to overcome these limitations

(Satorra & Bentler, 2001). Even using the Satorra-Bentler χ2, the very large sample sizes

123

used in estimating the measurement models resulted in p-values (less than 0.05) indicating poor fit. Fortunately, several alternative fit indices exist that are less susceptible to large sample sizes. The following comparative fit indices were used to determine overall measurement model fit: comparative fit index (CFI: Bentler, 1990) , the non-normed fit index (NNFI: Tucker & Lewis, 1973) and the root mean squared error of approximation (RMSEA: Steiger, 1998). These indices were selected because they are least susceptible to large sample sizes and non-normal data. If any two of the fit indices were within the acceptable critical ranges specified in the literature, the measurement model was deemed to fit. Acceptable critical cutoff values for each of the fit indices were selected from the literature. The lower the RMSEA value the better the model fit.

RMSEA values from 0 to 0.05 are considered a good fit and values between 0.05 and

0.08 are considered an acceptable fit (Hoyle 2000). The NNFI and CFI indices both range from 0 to 1 with numbers closer to 1 indicating better fit. CFI values between 0.97 and

1.0 indicate a good fit and values between 0.90 and 0.97 indicate an acceptable fit

(Bentler & Bonnet, 1980; Bentler, 1990). Similarly NNFI values between 0.97 and 1.0 indicate a good fit and values between 0.90 and 0.97 indicate an acceptable fit however the cutoff can be relaxed to 0.80 (Garson, 2007). Table 25 summarizes the critical cutoff values used to determine measurement model fit.

124

Table 25 -- Recommendations for Acceptable Levels of Model Fit Fit Index Proponent Acceptable Fit Good Fit

RMSEA Hoyle (2000) 0 ≤ RMSEA ≤ 0.05 0.05 ≤ RMSEA ≤ 0.08

NNFI Garson (2007) 0.8 ≤ NNFI ≤ 0.9 0.9 ≤ NNFI ≤ 1.0

CFI Bentler (1990) 0.9 ≤ CFI ≤0.97 0.97 ≤ CFI ≤ 1.0

Adapted from Schermelleh-Engel, Moosbrugger & Muller (2003), Boros (2005)

The overall fit of the measurement models is summarized in Table 26. Examining the fit of the measurement models by constructs, with the exception of the infrastructure construct, each measurement model fits in the majority of the neighborhoods. Examining the fit of the models by neighborhood, Table 26 shows that with the exception of Kenmore and South Akron, all neighborhoods fit on at least two of the constructs with the majority of the neighborhoods fitting on five or more constructs.

Table 26 also summarizes the fit of the measurement models by neighborhood. The final row of the table indicates the total number of neighborhoods in which each construct has acceptable fit. The measurement model for the infrastructural construct fit in the fewest neighborhoods (5 out of 19 neighborhoods) while the social construct measurement model fit in the most neighborhoods (16 out of 19 neighborhoods). The lack of model fit for some of the neighborhoods would normally be of concern however this result is expected in the context of Galster’s neighborhood model. Galster explicitly states “ in instances where a certain dimension *construct+ of the neighborhood bundle

125

is virtually absent at a certain location, ‘neighborhood’ in this dimension can be thought of being absent there (p. 2113). Galster’s model does not assume that all constructs are present in all neighborhoods. However, it is important to reiterate that all of the constructs are present in at least some of the neighborhoods. Table 27 summarizes the detailed fit indices for each construct by neighborhood. Given the degree of empirical fit, it was judged that the measurement models were acceptable and the next step of determining internal consistency could be explored.

126

Table 26 -- -- Empirical Fit of Measurement Models

Total # of

Constructs

with Class

Social Acceptable Proximity

Structural Fit

Demographics

Infrastructural

Environmental Tax/Public Service Tax/Public Genesis Neighborhood Akron X X X X 4 Early Expansion East Akron X X X X X 5 Middlebury X X X X X 5 South Akron X 1 Summit Lake X X X 3 Annexation Ellet X X X X X 5 Kenmore X 1 Merriman Valley X X X X X X X X 8 Industrial Goodyear X X X 3 Neighborhood FirestoneHeights Park X X 2 Neighborhood Highland Square X X X X X X 6 Retail North Hill X X 2 Wallhaven X X 2 Lrg. Scale Retail Chapel Hill X X X X X 5 or Service X X X X X 5 University Socio-economic Fairlawn Heights X X X X X 5 Status Northwest X X X X X X 6 WestAkron Akron X X X X 4 Elizabeth Park X X X X 4 Lane Wooster X X X X X 5 Total # Neighborhoods 11 5 7 12 9 12 9 16 with Acceptable Fit

127 Table 27 -- Measurement Model Fit for All Neighborhoods Neighborhood Structural Infrastructural Demographics Class Tax/Public Environmental Proximity Social Service RMSEA =0.2252 RMSEA =0.04179 RMSEA =0.1739 RMSEA =0.0180 RMSEA =0.1288 RMSEA =0.06016 RMSEA =0.05191 RMSEA =0.2369 Akron NNFI = 0.2099 NNFI = 0.593 NNFI = 0.3652 NNFI = 0.9974 NNFI = 0.584 NNFI = 0.8614 NNFI = 0.9732 NNFI = 0.8084 CFI = 0.5967 CFI = 0.8643 CFI = 0.6826 CFI = 0.9991 CFI = 0.8613 CFI = 0.9539 CFI = 0.9911 CFI = 0.9361 RMSEA = 0.0275 RMSEA = 0.04228 RMSEA = 0.118 RMSEA = 0.0546 RMSEA = 0.139 RMSEA = 0 RMSEA = 0.1438 RMSEA = 0.07933 Chapel Hill NNFI = 0.965 NNFI = 0 NNFI = 0 NNFI = 0.7538 NNFI = 0.8472 NNFI = 1 NNFI = 0 NNFI = 0.8005 CFI = 0.9883 CFI = 0.6579 CFI = 0.2817 CFI = 0.9179 CFI = 0.9491 CFI = 1 CFI = 0.5867 CFI = 0.9335 RMSEA = 0.0792 RMSEA = 0.1635 RMSEA = 0.1444 RMSEA = 0.1202 RMSEA = 0.1084 RMSEA = 0 RMSEA = 0.0669 RMSEA = 0.1289 East Akron NNFI = 0.9422 NNFI = 1 NNFI = 0.8318 NNFI = 0.6717 NNFI = 0.6291 NNFI = 1 NNFI = 0.9791 NNFI = 0.938 CFI = 0.9807 CFI = 0.2019 CFI = 0.9159 CFI = 0.8906 CFI = 0.8764 CFI = 1 CFI = 0.993 CFI = 0.9793 RMSEA = 0.0921 RMSEA = 0.0479 RMSEA = 0.135 RMSEA = 0.0500 RMSEA = 0.0598 RMSEA = 0.3839 RMSEA = 0.1026 RMSEA = 0 Elizabeth Park NNFI = 0.922 NNFI = 0 NNFI = 0.5763 NNFI = 0.9889 NNFI = 0.7958 NNFI = 0 NNFI = 0.9365 NNFI = 1 CFI = 0.974 CFI = 0 CFI = 0.7881 CFI = 0.9963 CFI = 0.9319 CFI = 0 CFI = 0.9788 CFI = 1 RMSEA = 0.0245 RMSEA = 0.0172 RMSEA = 0.0724 RMSEA = 0.225 RMSEA = 0.1171 RMSEA = 0.0070 RMSEA = 0.0674 RMSEA = 0.0524 Ellet NNFI = 0.9888 NNFI = 0.9899 NNFI = 0.7539 NNFI = 0.6158 NNFI = 0.8462 NNFI = 0.91 NNFI = 0.9477 NNFI = 0.9968 CFI = 0.9963 CFI = 0.9966 CFI = 0.8769 CFI = 0.8719 CFI = 0.9487 CFI = 0.97 CFI = 0.9826 CFI = 0.9989 RMSEA = 0 RMSEA = 0.1711 RMSEA = 0.2009 RMSEA = 0.0827 RMSEA = 0.185 RMSEA = 0.1658 RMSEA = 0.0651 RMSEA = 0.1059 Fairlawn NNFI = 1 NNFI = 0 NNFI = 1 NNFI = 0.8099 NNFI = 0.869 NNFI = 0.2329 NNFI = 0.9723 NNFI = 0.9849 Heights CFI = 1 CFI = 0.3893 CFI = 0 CFI = 0.9366 CFI = 0.9563 CFI = 0.7443 CFI = 0.9908 CFI = 0.995 RMSEA = 0.3174 RMSEA = 0.122 RMSEA = 0.09995 RMSEA = 0.0239 RMSEA = 0.0965 RMSEA = 0.2039 RMSEA = 0.03512 RMSEA = 0.00941 Firestone Park NNFI = 0 NNFI = 1 NNFI = 0.6938 NNFI = 0.9966 NNFI = 0.471 NNFI = 0 NNFI = 0.9914 NNFI = 0.9991 CFI = 0 CFI = 0.1812 CFI = 0.8469 CFI = 0.9989 CFI = 0.8237 CFI = 0.4987 CFI = 0.9971 CFI = 0.9997 RMSEA = 0.2931 RMSEA = 0.03983 RMSEA = 0.2172 RMSEA = 0.1075 RMSEA = 0.1094 RMSEA = 0.05417 RMSEA = 0.1429 RMSEA = 0.3666 Goodyear NNFI = 0 NNFI = 0 NNFI = 0.623 NNFI = 0.9389 NNFI = 0.8223 NNFI = 0.8076 NNFI = 0.5841 NNFI = 0.6726 Heights CFI = 0 CFI = 0.6174 CFI = 0.8115 CFI = 0.9796 CFI = 0.9408 CFI = 0.9359 CFI = 0.8614 CFI = 0.8909 RMSEA = 0.4237 RMSEA = 0.02045 RMSEA = 0.1619 RMSEA = 0 RMSEA = 0.0294 RMSEA = 0.0072 RMSEA = 0.00618 RMSEA = 0 Highland NNFI = 0 NNFI = 0.995 NNFI = 0.8605 NNFI = 1 NNFI = 0.9774 NNFI = 0.9993 NNFI = 0.9998 NNFI = 1 Square CFI = 0.464 CFI = 0.9983 CFI = 0.9302 CFI = 1 CFI = 0.9925 CFI = 0.9998 CFI = 0.9999 CFI = 1 RMSEA = 0.2922 RMSEA = 0.115 RMSEA = 0.1801 RMSEA = 0.2967 RMSEA = 0.0217 RMSEA = 0.03813 RMSEA = 0.03978 RMSEA = 0.5141 Kenmore NNFI = 0 NNFI = 0 NNFI = 0 NNFI = 0 NNFI = 0.9953 NNFI = 0.08117 NNFI = 0.9876 NNFI = 0.2244 CFI = 0 CFI = 0.08708 CFI = 0.2597 CFI = 0.484 CFI = 0.9984 CFI = 0.6937 CFI = 0.9959 CFI = 0.7415 RMSEA = 0.1223 RMSEA = 0.1712 RMSEA = 0.08305 RMSEA = 0.4237 RMSEA = 0.0485 RMSEA = 0 RMSEA = 0.1163 RMSEA = 0.2345 Lane Wooster NNFI = 0.8794 NNFI = 1 NNFI = 0.912 NNFI = 0.6033 NNFI = 0.9036 NNFI = 1 NNFI = 0.9427 NNFI = 0.867 CFI = 0.9598 CFI = 0.06972 CFI = 0.956 CFI = 0.8678 CFI = 0.9679 CFI = 1 CFI = 0.9809 CFI = 0.9557 Merriman RMSEA = 0.0668 RMSEA = 0 RMSEA = 0.08496 RMSEA = 0.1548 RMSEA = 0.1467 RMSEA = 0 RMSEA = 0.03775 RMSEA = 0 Valley NNFI = 0.7621 NNFI = 1 NNFI = 0.973 NNFI = 0.9169 NNFI = 0.9373 NNFI = 1 NNFI = 0.9946 NNFI = 1 CFI = 0.9207 CFI = 1 CFI = 0.9865 CFI = 0.9723 CFI = 0.9791 CFI = 1 CFI = 0.9982 CFI = 1 RMSEA = 0.0459 RMSEA = 0.0452 RMSEA = 0.1371 RMSEA = 0.0979 RMSEA = 0 RMSEA = 0.06787 RMSEA = 0.08291 RMSEA = 0.03857 Middlebury NNFI = 0.7958 NNFI = 0.1399 NNFI = 0.8053 NNFI = 0.9501 NNFI = 1 NNFI = 0 NNFI = 0.7915 NNFI = 0.9939 CFI = 0.9319 CFI = 0.7133 CFI = 0.9026 CFI = 0.9834 CFI = 1 CFI = 0.6304 CFI = 0.9305 CFI = 0.998

128 Table 27 (continued) -- Measurement Model Fit for All Neighborhoods Structural Infrastructural Demographics Class Tax/Public Environmental Proximity Social Service RMSEA = 0.3714 RMSEA = 0.04025 RMSEA = 0.1193 RMSEA = 0.083 RMSEA = 0.1637 RMSEA = 0.1548 RMSEA = 0.1315 RMSEA = 0.5274 North Hill NNFI = 0 NNFI = 0 NNFI = 0.8822 NNFI = 0.9651 NNFI = 0.6057 NNFI = 0 NNFI = 0.916 NNFI = 0 CFI = 0 CFI = 0.5234 CFI = 0.9411 CFI = 0.9884 CFI = 0.8686 CFI = 0.2226 CFI = 0.972 CFI = 0.2555 Northwest RMSEA = 0.0198 RMSEA = 0.1719 RMSEA = 0.2748 RMSEA = 0.2622 RMSEA = 0.08451 RMSEA = 0.02165 RMSEA = 0.09242 RMSEA = 0 Akron NNFI = 0.9272 NNFI = 0.7902 NNFI = 0 NNFI = 0.4175 NNFI = 0.9653 NNFI = 0.833 NNFI = 0.9242 NNFI = 1 CFI = 0.9757 CFI = 0.9301 CFI = 0.4005 CFI = 0.8058 CFI = 0.9884 CFI = 0.9443 CFI = 0.9747 CFI = 1 RMSEA = 0.1033 RMSEA = 0.05191 RMSEA = 0.1536 RMSEA = 0.4724 RMSEA = 0.1074 RMSEA = 0.1975 RMSEA = 0.07467 RMSEA = 0.2396 South Akron NNFI = 0.8092 NNFI = 0 NNFI = 0.57 NNFI = 0 NNFI = 0.5516 NNFI = 0 NNFI = 0.9772 NNFI = 0.7557 CFI = 0.9364 CFI = 0 CFI = 0.785 CFI = 0.6555 CFI = 0.8505 CFI = 0.6003 CFI = 0.9924 CFI = 0.9186 RMSEA = 0.1306 RMSEA = 0.157 RMSEA = 0.1317 RMSEA = 0.5817 RMSEA = 0.1297 RMSEA = 0.1747 RMSEA = 0.1995 RMSEA = 0 Summit Lake NNFI = 0.5443 NNFI = 0 NNFI = 0.8311 NNFI = 0.07792 NNFI = 0.05479 NNFI = 0 NNFI = 0.8444 NNFI = 1 CFI = 0.8481 CFI = 0.01331 CFI = 0.9156 CFI = 0.6926 CFI = 0.6849 CFI = 0.2373 CFI = 0.9481 CFI = 1 RMSEA = 0 RMSEA = 0 RMSEA = 0.1273 RMSEA = 0.148 RMSEA = 0 RMSEA = 0 RMSEA = 0.1869 RMSEA = 0.2857 University NNFI = 1 NNFI = 1 NNFI = 0.5851 NNFI = 0.9654 NNFI = 1 NNFI = 1 NNFI = 0.6995 NNFI = 0.8707 CFI = 1 CFI = 1 CFI = 0.7925 CFI = 0.9885 CFI = 1 CFI = 1 CFI = 0.8998 CFI = 0.9569 RMSEA = 0.1682 RMSEA = 0.06372 RMSEA = 0.2398 RMSEA = 1.6185 RMSEA = 0.06248 RMSEA = 0 RMSEA = 0 RMSEA = 0 Wallhaven NNFI = 0 NNFI = 0.04285 NNFI = 0 NNFI = 0 NNFI = 0.8868 NNFI = 1 NNFI = 1 NNFI = 1 CFI = 0 CFI = 0.6809 CFI = 0.2865 CFI = 0 CFI = 0.9623 CFI = 1 CFI = 1 CFI = 1 RMSEA = 0.4722 RMSEA = 0.1458 RMSEA = 0.1255 RMSEA = 0.0723 RMSEA = 0.1828 RMSEA = 0.09054 RMSEA = 0.01427 RMSEA = 0.01669 West Akron NNFI = 0 NNFI = 0 NNFI = 0.7342 NNFI = 0.9859 NNFI = 0.292 NNFI = 0.7852 NNFI = 0.9989 NNFI = 0.9968 CFI = 0 CFI = 0.6354 CFI = 0.8671 CFI = 0.9953 CFI = 0.764 CFI = 0.9284 CFI = 0.9996 CFI = 0.9989

129

Factor Loadings for Measurement Models

Factor loadings represent the magnitude and direction of the direct effects of the latent factor on each measurement variable (Bollen, 1989). The value of one of the factor-measurement variable paths in each measurement model was fixed to 1.0 for scale and identification. Examination of the factor loadings allows the researcher to explore the relative impact the latent factor has on each of the measured variables. In addition to factor loadings, LISREL Version 8.8 ( & , 2006) provides standard errors and a measure of significance for each non-fixed factor loading.

Standard errors show how accurately the factor loadings have been estimated and small standard errors indicate that the factor loadings have been estimated accurately

( & , 2001). In order to determine if the factor loadings are significant

(i.e. removing them from the model will not significantly worsen the fit of the model), a t-statistic can be examined. Table 28-33 provide the standardized factor loadings and significance of each measurement variable for each of the eight Galster constructs. The tables are organized by the genesis of Akron neighborhoods presented in Chapter 2.

130 Table 28 -- Factor Loadings and Significance for Neighborhoods Developed During the Early Expansion of Akron Variable Description East South Summit Akron Middlebury Akron Lake Variable Description Parameter Standardized Parameter Standardized Parameter Standardized Parameter Standardized ______Estimate Coefficient Estimate Coefficient Estimate Coefficient Estimate Coefficient

building age -0.2889** 0.1063 -0.1147** -0.0408 -0.1282** -0.0533 building condition 1.0000 0.3679 1.0000 0.3561 1.0000, 0.4158

Struct story height -0.1002** -0.0369 0.0085 0.003 -0.6356** -0.2643

proxim. to road

special tax

Infra building setback median resident age 1.0000 0.0894 1.0000 , 0.071 1.0000 0.0724 % Black -2.4589** -0.2199 -1.3966** -0.0992 -2.2423** -0.1624 % female HH -2.6783** -0.2395 -1.7580** -0.1249 -2.5612** -0.1856

Demog % HH one male 0.7987** 0.0714 2.3000** 0.1633 0.0435 0.0032

median HH income 1.0000, 0.0377 % bachelor degree -0.2997** -0.0113

Class % use public transport 2.4554** 0.0925

tax on land proxim. to institution tax on building

Tax/Serv proxim. to school

lot size 1.0000 1

% vacant 1.3679** 1.3679 nvir E proxim. to pollution 0.6450** 0.645

proxim. to CBD -1.6252** -0.08 -0.6335** -0.0739

proxim. to city limits 2.0625** 0.1015 0.6722** 0.0784 proxim. to open space -3.7981** -0.187 -1.0406** -0.1214

Proxim. proxim. to retail 1.0000 0.0492 1.0000 0.1167

proxim. to CDC 0.5637** 0.1739 -0.1205 -0.0327 0.1595 .0366 proxim. to block club 1.0000 0.3085 1.0000 0.2713 1.0000 0.2294

Social proxim. to other agency 0.2385** 0.7778** 0.2111 -0.0985 -0.0226 Note: * indicates p < 0.05 and ** indicates p < .01

131

Table 29 -- Factor Loadings and Significance for Neighborhoods Developed Through Annexation Variable Description Ellet Kenmore Merriman Variable Description Parameter Standardized Parameter Standardized Parameter Standardized ______Estimate Coefficient Estimate Coefficient Estimate Coefficient

building age -0.5303** -0.1746 -0.3617* -0.1470 building condition 1.0000 0.3292 1.0000 0.4064

Struct story height 1.2233** 0.4027 0.1867 0.0759

proxim. to road 0.1522** 0.0676 -0.0077 -.0073

special tax 0.1547** 0.0688 0.1128 0.1072

Infra building setback 1.0000 0.4445 1.0000 0.9500 median resident age 1.0000 0.1438 % Black -2.6201** -0.3767 % female HH -2.0194** -0.2903

Demog % HH one male -0.4472 -0.0643

median HH income 1.0000 0.1481 % bachelor degree 0.1061 0.0157

Class % use public transport -0.4828 -0.0643

tax on land 0.1055** 0.0219 0.2214** 0.0419 0.1128** 0.0191 proxim. to institution 1.2760** 0.2635 1.1336** 0.2147 1.3846** 0.2348 tax on building 0.0820** 0.0171 0.2776** 0.0526 0.8128** 0.1378

Tax/Serv proxim. to school 1.0000 0.2080 1.0000 0.1894 1.0000 0.1696

lot size 1.0000 .04280 1.0000 .0939

% vacant -0.4678** -0.0200 -1.3124 -0.1233 nvir E proxim. to pollution -0.0965 0.4712 0.0443

proxim. to CBD 0.2919 0.0786

proxim. to city limits -1.5942** -0.4290 proxim. to open space 1.1484** 0.3090

Proxim. proxim. to retail 1.0000 0.2691

proxim. to CDC 0.8945** 0.1106 0.3765 0.0852 proxim. to block club 1.0000 0.1236 1.000 0.2262

Social proxim. to other agency 0.9510** 0.1175 0.7956 0.1800 Note: * indicates p < 0.05 and ** indicates p < .01

132 132

Table 30-- Factor Loadings and Significance for Industrial Neighborhoods Variable Description Goodyear Heights Firestone Park Variable Description Parameter Standardized Parameter Standardized ______Estimate Coefficient Estimate Coefficient

building age building condition

Struct story height

proxim. to road

special tax Infra building setback median resident age

% Black % female HH

Demog % HH one male

median HH income 1.0000 .0.0648 1.0000 0.1092 % bachelor degree 0.2174** 0.0141 0.1311** 0.0143 Class % use public transport -1.4697** -.0.0952 -1.0884** -0.1189

tax on land 0.1113** 0.0246 proxim. to institution 0.5096** 0.1127 tax on building .1662** 0.0367

Tax/Serv proxim. to school 1.0000 0.2211

lot size 1.0000 0.0203

% vacant -5.0277** -0.1023 nvir E proxim. to pollution 3.1747** 0.0646 proxim. to CBD

proxim. to city limits proxim. to open space

Proxim. proxim. to retail

proxim. to CDC - 0.3402** -0.0831 proxim. to block club 1.0000 0.2444

Social proxim. to other agency -0.1037 -0.0253 Note: * indicates p < 0.05 and ** indicates p < .01

133

Table 31-- Factor Loadings and Significance for Neighborhood Retail Centers Variable Description Highland Square North Hill Wallhaven Variable Description Parameter Standardized Parameter Standardized Parameter Standardized ______Estimate Coefficient Estimate Coefficient Estimate Coefficient

building age building condition

Struct story height proxim. to road - 0.1054** -0.0457

fra special tax 0.3533** 0.1532

In building setback 1.0000 0.4335 median resident age 1.0000 0.1099 1.0000 0.0703 % Black -3.3462** -0.3677 -3.9133** -0.2750 % female HH -2.9086** -0.3196 -3.1377** -0.2202 Demog % HH one male 0.1720** -0.0189 0.5504 -0.0387

median HH income 1.0000 0.1514 1.0000 0.1263 % bachelor degree 0.2022** 0.0306 0.0781 0.0099

Class % use public transport -1.8242** -0.2762 -1.6590 -0.2095

tax on land -3.3449 -0.0898

proxim. to institution -0.4045 -0.0109 /Serv tax on building -8.6227** -0.2315 Tax proxim. to school 1.0000 -0.0269

lot size 1.0000 0.0130 1.0000 0.0347

% vacant -24.6882* -0.3216 1.4514 0.0504 nvir E proxim. to pollution 4.1972** 0.0547 -0.1406 -0.0049

proxim. to CBD

proxim. to city limits proxim. to open space Proxim. proxim. to retail

proxim. to CDC - 0.0053 -0.0015 0.1195 0.0372 proxim. to block club 1.0000 0.2859 1.0000 0.3111

Social proxim. to other agency 0.3126 0.0894 0.2615 0.0814 Note: * indicates p < 0.05 and ** indicates p < .01

134

Table 32-- Factor Loadings and Significance for Large Retail/Service Neighborhoods Variable Description Chapel Hill University Park Variable Description Parameter Standardized Parameter ______Estimate Coefficient Estimate Standardized Coefficient

building age -0.3579** -0.1261 -0.0879** -0.0335 building condition 1.0000 0.3524 1.0000 0.3812

Struct story height 0.4417 0.1557 -0.0492 -0.0188

proxim. to road -0.0987** -0.0217

special tax 0.0508 0.0112 Infra building setback 1.0000 0.2201 median resident age

% Black % female HH

Demog % HH one male

median HH income 1.0000 1.0000 0.0835 % bachelor degree -0.0203 -0.3439** -0.0287 Class % use public transport -2.8132** 2.9091** 0.2429

tax on land -0.1209 proxim. to institution 1.0721 tax on building -0.0295

Tax/Serv proxim. to school 1

lot size 1.0000 0.0177 1.0000 1.0000

% vacant 3.5518 0.0628 -0.4723 -0.4723 nvir E proxim. to pollution 4.5705 0.0809 0.1058 0.1058 proxim. to CBD

proxim. to city limits proxim. to open space

Proxim. proxim. to retail

proxim. to CDC 0.1078 0.0119 1.1794** 0.1301 proxim. to block club 1.0000 0.1100 1.0000 0.1103

Social proxim. to other agency 1.5137 0.1665 1.2278** 0.1355 Note: * indicates p < 0.05 and ** indicates p < .01

135

Table 33-- Factor Loadings and Significance for Neighborhoods Defined by Socio-economic Status Variable Description Fairlawn Heights NW Akron West Akron Variable Description Parameter Standardized Parameter Standardized Parameter Standardized ______Estimate Coefficient Estimate Coefficient Estimate Coefficient

building age -0.1649** -0.0607 -0.1002** -0.0386 building condition 1.0000 0.3679 1.0000 0.3852

Struct story height 0.4559 0.1677 0.3213** 0.1238

proxim. to road 0.0103 -0.0025 0.3248** 0.1587

special tax 0.1180 0.0287 0.2350** 0.1148

Infra building setback 1.0000 0.2433 1.0000 0.4887 median resident age % Black % female HH Demog % HH one male

median HH income 1.0000 0.1456 % bachelor degree 0.1346** 0.0196

Class % use public transport -1.7637 -0.2565

tax on land 0.3379** 0.0569 0.5106** 0.0786 proxim. to institution 1.9845** 0.3344 2.1547** 0.3318 tax on building 0.1128 0.0190 0.2652** 0.0408 Tax/Serv proxim. to school 1.0000 0.1685 1.0000 0.1504

lot size 1.0000 0.0596 1.0000 0.0228

% vacant 0.3432** 0.0204 -5.7787** -0.1316 nvir E proxim. to pollution -0.1248** 0.0074 0.9245** 0.0210

proxim. to CBD 0.9419 0.0583 -1.9459** -0.1126 0.9530** 0.1055

proxim. to city limits -4.2902** -0.2657 3.6188** 0.2094 -1.4652** -0.1623 proxim. to open space 1.6417** 0.1016 1.5316** 0.0886 -0.3217** -0.0356 Proxim. proxim. to retail 1.0000 0.0619 1.0000 0.0579 1.0000 0.1107

proxim. to CDC 0.5982** 0.0703 0.8438** 0.1259 -0.0031 -0.0009 proxim. to block club 1.0000 0.1175 1.0000 0.1492 1.0000 0.2834

Social proxim. to other agency 0.6090** 0.0715 0.4248** 0.0634 -0.1219 0.0345 Note: * indicates p < 0.05 and ** indicates p < .01

136

Table 33 (continued) -- Factor Loadings and Significance for Neighborhoods Defined by SES Variable Description Elizabeth Park Lane Wooster Variable Description Parameter Standardized Parameter Standardized ______Estimate Coefficient Estimate Coefficient

building age -0.2807** -0.1404 -0.2600** -0.1672 building condition 1.0000 0.4999 1.0000 0.6429

Struct story height -0.5164** -0.2582 -0.5095** -0.3276

proxim. to road

special tax

Infra building setback median resident age 1.0000 0.1075 % Black 0.3431** 0.0369 % female HH -1.5404** -0.1657

Demog % HH one male 0.2349** 0.0253

median HH income 1.0000 0.1159 % bachelor degree -0.0421** -0.0049

Class % use public transport -1.4091** -0.1633

tax on land -1.5362** -0.0954 proxim. to institution -0.2710 -0.0168 tax on building -3.4753** -0.2158

Tax/Serv proxim. to school 1.0000 0.0621

lot size 1.0000 0.0087

% vacant -20.4882** -0.1781 nvir E proxim. to pollution 18.1613** 0.1579

proxim. to CBD 1.3719** 0.1165

proxim. to city limits -1.1691** -0.0993 proxim. to open space 2.3241** 0.1974

Proxim. proxim. to retail 1.0000 0.0850

proxim. to CDC - 0.0455 -0.0105 0.5247** -0.01227 proxim. to block club 1.0000 0.2317 1.0000 0.2339

Social proxim. to other agency 0.2174** 0.0504 -0.2556** -0.0598 Note: * indicates p < 0.05 and ** indicates p < .01

137

Each of the preceding tables provide the parameter estimate, its significance and a standardized value for the parameter estimate. Parameter estimates are the factor loadings (lambdas) from the single factor congeneric measurement models. According to Bollen (1989, p. 17), these parameter estimates are “the magnitude of the expected change in the observed variable for a one unit change in the latent variable.” The significance of the parameter estimates was determined by dividing the parameter estimate by its standard error to produce a t statistic. Variables are considered significant if removing them from the model will not significantly worsen the fit of the model ( & , 2001). The standardized coefficients are rescaled from the original parameter estimates after the latent variables have been standardized (i.e. a mean of zero and a standard deviation of one).

Discussion

Examining tables 28-33 as a whole there appears to be wide variation in the magnitude of the factor loadings. This wide range of values is consistent with Galster’s assertion that the degree of presence of the model constructs could vary from neighborhood to neighborhood. One issue of some concern is that the sign of the factor loadings for some variables change from neighborhood to neighborhood. However, unlike the natural sciences where truly universal laws and relationships exist, in the social sciences the nature of relationships can vary from location to location. This non- universality of models or relationships in the social sciences in termed spatial non-

138

stationarity. Fotheringham, Brunsdon and Charlton (2002, p. 9) describe this phenomenon in this way, “an explanatory variable might be highly relevant in one application [location] but seemingly irrelevant in another ... parameters describing the same relationship might be negative in some applications but positive in others.” The demographic construct in Galster’s model provide an example of spatial non- stationarity. The factor loading for one of the variables, percentage Black population, representing the demographic construct is negative and significant in all but one of the neighborhoods. The negative parameter estimate indicates that when degree of presence of the demographic component increases the percentage of Black population decreases. This relationship holds true in all instances where the variable is significant except one, the Lane Wooster neighborhood. The parameter estimate for the %Black variable is positive in this neighborhood indicating that the degree of presence of the demographic construct increases the percentage Black population increases.

Interestingly, the Land-Wooster neighborhood is predominantly Black (87%)and has always been a predominately Black neighborhood. It was usually the first place where

Blacks settled when they moved to Akron from the South in the early 20th century (City of Akron, 2007).

In general, Tables 28-33 show that the signs of the loadings for each factor are consistent within the genesis typology presented in Chapter 2. This consistency of pattern in the factor loadings hints that the factors influencing the genesis of each neighborhood might still have influence in the contemporary neighborhood.

139

Table 34-- Factor Loadings, Standard Errors and Significance of Measurement Models for the City-wide Model

Construct Factor t-value Standardized Variable Description Loading loading Structural building age -0.2692 -55.3468 -0.1125 story height -0.1361 -26.1869 -0.0569 building condition 1.0000 0.4179 Infrastructure building setback 1.0000 0.3359 proximity to road 0.0265 4.4694 0.0094 tax assessment 0.1243 28.3022 0.0442 Demographic median resident age 1.0000 0.1137 % Black -1.7415 -23.1805 -0.1980 % HH one male 0.1553 7.8437 0.0177 % female HH -1.7265 -24.6393 -0.1963 Class median HH income 1.0000 0.1490 % use public transp. -1.2509 -122.6489 -0.1864 % bachelor degree 0.1585 169.1555 0.0236 Tax/Public proximity to school 1.0000 0.1503 proximity to institution 1.7270 64.9536 0.2595 tax on land 0.6082 63.9652 0.0914 tax on building 0.8943 57.3789 0.1344 Environmental lot size 1.0000 0.0332 % vacant -2.8292 -17.2075 -0.0914 proximity to pollution 2.7057 19.3130 0.1344 Proximity proximity to retail 1.0000 0.1104 proximity to open space 0.2938 16.3529 0.0324 proximity to city limits -2.8959 -77.6792 -0.3197 proximity to downtown 1.5756 61.9988 0.1739 Social Interactive proximity to block club 1.0000 0.4059 proximity to CDC 0.5168 172.9717 0.2098 proximity to other agency 0.4637 136.4625 0.1882

In addition to estimating the measurement models for each construct for each neighborhood separately, measurement models for each of the eight constructs were evaluated for the city as a whole (N=67,389) . Table 34 presents the factor loadings,

140

significance and standardized loadings for the measurement models for each of the eight constructs. The signs of the factor loadings are consistent with the neighborhood models and all variables are highly significant at the city level.

Item Reliability

The second step in determining if the measurement models are adequate is to examine the internal reliability of the various measurement variables used in each of the constructs. Internal reliability supports the assertion of Kline (1998, p.190) that “to claim that a set of indicators assesses some common construst, data from the indicators should be consistent with certain predictions.” In order to assess the internal consistency of the measurement variables for each construct, LISREL provides a measure of the squared multiple correlations. The squared multiple correlation is a measure of the strength of the linear relationship between the measurement variables

( & 2001) and can be expressed mathematically as , where is the estimated error variance and is the total variance of the i-th measurement variable ( & 2001, p. 26). Variables with high squared multiple correlations are considered good measures of the latent constructs. The internal reliability for each of the measurement models for each neighborhood are shown graphically in the Appendix. The reliability of the measurement variables vary greatly across the neighborhoods.

141

The acceptable internal reliability coefficients reported across many of the neighborhoods and constructs in Figures 38-56 indicate that the measurement variables are reasonable indicators of the constructs in at least one of the neighborhoods.

The overall empirical fit, significant factor loadings and internal consistency of the measured variables indicate that the measurement models adequately capture the presence of the unobserved or latent constructs and are appropriate for further analysis. With an adequate tool for measuring the presence of the latent constructs, the interrelationship among the latent factors can be explored through structural analysis.

These interrelationships will be explored in the next chapter.

Chapter 8

Results: Interrelationships Among Galster’s Concepts

Introduction

Implicit in Galster’s conceptual model is that neighborhood is a composite commodity (Galster 2001, p. 2111). The various constructs that comprise neighborhood can potentially interact or influence one another (e.g. demographics and class status).

In the context of structural equation modeling, the potential interactions among constructs will be manifested as covariances among the first-order factors in the confirmatory factor model. To further the goal of empirically verifying Galster’s conceptual model of neighborhood, this chapter identifies the component concepts of neighborhood which are significantly related to each other.

Confirmatory Factor Analysis of Galster’s Model

The previous chapter confirmed the measurement models for each of the constructs in Galster’s conceptual model of neighborhood. However, explicit in

Galster’s explanation of his model is that these constructs interact or influence each other to create a degree of presence of neighborhood. In order to operationalize this aspect of Galster’s model, all single-factor congeneric measurement models were combined to form a nine factor confirmatory factor analysis model (Figure 62). This

142 143

nine factor confirmatory factor model was evaluated for each of the 19 neighborhoods in Akron. One factor loading was fixed to zero for each of the constructs and the factor covariances were all set to free. As with single factor congeneric measurement models, it is important to determine the overall empirical fit of the confirmatory factor model before interpreting any parameter estimates. Table 35 summarizes the global measures of fit for each of the neighborhoods. Although the global fit is adequate in the majority of the neighborhoods (11 out of 19), some neighborhoods show marginal fit. The neighborhoods of East Akron, Kenmore, Goodyear Heights, North Hill, University,

Northwest Akron and Lane Wooster had marginal fit on the nine factor confirmatory factor model. These neighborhoods represent all of the neighborhood genesis types.

All but one of the neighborhoods in the early expansion group of neighborhood (East

Akron, Middlebury, South Akron and Summit Lake) had adequate fit on the nine factor confirmatory factor model. As was demonstrated in Chapter 8 using single-factor congeneric measurement models, not all constructs are present to the same degree in each neighborhood. In fact, some constructs may be virtually missing in some neighborhoods. These ill-fitting or missing measurement models were forced into the nine factor confirmatory factor model so that the same model (i.e. measurement variables and latent factors) could be evaluated for all of the neighborhoods. Arguably, this is why some of the fit indices listed in Table 27 are marginal. Also, no post hoc modifications were made to the confirmatory factor models in order to improve overall model fit. For example, in the ill-fitting models error covariances could have been freed

144

to improve fit. However, freeing error covariances in one neighborhood to improve fit would have resulted in different models being evaluated in different neighborhoods. It was deemed more important to test equivalent models across all neighborhoods rather than having ideal model fit in all neighborhoods.

Another potential reason for the ill-fitting models in some of the neighborhoods is the presence of secondary loadings for some of the measurement variables. A secondary loading indicates that a measurement variable loads or more than one latent factor thus violating the assumptions of simple form. Model fit indices are based on the assumption that a measurement variable loads on a single factor. Examination of model modification indices for the eight neighborhoods with ill-fitting models shows the presence of secondary loadings on several variables. For example, the variable proximity to city limits used as an indicator for the proximity construct is also loading on the class construct. This relationship is reasonable given that in Akron, Ohio, the neighborhoods where a high degree of presence of the class construct is expected are located on the outskirts of the city away from the deteriorating city core. Another example of secondary loadings occurs in the measurement variable proximity to community development corporation (CDC). The proximity to CDC variable was used as a proxy for the social/interactive construct. However, it is also loading on the demographics constructs. Again, this relationship is expected given that community development corporations are located in socio-economically deprived areas often with very homogeneous demographics. Although an in-depth examination of these

145

secondary loadings could prove interesting in formulating an alternative conceptual model of neighborhood, the goal of this dissertation is to empirically verify the initial model as set forth by Galster.

The estimation algorithm did converge in all neighborhoods indicating that the model is reasonably identified and that there is some degree of fit between the measurement variables and the constructs. The adequate global fit indices present in the majority of the neighborhoods and the empirical convergence in all of the neighborhood models provides sufficient evidence that the parameter estimates from the confirmatory factor models can used to explore the relationships among the different constructs in Galster’s model.

146

Table 35-- Fit Indices for Neighborhood Confirmatory Factor Analysis Models Genesis Neighborhood RMSEA NNFI CFI Early Expansion East Akron 0.1007 0.7985 0.8279 *Middlebury 0.0452 0.9464 0.9542 *South Akron 0.0409 0.9687 0.9733 *Summit Lake 0.0000 1.0000 1.0000

Annexation *Ellet 0.0684 0.8833 0.9003 Kenmore 0.1145 0.6730 0.7206 *Merriman Valley 0.0000 1.0000 1.0000

Industrial Goodyear Heights 0.1128 0.7411 0.7780 Neighborhood *Firestone Park 0.0383 0.9543 0.9609

Neighborhood *Highland Square 0.0000 1.0006 1.0000 Retail North Hill 0.1197 0.7592 0.7943 *Wallhaven 0.0000 1.0000 1.0000

Lrg. Scale Retail *Chapel Hill 0.0069 0.9991 0.9992 or Service University 0.1176 0.7699 0.8034

Socio-economic *Fairlawn Heights 0.0430 0.9794 0.9820 Status Northwest Akron 0.0957 0.8434 0.8662 *West Akron 0.0428 0.9647 0.9698 Elizabeth Park 0.0873 0.8648 0.8845 Lane Wooster 0.1340 0.6504 0.7013 * indicates an acceptable fit on at least two fit indices

147

Figure 60 -- Path Diagram of Galster's Conceptual Model of Neighborhood

148

Relationships Among Factors at the Construct Level

Determining the degree to which the various components of Galter’s neighborhood model are interacting will be accomplished in two stages. First, the overall pattern of factor covariance will be explored. Second the standardized factor correlations will be examined to determine the magnitude of the relationships among factors. Examining which pairs of factors (e.g. demographic and class, political and social) are significantly correlated will help determine if there are particular combinations of Galster’s neighborhood concepts which interact to produce neighborhood. Arguably, as the number of significant factor covariances increases, the neighborhood could be viewed as being more complex (i.e. there are more concepts interacting to produce a sense of neighborhood). Also, significant covariance among the primary factors (constructs) implies that there is a higher-order factor (concept) causing the covariance. The presence of a higher-order concept is not explicit in

Galster’s model and empirical evaluation of the presence of a higher-order factor is beyond the scope of this dissertation. Further, before a higher-order factor could be evaluated the first-order conceptual model (as originally proposed by Galster) needs to be verified. Table 37 summarizes the number of factor pairs which are significantly correlated in each of the nineteen neighborhoods. Factor covariances were freely estimated in the confirmatory factor model resulting in some degree of covariance among all factor pairs. However, as in the case of factor loadings, LISREL provides the magnitude and sign of the covariance, the standard error and an indication as to

149

whether the factor covariance is statistically significant. For each neighborhood, Table

37 shows which factor pairs (class-demographic, structural-demographic) are significantly correlated. Note that some factor pairs are significant in nearly all of the neighborhoods while some factor pairs are significantly correlated in only one of the neighborhoods. Three factor pairs with wide-spread significant correlations (i.e. they were significantly correlated in 17 or 18 of the 19 neighborhoods) were selected for discussion. In rank order, these factor pairs are environmental-infrastructure, class- demographic and class-tax/public service. Table 36 summarizes the standardized covariances (correlations) for these three pairs of factors.

150

Table 36 -- Most Common Factor Interrelationships Class – Environmental - Class - Tax/Public Genesis Neighborhood Infrastructure Demographics Service Early Expansion East Akron +* +* Middlebury +* -* -* South Akron +* +* -* Summit Lake +* +* +*

Annexation Ellet +* -* +* Kenmore +* +* -* Merriman +*

Industrial Goodyear Heights +* +* -* Neighborhood Firestone Park +* +* -*

Neighborhood Highland Square +* +* -* Retail North Hill +* +* +* Wallhaven -* -*

Lrg. Scale Retail Chapel Hill +* -* or Service University +* +* -*

Socio-economic Fairlawn Heights +* +* +* Status NW Akron +* +* +* West Akron +* +* +* Elizabeth Park +* +* -* Lane Wooster +* -* plus sign indicates a positive correlation between factors, negative sign indicates a negative correlation between factors, * indicates p-value < .05

Environmental—Infrastructure

As shown in Table 36, there are significant positive correlations between the environmental and infrastructure constructs of Galster’s model in all Akron neighborhoods except Wallhaven. The environmental construct in Galster’s model captures aspects of the physical environment of the residents and was operationalized

151

in Akron by measuring the proximity to pollution, the amount of vacant housing and the lot size for homes. The infrastructure constructs also captures aspects of the physical environment, specifically those provided by local government and public utilities. The infrastructure component was captured by measuring characteristics of house lots and the level of improvements to the infrastructure in a neighborhood. Since both the environmental and infrastructure components capture aspects of the physical environment of a neighborhood a strong relationship between these constructs is expected.

Class—Demographics

The demographic construct in Galster’s model captures characteristics of the residents and was operationalized by measuring the age distribution, race and household structure of neighborhoods. In general, as the degree of presence of the class construct increases the degree of presence of the demographic construct increases. Again, the linkage between class and demographics is clear. Although social class is often difficult to quantify, few would argue against its linkages to a privileged white majority and specific family structures.

Class—Tax/Public Service

Galster’s construct of social class relates to the characteristics of the residents in a neighborhood and was operationalized in Akron by capturing household income, level of use of public transportation and educational attainment. The tax/public service

152

component was designed to capture the level and quality of public services (education, safety) as it relates to the assessment of local taxes. In Akron, the tax/public service component was operationalized by measuring proximity to the nearest school or other public institution and the level of local taxes assessed to each property. The relationship between the construct of class and tax/public service seems plausible. Meade and

Earickson (2000) indicate that “for the poorest populations in the United States, place of residence is a significant factor in the access and utilization of health service [an increasingly important component of public service+.” The relationship between class and the tax portion of the tax/public service component is clear given that local taxes are assessed based on the assessed value of real property in the city. Arguably, residents with a higher socio-economic class would more likely be homeowners and would live on larger lots with more expensive homes.

Patterns of Linkages between Concepts in Galster Model

The significant factor correlations (relative for ecological data) indicate that the various components of Galster’s model are interacting to contribute to the degree of presence of neighborhood and that measurement models alone are not sufficient to capture the various concepts in Galster’s model. Taking the first factor pair as an example, environment and infrastructure contribute to the presence of neighborhood but the interaction of environment and infrastructure also contributes significantly. To more fully capture both the measurement of Galster’s concepts and the interactions

153

among the constructs, factor score will be explored. Outlined in the next chapter, the calculation of factor scores will allow the exploration of Galster’s conceptual model across space and will help determine to what extent each concept (factor) exists in each of the Akron neighborhoods can be explored.

154

Table 37 -- Summary of Factor Covariances by Neighborhood

Number of

Neighborhoods

with

Significant

Factor

Covariances

Factor Pairs Park lizabeth (x/19)

Chapel Hill Chapel East Akron E Ellet Heights Fairlawn Park Firestone Heights Godyear Square Highland Kenmore Lane Wooster Merriman Middlebury Hill North Akron NW Akron South Lake Summit University Wallhaven Akron West INFRA STRUCT -* -* -* -* -* -* -* -* +* -* -* 11 DEMOG STRUCT +* +* +* +* + +* + +* +* - * +* 9 DEMOG INFRA +* - * +* +* -* +* -* +* +* +* 10 TAXPUB STRUCT - * +* +* -* +* - * +* +* -* +* +* 11 TAXPUB INFRA - * +* +* +* -* +* +* -* +* -* -* +* - * 13 TAXPUB DEMOG -* +* - * -* +* +* -* +* - * +* +* +* +* -* +* + - * 16 CLASS STRUCT +* +* +* +* +* +* +* +* +* -* +* 11 CLASS INFRA +* +* +* +* - * +* +* + +* +* +* +* 11 CLASS DEMOG - * +* +* -* +* +* +* +* +* -* +* +* +* +* +* -* +* 17 CLASS TAXPUB +* -* +* +* -* -* -* -* - * -* +* +* -* +* -* -* +* 17 ENVIRON STRUCT +* +* +* +* +* +* +* + +* +* +* +* 11 ENVIRON INFRA +* +* +* +* +* +* +* +* +* +* +* +* +* +* +* +* +* +* 18 ENVIRON DEMOG +* +* +* -* +* +* +* +* +* +* +* +* 12 ENVIRON TAXPUB -* +* +* +* - * +* +* +* - * +* + +* -* - * +* 14 ENVIRON CLASS -* -* -* +* +* -* +* +* +* -* -* -* +* +* + +* +* 16 PROXIM STRUCT +* +* +* +* +* +* +* +* +* +* +* +* 12 PROXIM INFRA +* +* +* - * +* +* +* 7 PROXIM DEMOG +* +* +* + +* +* +* +* +* - * +* +* +* +* - * +* 15 PROXIM TAXPUB - * + +* +* +* +* - * +* +* +* +* + -* -* +* 13 PROXIM CLASS +* +* +* +* +* +* +* -* +* +* +* - * +* +* 14 plus sign indicates a positive correlation between factor s, negative sign indicates a negative correlation between factors, * indicates p-value < .05

155

Table 37 (continued) -- Summary of Factor Covariances by Neighborhood

Number of

Neighborhoods

with

Significant Factor

Covariances

Factor Pairs st Akron (x/19)

Chapel Hill Chapel Ea Park Elizabeth Ellet Heights Fairlawn Park Firestone Heights Godyear Square Highland Kenmore Lane Wooster Merriman Middlebury Hill North Akron NW Akron South Lake Summit University Wallhaven Akron West PROXIM ENVIRON +* +* +* +* +* +* +* +* +* +* +* 11 POLIT STRUCT - + +* + 1 POLIT INFRA +* +* +* +* +* +* 6 POLIT DEMOG + -* +* 2 POLIT TAXPUB - * - * 2 POLIT CLASS +* 1 POLIT ENVIRON +* +* + +* 3 POLIT PROXIM +* +* +* +* 4 SOCIAL STRUCT +* +* - * +* +* +* +* +* - * 9 SOCIAL INFRA + +* +* -* +* +* 5 SOCIAL DEMOG +* +* +* + +* +* +* +* - * -* +* +* -* +* 13 SOCIAL TAXPUB -* +* -* +* +* +* +* -* -* +* - * -* +* +* -* - * 16 SOCIAL CLASS -* + +* +* +* +* +* +* +* +* +* +* 11 SOCIAL ENVIRON +* +* +* -* -* +* +* +* 8 SOCIAL PROXIM -* +* +* +* -* +* +* - * +* +* +* 11 SOCIAL POLIT -* +* 2 Number of Significantly Correlated Latent 12 18 17 26 16 14 24 23 28 21 18 15 21 30 17 16 20 7 20 Factor Pairs (x/36) plus sign indicates a positive correlation between factor, negative sign indicates a negative correlation between factors, * indicates p-value < .05

Chapter 9

Results: Examining Galster’s Model at the Micro-Scale

Introduction

In previous chapters the validity of Galster’s conceptual model was verified by confirming the existence of the constructs comprising the model and confirming

Galster’s assertion that the constructs would interact with one another. This chapter moves toward the third goal of this dissertation, to inform the local geography of Akron,

Ohio. Latent variable scores will be used to answer the question “to what degree does each of Galster’s constructs exist in each of the Akron neighborhoods? This chapter provides a brief introduction to the method of calculating latent variable scores and then explores the spatial distribution of each of the eight model constructs.

Method

Unlike traditional statistical techniques (e.g. regression), Bollen (1989) states that structural equation modeling emphasizes global covariance matrices rather than investigating individual observations. Bollen’s tenet held true until relatively recent advances in structural equation modeling theory. et al. (2006) introduced a method by which latent variable scores could be calculated for each individual observation in a set of data. Latent variable scores can be defined as an individual

156 157

observation’s relative standing on a latent variable (Grice, 2001). In the context of

Galster’s neighborhood model, the calculation of latent variable scores will illustrate the relative standing of each observation (parcel) on each of the nine model components

(factors). These individual latent variable scores can be mapped and will assist in answering the question “to what degree does each latent variable (factor) exist in each of the neighborhoods?” It is important to distinguish latent variable scores from the more common factor scores computed in traditional exploratory factor analysis.

Although factor scores also represent an observation’s relative standing on a latent factor, they suffer from mathematical indeterminacy. Indeterminacy occurs when the same set of observed variables produce different factor scores and has been a known problem in factor analytic techniques since the 1920s (Acito & Anderson, 1986). In CFA models a variable loads generally on a single factor. For a set of variables , the variable is:

(13)

(14)

(15)

(16)

Since a score, , consists of true score ( ) and error , factor scores generated from measurement Equations 13-16 are not influenced by the error term and therefore not indeterminate. Latent variable scores reduce the issue of indeterminacy

158

by focusing on the true score and isolating the error, . Ultimately the goal of the latent variable scores algorithm is to “produce latent variable scores that have the same covariance matrix as the latent variables themselves” ( et al., 2006, p. 1).

A detailed discussion of the derivation of latent variable scores can be found in J reskog

(2000).

Calculating Latent Variable Scores for Akron, Ohio

In order to map latent variable scores across the entire extent of the city, observations from all neighborhoods were combined into a single dataset and a single nine-factor confirmatory factor analysis model was estimated for the entire city. The overall fit of the model was adequate (RMSEA= 0.0846, NNFI = 0.8875 and CFI = 0.9062).

Table 38 shows the standardized parameter estimates and their associated reliabilities for each of the measurement variables. This model produced latent variable scores for each of the nine constructs in Galster’s model. Given that the Akron dataset contains over 67,000 observations, mapping the latent variable scores as points yielded maps that were not easily interpretable. To enhance the visualization of the spatial distribution of the latent variable scores, a surface was created from the points through inverse distance weighted interpolation. Inverse distance weighted interpolation is a technique which produces a smoothed continuous surface from irregularly space point data (Shepard, 1968).

159

Table 38 -- Squared Multiple Correlation Coefficients for Measurement Variables Construct Standardized Internal Reliability Variable Description Parameter of Measurement Estimate Variables (R2)

building age -0.1578* 0.5904 Structural story height -0.1270* 0.2268 building condition 0.2817* 0.4529 building setback 0.1581* 0.2018 Infrastructure proximity to road 0.0253* 0.0040 tax assessment 0.0969* 0.0896 median resident age 0.0477* 0.1639 % Black -0.5801* 0.6520 Demographic % HH one male -0.0382* 0.0100 % female HH -0.3049* 0.4112 median HH income 0.1270* 0.7027 Class % use public transp -0.2070* 0.2808 % bachelor degree 0.0270* 0.5432 proximity to school 0.0684* 0.0674 Tax/Public proximity to institution 0.1516* 0.2167 Service tax on land 0.1660* 0.7341 tax on building 0.1889* 0.3119 lot size 0.0247* 0.2958 Environmental % vacant -0.1257* 0.0743 proximity to pollution 0.1119* 0.1332 proximity to retail 0.1231* 0.1349 proximity to open space 0.0278* 0.0042 Proximity proximity to city limits -0.3468* 0.6853 proximity to downtown 0.1576* 0.7912 proximity to block club 0.3229* 0.6269 Social proximity to CDC 0.2751* 0.6580 Interactive proximity to other agency 0.2188* 0.3984 * indicates p-value < .05

Since large areas of Akron are non-residential and could potentially interfere with the interpretation of the map, any non-residential area was symbolized as grey on the map.

Maps were created for each of the nine constructs of the Galster model and are

160

summarized below. Bar charts provide the average factor score for each of the constructs in each of the neighborhoods.

Structural

The structural component of Galster’s model captures characteristics of the buildings comprising a neighborhood. A strong spatial pattern for the factor scores representing the structural component of Galster’s model is apparent in Akron. Factor scores represent the relative standing of each observation on the latent variable. In the map representing factor scores for the structural component (Figure 63), a high latent variable score, symbolized in red, indicates that the “degree of presence” of the structural construct is strong in this area in comparison to the overall study area.

161

Figure 61-- Spatial Patterns of the Structural Component

Conversely, areas symbolized in blue indicate that the “degree of presence” of the structural component is relatively weak in this area. The “degree of presence” of the structural characteristics of the buildings making up the neighborhoods – the structural construct -- is very strong in the areas surrounding downtown. Downtown developed around the sites of the two original settlements from which Akron grew and has some

162

of the oldest housing stock in the city and is predominantly made up of two story residential dwellings. In contrast, in areas on the outskirts of the city, specifically the

Northwest Akron and Fairlawn Heights neighborhoods, the structural component is not as prevalent. These are some of the newest and most affluent neighborhoods in the city and both contain a fair number of single story homes.

Figure 64 shows the average factor scores for the structural component by neighborhood and neighborhood genesis. Two notable patterns are apparent. For all of the neighborhoods in the early expansion group, the factor scores representing the structural component are all positive and relatively high. The second notable pattern appears in the SES group of neighborhoods. Two distinct subgroups are apparent here.

First, the three neighborhoods with low socio-economic status (West Akron, Lane

Wooster, and Elizabeth Park) have high positive average factor scores on the structural component. Second, the two neighborhoods with high socio-economic status

(Northwest Akron and Fairlawn Heights) have highly negative average factor scores.

163

Figure 62 – Average Factor Scores by Neighborhood and Genesis for the Structural Component

Infrastructure

The infrastructure component in Galster’s model was intended to capture enduring physical aspects of the neighborhood. The infrastructure component was operationalized with the variables; building setback, proximity to a major road and the taxes assessed for special infrastructure projects. The infrastructure component is weak in the areas adjacent to downtown (Figure 65). This area represents the oldest neighborhoods in the city where houses were build on smaller lots very near the road.

Also, since many of the major streets intersect near downtown, the houses is this area are not far from a major road. In contrast the infrastructure component is quite high in the neighborhoods of Fairlawn Heights and Northwest Akron. In these neighborhoods, the lots are large and houses are often set far back from the street. These

164

neighborhoods are bisected by only one major street, West Market Street, therefore in comparison to rest of the city, houses in these neighborhoods are far from a major road.

Figure 63-- Spatial Patterns of Infrastructure Construct

Figure 66 shows the average factor scores for the structural component by neighborhood and neighborhood genesis. Following a similar pattern to the demographic component, average factor scores have very high or very low values in the

165

early expansion and socio-economic genesis groups indicating that the infrastructure component is particularly important in these two groups of neighborhoods.

Figure 64 -- Average Factor Scores by Neighborhood and Genesis for the Infrastructural Component

Demographic

The demographic component of Galster’s model captures characteristics of the residents who make up each neighborhood. In Akron, the variables used to operationalize the demographic construct were age distribution, race and household structure. Notable spatial patterns in the demographic construct include high values in the predominantly Black neighborhood of West Akron and low values in the predominantly White neighborhoods of Kenmore and Ellet (Figure 67).

166

Figure 65-- Spatial Patterns of Demographic Construct

Figure 68 shows the average factor scores for the demographic component by neighborhood and neighborhood genesis. The annexed neighborhoods of Kenmore and

Ellet have high average factor scores on the demographic component. Both of these neighborhoods are predominantly white (one variable used to measure the demographic component). The neighborhoods in the SES group with low socio-

167

economic status (West Akron, Lane Wooster and Elizabeth Park) have highly negative average factor scores. These neighborhoods are predominantly Black.

Figure 66 -- Average Factor Scores by Neighborhood and Genesis for the Demographic Component

Class

The class constructs in Galster’s model captures the income and educational attainment of the neighborhood residents. Following a similar pattern of the other constructs, the latent variable scores representing class are the lowest in the areas surrounding downtown. The areas with the highest latent variable scores for class are almost all located on the outskirts of the city (Figure 69).

168

Figure 67-- Spatial Patterns of Social Class Construct

Figure 70 shows the average factor scores for the class component by neighborhood and neighborhood genesis. Both neighborhoods in the large retail/service genesis group show highly negative average factor scores on the class component. This pattern is most likely due to the low median income and heavy use of public transportation in these neighborhoods. The early expansion group of

169

neighborhoods also show highly negative average factor scores most likely the result of low median income in these areas.

Figure 68 -- Average Factor Scores by Neighborhood and Genesis for the Class Component

Tax and Public Service

Examination of the spatial patterns of the tax and public service latent variable scores (Figure 71) shows very low values in the neighborhoods of Kenmore, Summit

Lake, South Akron, University, and Middlebury. This construct captures the accessibility and level of services provided by the municipal government.

170

Figure 69 -- Spatial Patterns of Tax/Public Service Construct

Interestingly, the neighborhoods with low tax and public service latent variable scores all have large concentrations of industrial/commercial land use (Figure 5, p. 21).

The neighborhoods with the highest tax and public service factor scores are Fairlawn

Heights and Northwest Akron located on the west side of the city. Two other notable

171

clusters of high latent variable scores appear on the outskirts of the city in Ellet and

Merriman Valley.

Figure 72 shows the average factor scores for the structural component by neighborhood and neighborhood genesis. Familiar patterns are apparent here. The early expansion group of neighborhoods shows highly negative average factor scores for the tax/public service component. Two of the measurement variables used to capture the tax/public service component were tax on land and tax on building. The early expansion neighborhoods are characterized by small lots with small frontages and poor house quality resulting in low assessed values on homes in these areas. In contrast, the affluent Fairlawn Heights neighborhood in the SES genesis group has the highest average factor score for the tax/public service component. Homes in this neighborhood are prestigious and are built on large lots with large frontages resulting in high taxes on the land and building.

172

Figure 70 -- Average Factor Scores by Neighborhood and Genesis for the Tax/Public Service Component

Environment

The environmental construct captures proximity to pollution as well as other physical aspects of the neighborhood’s landscape. The same spatial pattern as was evident in the tax and public service map – following historical industrial and commercial spokes within the city –are apparent in the environmental construct (Figure

73). Low values appear in the areas with high levels of industrial and commercial land use. The highest environmental latent variable scores occur to the west and northwest of downtown in areas that are primarily residential. Interesting though the high environmental latent variable scores are not divided as clearly along socio-economic lines as other factors. West Akron, a neighborhood with low socio-economic status, and

173

Fairlawn Heights, the most affluent neighborhood in the city, have nearly identical environmental factor scores.

Figure 71-- Spatial Patterns of Environmental Construct

Figure 74 shows the average factor scores for the environmental component by neighborhood and neighborhood genesis. The presence of the environmental construct was captured with captured using the manifest variables of percentage of homes vacant

174

(negative loading), proximity to pollution and lot size. Three genesis groups show interesting patterns in average factor scores for the environmental component. First, the affluent neighborhoods in the SES group demonstrate high average factor scores most likely due to low vacancy rates and high proximity from pollution sites located near the center of the city. Second, both neighborhoods in the large retail/service group show highly negative average factor scores probably dues to relatively small lot sizes and close proximity to a “spoke” of manufacturing which extends from downtown, through the University and Chapel Hill neighborhoods. This spoke of manufacturing land use is a source for many of the pollution sites within the city. The third group showing an interesting pattern of average factor scores is the early expansion neighborhoods. Here all of the average factor scores are highly negative due most likely to high vacancy rates in these neighborhoods.

175

Figure 72 -- Average Factor Scores by Neighborhood and Genesis for the Environmental Component

Proximity

The proximity construct captures the accessibility and location of the neighborhood. Four proximity measures were used to operationalize this construct – proximity to; retail, openspace, the city limits and downtown. Much of the opensapce in Akron is located away from the downtown where land costs have historically been high. Also, there are high concentrations of retail located near or just on the other side of the city limits. As a result areas immediately surrounding downtown have very low values for the proximity construct (Figure 75).

176

Figure 73-- Spatial Patterns of Proximity Construct

Figure 76 shows the average factor scores for the proximity component by neighborhood and neighborhood genesis. The presence of the proximity construct was captured by measuring proximity to retail, open space, the city limits and downtown however, proximity to the city limits loaded most heavily (negatively) on this factor.

177

Hence the patterns produced in Figure 71 are influenced by proximity to either the city limits or downtown rather than by neighborhood genesis.

Figure 74 -- Average Factor Scores by Neighborhood and Genesis for the Proximity Component

Political

The political construct was measured with a single binary variable, registered to vote. No clear spatial patterns is apparent for this construct with the exception of clusters of high values in the neighborhoods of Fairlawn Heights and Northwest Akron and a cluster of low values in the Merriman Valley neighborhood (Figure 77). In all other neighborhoods, the political construct as manifested through voter registration is heterogeneous.

178

Figure 75-- Spatial Patterns of Political Construct

179

Figure 76 -- Average Factor Scores by Neighborhood and Genesis for the Political Component

Social Interactive

The original intent of this component in Galster’s model was to capture “local friend and kin networks, *and the+ type and quality of interpersonal associations”

(Galster, 2001, p. 2112). In this research this component of Galster model was operationalized by measuring a neighborhood’s proximity to sites of neighborhood activism such as block clubs, block watches and community development corporations.

The resulting spatial patterns are very clear (Figure 79).

180

Figure 77-- Spatial Patterns of Social/Interactive Construct

Since proximity to sites of social activism was used to measure the social/interactive component, low factor score symbolized as blue indicate a higher level of social interactivity. Interpreting the map in this way it is clear that the level of social interactivity as manifest in social activism is concentrated in the core of the city. This is logical given that the mission of neighborhood clubs, block watches and community

181

development corporations is to resolve issues of crime and deteriorating housing stock which are more prevalent in the inner city.

Figure 80 shows the average factor scores for the social/interactive component by neighborhood and neighborhood genesis. The social/interactive construct was captured using proximity measures to block clubs, community development corporations and other social services agencies. These agencies tend to be located in the inner city where crime is a large concern. The neighborhoods in the annexation group (Merriman, Kenmore and Ellet), all located on the outskirts all exhibited high average latent variable scores on the social/interactive component.

Figure 78 -- Average Factor Scores by Neighborhood and Genesis for the Social Component

182

Summary

This chapter has outlined the spatial distribution of each of the constructs comprising Galster’s conceptual model and attempted to link or explain the distributions to other aspects of the local geography. Although this analysis was by no means exhaustive, it does show that Galster’s assertion that the degree of presence of neighborhood for each of the constructs does vary across space in Akron, Ohio. The analysis in this chapter focused on the spatial distribution of Galster’s concepts across the existing vernacular neighborhoods in Akron as defined by the City’s Planning

Division.

At this point the first two goals of this dissertation have been addressed; (1) empirical evaluation of Galster’s model (Chapters 7 and 8) and (2) determining the spatial variability of Galster’s constructs (Chapter 9). The next chapter will critically evaluate Galster’s model for neighborhood research as it was operationalized in Akron,

Ohio.

Chapter 10

Post-hoc Assessment of Galster’s Conceptual Model

Introduction

The original motivation for Galster’s production of his conceptual neighborhood model was to advance a conceptualization of neighborhood that would overcome the shortcomings present in many of the existing definitions of neighborhood (Galster,

2001, p. 2112). Galster conceptualizes neighborhood as a complex commodity made up of the following simpler commodities: structural characteristics of the buildings, infrastructural characteristics, demographic and class characteristics of the residents making up the neighborhood, tax/public services provided by the local government, environmental characteristics, proximity characteristics, political characteristics, social/interactive characteristics and finally sentimental characteristics. With the exception of the sentimental commodity, Galster’s model was evaluated using confirmatory factor analysis for the neighborhoods in Akron, Ohio. This brief chapter will provide an evaluation of the utility of Galster’s model as it was operationalized in

Akron, Ohio.

183 184

Positive Aspects of the Operationalization of Galster’s Model

As is customary in multi-factor models, congeneric measurement models were tested to determine if the observed variables were adequate to capture the presence of the latent factors. These measurement models essentially capture the convergent validity, the degree to what a construct explains the variability in each item, of each of the components of Galster’s model. The convergent validity of eight of the Galster constructs was evaluated. The sentimental construct was eliminated from the model because no variables were available to capture it. Although the political construct was included in the model, it was captured using a single variable – voter registration status.

Convergent validity can’t be determined for a single measurement variable.

Congeneric measurement models for each of Galster’s constructs were evaluated separately for all 19 neighborhoods in Akron, Ohio. Empirical model fit measures indicated that all of the tested constructs were present to some degree in

Akron. The poorest performing measurement model was for the infrastructure component. It converged on only five Akron neighborhoods. The social/interactive construct converged in 16 of the 19 neighborhoods demonstrating the best fit of all of the constructs. The lack of fit of some of the constructs in some of the neighborhoods is consistent with Galster’s assertion that “in instances where a certain dimension (social- interactive or sentimental, for example) of the neighborhood bundle is virtually absent at a certain location, ‘neighborhood’ in this dimension can be thought of as being absent there” (Galster, 2001, p. 2113).

185

Examining the fit of the congeneric measurement models by neighborhood showed that with the exception of the South Akron and Kenmore neighborhoods, all neighborhoods converged on at least two constructs. This supports Galster’s assertion that neighborhood is a complex commodity made up of simpler constructs. However, clear reasons why the model fit so poorly in the South Akron and Kenmore neighborhoods are not readily apparent.

Examining the fit of the congeneric measurement models by the typology of neighborhood genesis shows somewhat mixed results. Table 39 shows that the fit of the constructs within each neighborhood genesis type is somewhat consistent. For example, in the early expansion group of neighborhoods the structural component converged (i.e. was present) in three out of the four neighborhoods making up this group. A perfect fit between the convergence of the constructs and the neighborhood genesis typology would not be expected. Although these neighborhoods may have had similar beginnings they arguably have evolved into very different neighborhoods. For example, the Ellet, Kenmore and Merriman Valley neighborhoods were grouped into a single type – annexation. However, Kenmore and Ellet were annexed in 1920s whereas portions of the Merriman Valley were annexed in the 1980s and 1990s.

186

Table 39 -- Fit of Measurement Models by Neighborhood Genesis Number of Neighborhoods where model

converged

Class

Social Proximity

Number of Structural

Demographics Infrastructural

Neighborhoods Environmental Tax/Public Service Tax/Public Genesis in Group Early 4 3 0 3 2 0 2 4 4 Expansion Annexation 3 2 2 1 1 3 2 1 2 Industrial 2 0 0 0 2 1 1 0 1 Neighborhood Neighborhood 3 0 1 2 2 1 1 0 1 Retail

Lrg. Scale 2 2 1 0 2 1 2 0 2 Retail or Service Socio- 5 4 1 1 3 3 3 4 5 economic Status

Galster advances a working definition where neighborhood is defined as “the bundle of spatially based attributes associated with clusters of residences, sometimes in conjunction with other land uses” (Galster, 2001 p. 2112). Implicit in this definition is that there is interaction among the constructs of the model. in order to test this assertion, all nine single-factor congeneric measurement models were combined to form a single nine-factor confirmatory factor analysis model. In this model, correlations among the factors were freed and estimated using LISREL. Table 40 shows that there is

187

wide and significant correlation among the factors. Arguably, as the number of significant factor covariances increases, the neighborhood could be viewed as being more complex (i.e. there are more concepts interacting to produce a sense of neighborhood). The strong presence of significantly correlated factors confirms

Galster’s assertion that various components of his model interact to produce the presence of neighborhood. The Kenmore neighborhood had the highest number of significantly correlated factor pairs. The Wallhaven neighborhood had the fewest number of correlated factor pairs.

A single nine-factor confirmatory factor analysis model was estimated for all parcels in the city to produce factor scores (relative degree of presence of each of

Galster’s constructs) for every residential parcel in the city. The overall model fit for this confirmatory factor model was adequate. When evaluated at the city-level, Galster’s model accounted for a significant portion of the variance in all of the measurement variables. Essentially when the arbitrary neighborhood boundaries were removed from the analysis, Galster’s model fit the data well. Recall that the neighborhood boundaries used to test Galster’s model at the neighborhood scale were drawn by the City of Akron

Planning Department and may not correspond to the “true” neighborhood boundaries as perceived by local residents.

188

Table 40 -- Extent of Significant Correlation among Factors Number of Significantly Correlated Latent Genesis Neighborhood Factor Pairs (x/36) Early Expansion East Akron 18 Middlebury 15 South Akron 17 Summit Lake 16

Annexation Ellet 26 Kenmore 28 Merriman Valley 18

Industrial Goodyear Heights 24 Neighborhood Firestone Park 14

Neighborhood Highland Square 23 Retail North Hill 21 Wallhaven 7

Lrg. Scale Retail Chapel Hill 12 or Service University 20

Socio-economic Fairlawn Heights 16 Status Northwest Akron 30 West Akron 20 Elizabeth Park 17 Lane Wooster 21

Shortcomings of the Operationalization of Galster’s Model

Although there was a reasonable level of convergence of the neighborhoods on

Galster’s constructs, the global model fit indices typically were in the “acceptable fit” range as proposed by Schermelleh-Engle, Moosbrugger and Muller (2002). Rarely did the global fit indices enter the “good fit” range. However, all of the analysis at the

189

neighborhood scale were conducted using the ad-hoc neighborhoods as outlined by the

City of Akron Planning Division. To date there has been no research to verify that these ad-hoc regions correspond to neighborhood boundaries as perceived by local residents.

The moderate to marginal level fit could be due either to a false conceptual model or that Galster’s conceptual model was applied to arbitrary regions.

When testing the measurement model for each of the constructs for each of the neighborhoods separately, some neighborhoods converged on only one or two constructs. It is hard to imagine that such “simplistic” neighborhoods would be recognized by municipal planners. Additional research is required to determine if the measurement variables used to determine the presence of the constructs in Galster’s model are robust and parsimonious.

Summary

Although the analysis of the patterns of significant factor covariances (Chapter 9) and the spatial distribution of the factor scores (Chapter 10) illustrates some interesting patterns for the Akron neighborhoods, overall it is still difficult to discern any broad patterns from the covariance analysis for two reasons. Operationally the large number of neighborhoods in the analysis makes it difficult to discern any spatial and/or numerical similarities across neighborhoods. More importantly however is that conceptually Galster’s model accentuates similarities in neighborhood diversity. Stated another way, Galster’s model compartmentalizes neighborhood into nine constructs. As

190

is evident from the analysis in Chapter 9, these constructs are present to different degrees in different neighborhoods. It is natural to group neighborhoods which load significantly on the same construct. Essentially examining the results of the constructs individually accentuates the difference between neighborhoods (high loading on a factor vs. low loading).

Arguably examining the taxonomy in fit across all of the constructs simultaneously (i.e. determining which neighborhoods converge highly on the same constructs and converge lowly on the same constructs) could provide a higher and more meaningful coalescence of neighborhoods. This extension of Galster’s conceptual mode is presented in the next chapter.

Chapter 11

Results: Extending Galster to Produce An Alternate Regionalization of Akron Neighborhoods

Introduction

The purpose of extending Galster’s conceptualization to address similarities in constructs across multiple neighborhoods is to answer the question “Are there neighborhoods in Akron that are similar?” It is this question that is at the heart of the third research goal of this dissertation, to advance the understanding of the local geography of Akron. Although it would be easy for any local resident to answer this question based on one or even two variables, race or income for example, mentally grouping the neighborhoods on all of the nine constructs in Galster’s neighborhood model would not be possible. However, data classification tools, specifically clustering techniques, do exist that can provide meaningful groupings of neighborhoods.

Method

In order to produce an extension to Galster for Akron, parcels with similar latent variable scores were grouped together. Given the large number of observations in the dataset (N = 64,000) a clustering algorithm was required. Cluster analysis has been used in geography to “divide a region into a smaller number of contiguous subregions”

(Rogerson, 2006, p. 264). The number of clusters was limited to four to allow for easy

191 192

interpretation of the resulting groups and was based on an intuitive understanding of the Akron neighborhoods. Although a variety of clustering algorithms are available, a two-step cluster analysis was used to place each of the observations

(parcels) into one of four groups. The two-step clustering algorithm was selected because it efficiently handles extremely large datasets by first coarsely pre-clustering the data (step-one) and then clustering the sub-clusters (step-two) (SPSS, 2001). Each parcel’s factor score on each of the nine Galster concepts, calculated as described in

Chapter 9, was used as input to the clustering algorithm. The result of the two-step cluster analysis is four groups of observations where, based on all nine input variables, the within-in group variation is minimized and the between group variation is maximized. The two-step clustering technique is aspatial, that is, it places a parcel into a cluster based on its attribute value only without regard for its spatial location. For example, some of the parcels in the Chapel Hill neighborhood were placed in the first cluster, some in the second cluster, etc. It is not expected that neighborhoods are homogenous therefore not all of the parcels within a neighborhood were be classified into the same cluster. Since the goal of this chapter was to group existing neighborhoods into a smaller set of neighborhoods, each of the existing 19 neighborhoods was assigned to one of the four clusters resulting from the cluster analysis. Each neighborhood was assigned to the cluster in which the highest percentage of its parcels fell (Table 41). For example, for the East Akron neighborhood,

71% of its parcels were placed into Cluster #2 by the two-step clustering algorithm. No

193

other cluster has a higher proportion of East Akron’s parcels therefore the second cluster is most representative of East Akron.

Table 41 -- -- Percentage of Neighborhood Parcels Falling into each Cluster. Cluster Neighborhood 1 2 3 4 Urban Urban core Urban core Outlier periphery (moderate (lower SES) cluster (higher SES) SES) Chapel Hill 61% 16% 2% 21% East Akron 9% 71% 16% 4% Elizabeth Park 17% 44% 33% 6% Ellet 80% 5% 3% 12% Fairlawn Heights 61% 1% 1% 36% Firestone Park 71% 22% 3% 4% Goodyear Heights 68% 23% 4% 4% Highland Square 32% 42% 17% 9% Kenmore 61% 25% 12% 3% Lane Wooster 0% 44% 50% 6% Merriman Valley 35% 15% 17% 33% Middlebury 0% 71% 28% 1% North Hill 37% 43% 11% 9% Northwest Akron 54% 8% 1% 36% South Akron 9% 59% 29% 3% Summit Lake 0% 39% 55% 6% University 1% 70% 27% 2% Wallhaven 68% 13% 1% 18% West Akron 7% 64% 23% 6% Highlighted cell indicates the final cluster to which the neighborhood was assigned.

Interestingly, only three clusters resulted from the clustering technique. The fourth cluster did not represent the majority of parcels in any of the neighborhoods. In general, this fourth cluster contains relatively low proportions of the parcels for any single neighborhood and can be viewed as the outlier cluster capturing parcels which

194

are outside the typical range of attribute values for a neighborhood. Table 42 highlights the groups of neighborhood resulting from the cluster analysis.

Table 42 -- -- Typology and Genesis of Akron Neighborhoods Urban Periphery Urban Core Urban Core (higher SES) (moderate SES) (lower SES) Chapel Hill [LRG] East Akron [EE] Lane Wooster [SES] Ellet [ANX] Elizabeth Park [SES] Summit Lake [EE] Fairlawn Heights [SES] Highland Square [RET] Firestone Park [IND] Middlebury [EE] Goodyear Heights [IND] North Hill [RET] Kenmore [ANX] South Akron [EE] Merriman Valley [ANX] University [LRG] NW Akron [SES] West Akron [SES] Wallhaven [RET] Neighborhood Genesis Typology: EE – early expansion, ANX – annexation, IND – industrial neighborhoods, RET- neighborhood retail, LRG – large retail/service, SES – socio-economic status

It has become common place in urban/market segmentation systems to assign descriptive names to these typologies although it is often difficult to capture the essence of a complex group of neighborhoods with a single descriptive name. Even though, a descriptive title was assigned to each of the groupings of neighborhoods resulting from the cluster analysis. These titles were based, in part, on the summary of socio-economic variables presented in Table 43. The first grouping of neighborhoods can be described as the urban periphery with a higher socio-economic status (SES) relative to the city. Figure 81 shows that the all of the neighborhoods in group 1 are

195

located on the edge of the city however they are not completely contiguous. The North

Hill neighborhood forms a portion of the city limits but was not classified in the stable urban periphery group. This group of neighborhoods contains all of the Akron neighborhoods whose genesis was annexation [ANX] or industrial neighborhood development [IND]. It also contains the both of the neighborhoods whose genesis was a high socio-economic status [SES]. The fact that neighborhoods with a common genesis are falling into the same typology built from similarities in latent variable scores implies factors influencing the genesis of each neighborhood might still have influence in the contemporary neighborhood. Returning briefly to the measurement level, Table 43 shows that of the three groups of neighborhoods, group #1 has the highest levels of selected socio-economic variables indicating that they are the economically stable and viable foundation of the city’s neighborhoods.

The second group of neighborhoods in the typology – urban core with moderate

SES status – forms the core of the city surrounding the downtown. These neighborhoods have a significantly lower median household income and median housing when compared to the urban periphery neighborhoods. The moderate SES urban core contains the majority of the early expansion neighborhoods. The final group in the typology – lower SES urban core – contains two neighborhoods which are located adjacent to downtown but are physically separated from it by a seldom used extension of a state highway the Route 59 Innerbelt. Arguably this has contributed to the extremely low socio-economic status of this group of neighborhoods. The lower SES

196

urban core has the lowest median household income and the highest percentage of renters in the city.

It is important to note that the groups of neighborhoods is based on latent variable scores – the relative standing of each parcel on the latent variables. The socio- economic variables in Table 43 are presented only to assist in assigning meaningful names to the neighborhood groupings.

Table 43 -- Selected Socio-Economic Variables for Neighborhood Groups. Source: US Census 2000 Median % Residents Neighborhood Percent Average Median House Household with graduate Type Renters Rent Value Income degree

Urban Periphery $42,675 28.5% $461 $104,253 19.6% (higher SES)

Urban Core $27,956 49.6% $412 $61,326 9.6% (moderate SES)

Urban Core $21,109 53.8% $336 $48,203 1.8% (lower SES)

Summary

Using the latent variable scores generated by empirically evaluating Galster’s conceptual model of neighborhood, this brief chapter provided an alternative regionalization for the vernacular neighborhoods of Akron, Ohio. A clustering technique was used to group existing neighborhoods into a smaller set of neighborhoods. The clustering of neighborhoods was based on the latent variable scores for all nine

197

constructs in the Galster model. These latent variable scores capture the full range of the covariance behavior of the underlying measurement variables, account for error in measurement and interaction among latent factor. An alternative regionalization based on latent variable scores arguably captures the complex nature of neighborhood. The resulting regionalization depicted in Figure 81 is intuitive and logically links back to the empirical plane by examining traditional socio-economic indicators such as median household income and median house value (Table 43).

198

Urban Periphery (higher SES) Urban Core (moderate SES) Urban Core (lower SES) Excluded Areas

Figure 79 -- Spatial Distribution of the Typology of Akron Neighborhoods

Chapter 12

Discussion and Future Research

Galster (2001, p. 2113) describes the 20th century Holy Grail of neighborhood analysis as “a means of unambiguously, meaningfully bounding urban neighborhoods.”

While he was clear to state that his conceptual neighborhood model was not the Holy

Grail, this dissertation has shown that his model does inform the understanding of neighborhood as a complex commodity which varies both spatially and conceptually.

This research began with three goals; to empirically evaluate Galster’s conceptual neighborhood model, determine the spatial distribution of the components of the model and to enhance the understanding of the vernacular neighborhoods of Akron,

Ohio. Each of these goals will be discussed in turn. Finally, suggestions for future research will be explored.

Empirical Evaluation of Galster’s Model

The conceptual model of neighborhood as proposed by Galster contains ten components representing different characteristics of neighborhood; structural, infrastructural, demographic, class status, tax/public service, environmental, proximity, political, social-interactive and sentimental. Nine of these components were operationalized with measured variables with variable selection matching as closely as

199

200

possible the original suggestions of Galster. The tenth concept of neighborhood, sentimental characteristics, was excluded from the analysis since it is tied to the perceptions of individual residents and no broad data sources were available to capture this construct. Factor analytic congeneric measurement models were constructed to evaluate each of the nine remaining components of the model. These measurement models were evaluated for each of the 19 vernacular neighborhoods of the city.

For all nine constructs, the measurement models converged and fit the data reasonably well in at least one-third of the neighborhoods. The adequate fit of the measurement models suggests that Galster’s concepts are present in many of the Akron neighborhoods and that their presence was captured with the manifest variables. The fact that the congeneric measurement models did not fit in all of the neighborhoods and that the degree of fit varies from weak to very strong supports a key assertion of

Galster’s model that “in instances where a certain dimension *construct+ (social- interactive or sentimental, for example) of the neighbourhood bundle is virtually absent at a certain location, ‘neighbourhood’ in this dimension can be thought of being absent there” (Galster, 2001 p. 2113).

201

confirmatory factor analytic (CFA) model where the covariances among the nine factors were freely estimated. CFA models were evaluated for each of the 19 neighborhoods.

All neighborhoods had large numbers of significantly correlated pairs of constructs (e.g. demographic-class, structure-infrastructure) supporting Galster’s assertion.

Spatial Distribution of the Conceptual Neighborhood Model

Galster states that “the unifying feature of these attributes [constructs] constituting the bundle called neighbourhood is that they are spatially based. The characteristics of any attribute can be observed and measured only after a particular location has been specified” (Galster, 2001 p. 2113). Further, he states that “the geographical scale across which an attribute [construct] varies often is wildly dissimilar among attributes.”

In order to explore the spatial variability of Galster’s model, latent variable scores were estimated for each observation (parcel) for each of the constructs. These latent variable scores were mapped and their spatial distributions examined. In accord with Galster’s assertions of spatial non-stationarity, the “degree of presence” of the constructs varied both within neighborhoods and across neighborhoods. In general he spatial patterns were meaningful and related well to the underlying data.

202

Understanding the Vernacular Neighborhoods of Akron, Ohio

Although the primary goal of this research was to empirically evaluate Galster’s conceptual neighborhood model an important subordinate goal was to add to the understanding of the local geography of Akron, Ohio. To date, the delineation of vernacular neighborhoods has been arbitrary with no empirical evaluation of the delineations. In order to both inform the local geography of Akron and to evaluate the utility and meaningfulness of Galster’s model, neighborhoods were grouped into a typology. Three groupings were generated by cluster analysis of the latent variable scores and descriptive names were assigned; stable urban periphery, struggling urban core, and distressed urban core. Neighborhoods have long been categorized by various attributes (e.g. social area analysis) but a common complaint of these groupings is that they are simplistic and don’t capture the complexity of neighborhood. The typology generated in this dissertation is based on commonalities in the latent variable scores of nine complex factors.

The typology demonstrates empirically what arguably almost any long-time resident of the city could confirm, that:

the highly segregated (black) neighborhoods near downtown have deteriorating

housing stock, low socio-economic status, and low educational attainment

(urban core with lower SES)

203

the somewhat racially mixed neighborhoods near downtown have older housing

stock, moderate socio-economic status but remain engaged in local governance

(urban core with moderate SES)

the somewhat segregated (white) neighborhoods at the city’s edges have good

housing stock, and enjoy the highest socio-economic status and educational

attainment in the city (urban periphery with higher SES).

Future Research Considerations

The three primary goals of this dissertation were discussed in the previous section. A common adage is that “research generates more questions than answers”, as was the case for the evaluation of Galster’s conceptual model in Akron, Ohio. Several brief suggestions for future research are discussed.

Measurement Variable Strength and Parsimony

The measurement variables selected to capture the latent constructs were chosen to match, as closely as possible, the suggested variables in Galster’s original presentation of the model. Finding data which are spatially extensive (i.e. available for every parcel in the city) and that meet the assumptions of SEM estimation methods is challenging. The measurement models generated in this research had only marginally acceptable levels of internal reliability. Additional research is necessary to find the most robust and parsimonious set of measurement variables for each of the constructs.

204

Model Parsimony

Inherent in Galster’s model is that not all constructs are present to the same degree in each neighborhood. In fact, some constructs may be virtually missing in some neighborhoods. However, multi-group means and covariance structural analysis has a basic assumption of configural invariance (i.e. the same model must be used in all groups of the analysis). For this research, the configural invariance requirement was met by fitting the same model across all of the neighborhoods when in fact that may not have been the best model for some neighborhoods. Additional confirmatory factor analysis is necessary to find the most parsimonious models for each of the 19 neighborhoods in the study area. Alternatively, alternate regionalizations could be constructed where all of the constructs in Galster’s model would be present thus meeting the requirement of configural invariance.

Galster’s Model at Varying Spatial Scales

Galster aligns his conceptual neighborhood model with Suttles’ (1972) spatial view of neighborhood where neighborhood can be identified at four distinct spatial scales. The smallest scale is the “block face” extending over one side of standard city block. The second level is the “defended neighborhood” – the smallest area possessing a corporate identity as defined by mutual opposition or conflict to another area. The third level is the “community of limited liability” often-representing areas demarcated by local governments. Finally, the “expanded community of limited liability” represents

205

the entire urban area. Although a daunting task, additional research is necessary to test the utility of Galster’s model at each of these spatial scales.

Spatial Bounds of Vernacular Neighborhoods

Definitions of vernacular neighborhoods in Akron, Ohio were taken from the City

Planning and Development maps. Some city residents have argued that the spatial bounds of these neighborhood are arbitrary at best or at worst a classic example of gerrymandering. Additional research into the true spatial extent of the neighborhoods and reevaluating the models with the new neighborhoods would shed light on the meaningfulness of the city’s bounding of the neighborhoods.

Neighborhood Delineation (Region Building)

As an alternative to using Galster’s conceptual model to verify the presence of a priori neighborhoods, the latent variable scores, available for each parcel in the study area, could be used as input to region building techniques. Additional research into the utility and robustness of spatially constrained clustering algorithms on latent variable scores would be of particular interest to geographers and urban planners.

206

APPENDIX

Figure 80 -- Squared Multiple Correlations (R2) for the East Akron Neighborhood

207

Figure 81-- Squared Multiple Correlations (R2) for Middlebury Neighborhood

Figure 82-- Squared Multiple Correlations (R2) for South Akron Neighborhood

208

Figure 83-- Squared Multiple Correlations (R2) for Summit Lake Neighborhood

Figure 84-- Squared Multiple Correlations (R2) for Ellet Neighborhood

209

Figure 85-- Squared Multiple Correlations (R2) for Kenmore Neighborhood

Figure 86-- Squared Multiple Correlations (R2) for Merriman Valley Neighborhood

210

Figure 87-- Squared Multiple Correlations (R2) for Goodyear Heights Neighborhood

Figure 88-- Squared Multiple Correlations (R2) for Firestone Park Neighborhood

211

Figure 89-- Squared Multiple Correlations (R2) for Highland Square Neighborhood

Figure 90-- Squared Multiple Correlations (R2) for North Hill Neighborhood

212

Figure 91-- Squared Multiple Correlations (R2) for Wallhaven Neighborhood

Figure 92 -- Squared Multiple Correlations (R2) for Chapel Hill Neighborhood

213

Figure 93-- Squared Multiple Correlations (R2) for University Neighborhood

214

Figure 94-- Squared Multiple Correlations (R2) for Fairlawn Heights Neighborhood

Figure 95-- Squared Multiple Correlations (R2) for Northwest Akron Neighborhood

215

Figure 96-- Squared Multiple Correlations (R2) for West Akron Neighborhood

Figure 97-- Squared Multiple Correlations (R2) for Lane Wooster Neighborhood

216

Figure 98 -- Squared Multiple Correlations (R2) for Elizabeth Park Neighborhood

BIBLIOGRAPHY

Abler, R., Adams, J. S., & Gould, P. R. (1971). Spatial organization; the geographer's view

of the world. Englewood Cliffs, N.J.: Prentice-Hall.

Abu-Lughod, J. L. (1991). Changing cities : Urban sociology. New York, NY: Harper

Collins.

Acito, F., & Anderson, R. D. (1986). A Simulation Study of Factor Score Indeterminacy.

Journal of Marketing Research, 23, 111-118.

Akron Beacon Journal (2004). Discover Akron and Northeastern Ohio. (2004, May 16,

2004). Akron Beacon Journal Discover Magazine.

Akron Public Schools. (2007). School Profiles. Retrieved June 15, 2007, from

http://www.akronschools.com/

Andreassen, T. W., Lorentzen, B., & Olsson, U. H. (2006). The Impact of Non-Normality

and Estimation Methods in SEM on Satisfaction Research in Marketing. Quality &

Quantity, 40(1), 38-59.

Austin, M. E. (1972). Land resource regions and major land resource areas of the United

States. Rev. ed. Agricultural Handbook 296. Washington, D.C.: U.S. Department

of Agriculture, Soil Conservation Service.

Beauregard, R. (2007). More Than Sector Theory: Homer Hoyt's Contributions to

Planning Knowledge. Journal of Planning History, 6(3), 248-271.

217 218

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological

Bulletin, 107, 238-246.

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the

analysis of covariance structures. Psychological Bulletin, 88, 588-606.

Berry, B. J. L. (1966). Essays on commodity flows and the spatial structure of the Indian

economy. Chicago: Dept. of Geography, University of Chicago.

Berry, B.J., & Garrison, W.L. (1958) The Functional Bases of the Central Place Hierarchy.

Economic Geography, 34, 145-154.

Blaut, J.M. (1961). Space and Process. The Professional Geographer, 13, 1-7.

Blowers, A. (1973). The neighbourhood: exploration of a concept, Open Univ. Urban

Dev. Unit 7, 49-90.

Bollen, K. A. (1989). Structural equations with latent variables. New York : Wiley.

Boros, A. (2005). The Classification of the Urban Environment into Ecological Zones for

the Examination of Intensive Supervision Probation Success Rates: An Assessment

of Factor Analytic Models. Kent State University, Kent, Ohio.

Browne, M.W. (1984). Asymptotically distribution-free methods for the analysis of

covariance structures. British Journal of Mathematical and Statistical Psychology,

37, 1-21.

Burgess, W. (1925). The Growth of the City: An Introduction to a Research Project in

The City. Park, R. E., Burgess, E. W., McKenzie, R. D., & Wirth, L. (1925). Chicago,

Ill.: The University of Chicago Press.

219

Byard, K., & Schleis, P. (2003, August 10, 2003). Despite Challenges, Area Serves as

Symbol of Rebirth – Lane Wooster Enjoys Neighborhood Revival. Akron Beacon

Journal,

Cadwallader, M. T. (1985). Analytical urban geography : Spatial patterns and theories.

Englewood Cliffs, N.J.: Prentice-Hall.

Census, U. S. (2000, July 15, 2007). Census of Population and Housing, Summary File 3.

from http://www.census.gov

Chaskin, R.J. (1997). Perspectives on neighborhood and community: A review of the

literature. Social Service Review (1997), pp. 521–547.

City of Akron. (2007, July 15, 2007). Akron Neighborhood Profiles. Retrieved June 10,

2007 from http://www.ci.akron.oh.us/planning/cp/neighborhoods.htm

Cohen, S. B., & Lewis, G. K. (1967). Form and Function in the Geography of Retailing.

Economic Geography, 43, 1-42.

Congress of the New Urbanism. (2007). Charter of the New Urbanism. Retrieved

March 1, 2007, from http://www.cnu.org/charter

Dillon, W. R., & Goldstein, M. (1984). Multivariate Analysis: Methods and Applications.

New York: John Wiley and Sons.

Dunteman, G. H. (1989). Principal components analysis. Newbury Park: Sage

Publications.

Eichenbaum, J., & Gale, S. (1971). Form, Function, and Process: A Methodological

Inquiry. Economic Geography, 47, 525-544.

220

Fotheringham, S. A, Brundson, C. & Charlton, M. (2002). Geographically Weighted

Regression - the analysis of spatially varying relationship. West Sussex, England:

John Wiley & Sons Ltd.

Finley, C.F. (1986). A History of Firestone Park. Akron, Ohio : Summit County Historical

Society.

Ford, L. R. (1972). Metropolitan evolution, urban images, and the concentric zone

model. California Geographer, 13, 59-62.

Forrest, R., & Kearns, A. (2004). Who Cares About Neighbourhood? Paper presented at

the Community, Neighbourhood, Responsibility. from

http://www.neighbourhoodcentre.org.uk

Frazier, K. D. (1994). Model industrial subdivisions : Goodyear Heights and Firestone

Park and the town planning movement in Akron, Ohio, 1910-1920.

Galster, G. (1986). What is neighborhood? An externality-space approach. International

Journal of Urban and Regional Research, 10, 243-261.

Galster, G. (2001). On the Nature of Neighbourhood. Urban Studies, 38(12), 2111-2124.

Garson, G. D. (2007). "Structural Equation Modeling" from Statnotes: Topics in

Multivariate Analysis. Retrieved June 10, 2007, from

http://www2.chass.ncsu.edu/garson/pa765/statnote.htm

221

Gephart, M. A. (1997). Neighborhoods and communities as contexts for development. In

J. B. Brooks-Gunn, G. J. Duncan, & J. L. Aber (Eds.), Neighborhood poverty: Vol. 1.

Context and consequences for children (pp. 1-43). New York: Russell Sage

Foundation.

Gould, P.R. (1967). On the Geographical Interpretation of Eigenvalues. Transactions of

the Institute of British Geographers, 42, 53-86.

Gorsuch, R. L. (1983). Factor analysis. Hillsdale, N.J.: L. Erlbaum Associates.

Graham, J. M. (2006). Congeneric and (Essentially) Tau-Equivalent Estimates of Score

Reliability: What They Are and How to Use Them. Educational and Psychological

Measurement, 66, 930-944.

Greene, R. P., & Pick, J.B. (2006). Exploring the Urban Community A GIS Approach. Upper

Saddle River, NJ: Pearson Prentice Hall.

Grice, J. W. (2001). Computing and evaluating factor scores. Psychological Methods, 6,

430-450.

Grismer, K. H. (1952). Akron and Summit County. Akron, Ohio: Summit County

Historical Society.

Haggett, P. (1972). Geography: A Modern Synthesis. New York: Harper & Row.

Haggett, P., Cliff, A., & Frey, A.. (1977). Locational Analysis in Human Geography, 2nd ed.

London: Edward Arnold.

222

Harris, C.D., & Ullman, E.L. (1945). The nature of cities. Annals of the American

Academy of Political and Social Science, 242: 7-17.

Harvey, D. (1969). Explanation in Geography. London: Edward Arnold

Harvey, M. E. (1972). The Identification of Development Regions in Developing

Countries. Economic Geography , 229-243.

Hoyle, R. H. (2000). Confirmatory Factor Analysis. In H. E. A. Tinsley & S. D. Brown (Eds.),

Handbook of Multivariate Statistics and Mathematical Modeling (pp. 465-497).

San Diego, CA: Academic Press.

Hoyt, H. (1939). The Structure and Growth of Residential Neighborhoods in American

Cities. Federal Housing Authority, Washington, D.C.: Government Printing Office.

Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer-Verlag.

, K. (2000). Latent Variables and Their Uses [Electronic Version], from

http://www.ssicentral.com

Jöreskog, K. G. (2005). Structural equation modeling with ordinal variables using LISREL.

Retrieved February10, 2005, from

http://www.ssicentral.com/lisrel/techdocs/ordinal.pdf

, K., & , D. (1993). LISREL 8: Structural Equation Modeling with the

SIMPLIS Command Language. Chicago.

, K., & , D. (2001). LISREL 8: User's Reference Guide. Lincolnwood, IL:

Scientific Software International.

223

, K., & , G. (2006). LISREL for Windows. Lincolnwood, Illinois: Scientific

Software International.

, K.G., , D., & Wallentin, F. Y. (2006). Latent Variable Scores and

Observational Residuals [Electronic Version], from

http://www.ssicentral.com/lisrel/techdocs/obsres.pdf

Kearns, A., & M. Parkinson. (2001). The Significance of Neighborhood. Urban Studies,

38(12), 2103-2110.

Keating, D. W., & Krumholz, N. (2000). Neighborhood Planning. Journal of Planning

Education and Research , 111-114.

Kent, R. B., & Butler, K. (2007). Akron the Rubber City Grows Up. In A. Keifer (Ed.) Ohio

Geography. Kent State University Press (in press).

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York :

Guilford Press.

Knox, P., & Pinch, S. (2000). Urban Social Geography An Introduction. New York: Prentice

Hall.

Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political

Economy, 74, 132-157.

Margenau, H. (1961). Open Vistas: Philosophical Perspectives on Modern Science. Yale

University Press.

224

Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness of fit indexes in

confirmatory factor analysis: The effect of sample size. Psychological Bulletin,

103, 391-410.

Marsh, H.W. & Hau, K.T. (1999). Confirmatory factor analysis: strategies for small

sample sizes. In: R. Hoyle, Editor, Statistical strategies for small sample research,

Sage, Thousand Oaks, CA ), pp. 251–284.

Marsh, H. W., Hau, K.-T., Balla, J. R., & Grayson, D. (1989). Is more ever too much? The

number of indicators per factor in confirmatory factor analysis. Multivariate

Behavioral Research, 33, 181-220.

Maruyama, G. (1998). Basics of structural equation modeling. Thousand Oaks, Calif.:

Sage Publications.

Martin, D. G. (2003). Enacting Neighborhood. Urban Geography, 24(5), 361-385.

Meade, M., & Earickson, R. (2000). Medical Geography (2nd Edition. ed.), Guildford

Press, New York (2000).

Newcomb, M.D., & Bentler, P.M. (1988). Consequences of adolescent drug use: Impact

on the lives of young adults, Sage, Beverly Hills, CA (1988).

Noble, A. G. (1975). Ohio, an American Heartland. Columbus, Ohio. Division of

Geological Survey.

225

Park, R., Burgess, E.W. & McKenzie, R.D. (1925). The City. Chicago: University of Chicago

Press, 1925.

Rogerson, P. (2006). Statistical Methods for Geography : A student's Guide. Thousand

Oaks, CA : Sage Publications, 2006.Satorra, A., & Bentler, P.M. (1994).

Corrections to test statistics and standard errors in covariance structure analysis.

In A. von Eye & C.C. Clogg (Eds.),Latent variables analysis: Applications for

developmental research (pp. 399–419). Thousand Oaks, CA: Sage.

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for

moment structure analysis. Psychometrika, 66, 507-514.

Sawicki, D. S., & Flynn., P. (1996). Neighborhood Indicators: A Review of the Literature

and an Assessment of Conceptual and Methodological Issues. Journal of the

American Planning Association, 62(2), 165-183.

Schermelleh-Engel, K., Moosbrugger, H., & Muller, H. (2003). Evaluating the fit of

structural equation models: Tests of significance and descriptive good-of-fit

measures. Methods of Psychological Research, 8, 27-74.

Schleis, P., & Byard, K. (2005, August 1, 2005). Down in the Valley, Will Things Look Up?

Akron Beacon Journal,

Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data.

Paper presented at the 1968 ACM National Conference.

226

Soja, E. (1980). The socio-spatial dialectic. Annals of the Association of American

Geographers, 70, 207-225.

SPSS (2001). Technical Report TSCWP-0101: The SPSS TwoStep Cluster Component: A

scalable component enabling more efficient customer segmentation. (S. Inc. o.

Document Number)

Steiger, J. H. (1998). A note on multiple sample extensions of the RMSEA fit index.

Structural Equation Modeling, 5, 411-419.

Summit County Historical Society. (1925). A Centennial history of Akron, 1825-1925.

Akron Ohio: The Summit County Historical Society.

Suttles, G. D. (1972). The Social Construction of Communities. Chicago: The University of

Chicago Press.

Symanski, R. & Newman, J.L. (1973). Formal, Functional, and Nodal Regions: Three

Fallacies. The Professional Geographer, 25, 350-352.

Tepper, K., R.H. Hoyle. (1996). Latent Variable Models of Need for Uniqueness.

Multivariate Behavioral Research, 31, 467-494.

Tucker L, C. Lewis. (1973). A reliability coefficient for maximum likelihood factor

analysis. Psychometrika, 38, 1-10.

U.S. Census Bureau (2007, January 1). About Metropolitan and Micropolitan Statistical

Areas. Retrieved January 1, 2007, from

http://www.census.gov/population/www/estimates/aboutmetro.html

227

USDA (2007). USDA Plant Hardiness Zone Map. Washington: United States Department

of Agriculture.

228

van Dujin, P. (1965). The Interaction of Theories and Experiments in Science in

Information and Prediction in Science, eds. S. Dockx and P. Bernays. New York:

Academic Press.