Parcel Level Land-Use Analysis Applying Lucis Plus Model: Scenario Planning for 2040 Hillsborough County, Florida

PARCEL LEVEL LAND-USE ANALYSIS APPLYING LUCIS PLUS MODEL: SCENARIO PLANNING FOR 2040 HILLSBOROUGH COUNTY, FLORIDA

KE LIN

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF URBAN AND REGIONAL PLANNING

UNIVERSITY OF FLORIDA

2018

To my parents and all my friends who support me

ACKNOWLEDGMENTS

I thank my parents Shae and Jianyong who support me study abroad. And I thank my friends Leilei Duan, Danxin Wang, Ying Zhou and Yiwei Zhang who helped me go through the hard time when writing this thesis. Last but not least, I thank my committee chair Paul Zwick, the co-chair Stanley Latimer and the member Tim Murtha.

It is a great honor to work with them.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 9

ABSTRACT ...... 10

CHAPTER

1 INTRODUCTION ...... 11

Literature Review ...... 12 Geographic Information System (GIS) ...... 12 GIS-Based Land-Use Analysis ...... 13 Land-Use Model ...... 14 Land-use conflict identification strategy (LUCIS) ...... 15 LUCIS plus ...... 16 Population and Employment ...... 16 2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum ...... 17

2 METHODOLOGY AND PROCESS ...... 20

Research Design ...... 20 LUCIS Plus Model ...... 21 Criteria Evaluation Matrix Construction ...... 22 The Region Projection Determination ...... 23 Population and Employment Density ...... 24 Allocation Sequence Determination ...... 24 Infill Population Allocation Procedure ...... 25 Redevelopment Population Allocation Procedure ...... 25 Greenfield Population Allocation Procedure ...... 27 Employment Allocation Procedure ...... 28

3 ANAYLSIS AND CONCLUSION ...... 38

Parcel-Level LUCIS Plus Scenario ...... 38 Population Allocation ...... 38 Employment Allocation ...... 39 TAZ-Level Preferred Hybrid Scenario ...... 39 Population Allocation ...... 40

Employment Allocation ...... 41 Comparison and Conclusion ...... 41

LIST OF REFERENCES ...... 57

BIOGRAPHICAL SKETCH ...... 58

LIST OF TABLES

Table page

1-1 The LUCIS approach and Odum Comparison Carr&Zwick Smart Land-Use Analysis: The LUCIS Model ...... 19

2-1 BEBR Population Forecast Projections for Hillsborough County ...... 30

2-2 Preferred Hybrid: Population and Employment Forecast Projections ...... 31

2-3 Preferred Hybrid: Employment Totals by Type ...... 32

2-4 Units Per Acre in Hillsborough County ...... 33

2-5 Adapted Redevelopment Propensity Index Criteria ...... 36

3-1 Parcel-Level LUCISplus Population Allocation ...... 46

3-2 Parcel-level land use analysis Employment Allocation and Goal...... 47

3-3 The Employment Allocation of Zone 765—Redevelopment Section ...... 48

LIST OF FIGURES

Figure page

1-1 Land Use Models Evolution ...... 19

2-1 Hillsborough County ...... 30

2-2 Infill Allocation Process ...... 31

2-3 Redevelopment Propensity Index Criteria of MPO ...... 32

2-4 Greenfield Allocation Process...... 35

2-5 Infill and Redevelopment Employment Allocation Procedure ...... 36

2-6 Greenfield Employment Allocation Procedure ...... 37

3-1 Parcel-Level LUCISplus Scenario: Population Growth Proportion of Each Land Use Category ...... 46

3-2 Parcel-Level LUCISplus Scenario: Population Growth Distribution Map ...... 47

3-3 Parcel-Level LUCISplus Scenario: Employment Allocation/Total Employment Ratio ...... 48

3-4 Parcel-Level LUCISplus Scenario: Employment Growth Distribution Map ...... 49

3-5 2040 Hillsborough County Preferred Hybrid Scenario Population Growth Map .. 50

3-6 Transportation Analysis Zone 612 with greatest population growth in Preferred Hybrid scenario from MPO ...... 51

3-7 Preferred Hybrid Scenario: Each Type of Employment Allocation /Total Employment Ratio ...... 52

3-8 2040 Hillsborough County Preferred Hybrid Scenario Employment Growth Distribution Map ...... 53

3-9 Population Growth Distribution Comparison: Parcel-level scenario vs. TAZ- level scenario...... 54

3-10 Employment Growth Distribution Comparison: Parcel-level scenario vs. TAZ- level scenario...... 55

3-11 Employment Growth Comparison: Zone 765 Example Case Map...... 56

LIST OF ABBREVIATIONS

ACS American Community Survey

BEBR Bureau of Business Economics

CEM Criteria Evaluation Matrix

FDOT Florida Department of Transportation

HART Hillsborough Area Regional Transit Plan

MPO Metropolitan Planning Organization

RPI Redevelopment Propensity Index

TAZ Transportation Analysis Zone

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Urban and Regional Planning

PARCEL LEVEL LAND-USE ANALYSIS APPLYING LUCIS PLUS MODEL: SCENARIO PLANNING FOR 2040 HILLSBOROUGH COUNTY, FLORIDA

Ke Lin

December 2018

Chair: Paul Zwick Cochair: Stanley Latimer Major: Urban and Regional Planning

With increasing population coming to the United States, allocating growth population within limited lands, and balancing the growth and consumption are challenges for planners. In the meantime, it is an opportunity to search for a sustainable and smart development future, and to constrain the unhealthy urban sprawl.

Utilizing the Hillsborough County population and employment projection data completed by the Bureau of Business Economics (BEBR) at the University of Florida as the analysis database, this research conducts a parcel-level land use analysis for 2040

Hillsborough County by using Land Use Conflict Identification plus (LUCISplus) model. In addition, this research compares the parcel-level scenario to another TAZ-level hybrid scenario for Hillsborough County completed by the Hillsborough County Metropolitan

Planning Organization (MPO).

CHAPTER 1 INTRODUCTION

Population allocation is a major topic for urban planners, in Florida and throughout the United States. Over the next forty years, the population of the U.S. is projected to increase by approximately 130 million people (Zwick, Patten, & Arafat,

2016), and planners need to allocate those people within limited areas in a sustainable and smart way. In addition, considering that the U.S. experienced a recession from

2008 to 2010, the balance of inefficiencies in the costs associated with space and productivity is important to urban planners. As a result of the great recession, the sprawl development model typical of the U.S. become unpopular, as it increases transportation costs and infrastructure investment (Zwick et al., 2016). Following this trend,

Hillsborough Metropolitan Planning Organization (MPO) and the Hillsborough County

Planning Commission conducted a preferred hybrid scenario based on Transportation

Analysis Zone (TAZ) level.

After learning parcel-level land-use analysis, the question came up of how to conduct a sustainable, high-density land-use analysis on the parcel level through the

LUCISplus model; and of what the differences are between parcel-level and TAZ-level land-use analysis? To answer these questions, this research conducts a parcel-level land-use analysis and compare it to the TAZ-level preferred hybrid scenario completed by Hillsborough MPO. Besides the difference in planning scale—parcel-level or TAZ- level—the research supposed that the local transportation project, participatory planning process, and local employment boost plan within the preferred hybrid analysis would influence the comparing results. The research questions are as follows:

• How can one conduct parcel-level land-use analysis through LUCISplus?

• How are the overall regional population and employment growth distributed in Hillsborough County in the TAZ-level and the parcel-level land-use planning scenarios?

• What are the difference between parcel-level and TAZ-level analyses?

To resolve these questions, I set the following objectives for each question:

Objective 1. Identify the areas suitable for infill, redevelopment, and greenfield plus development by using the LUCIS .

Objective 2. Calculate the population and employment growth in the two scenarios.

Objective 3. Map and visualize the population and employment growth in the two scenarios.

Objective 4. Compare the two scenarios from a spatial distribution perspective and discuss the reasons for their differences.

Literature Review

This research is a GIS-based land-use analysis. It applies LUCISplus approach and a projection database from the Hillsborough 2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum (2014).

Geographic Information System (GIS)

In general, geographic information systems (GIS) provides a way to “view and organize spatial features represented as digital objectives with attributes information”

(Carr & Zwick, 2007). The modern era in GIS has gone through three phases:

i. In the 1950–1970s, GIS research entered a frontier period that can be referred to as the “innovation stage,” with numerous GIS companies established.

ii. The 1980s, or the integration stage, saw the development of general-purpose GIS systems.

iii. The last two decades or so have been the proliferation stage, characterized by the development of user-oriented GIS technology (Malczewski, 2004).

In the 1950s, improvements in computer hardware and theoretical advances in the spatial sciences pushed the development of GIS forward. In the 1980s, computer

hardware kept climbing higher, influencing the development of commercial GIS software. Though major commercial GIS software companies had been established at the end of the 1960s, until the 1980s numerous commercial GIS systems were being developed. For example, the Environmental Systems Research Institute (ESRI) and the

Intergraph Corporation were both founded in 1969, they released ARC/INFO from ESRI, the first GIS to take advantage of new super-mini computer hardware, in the early

1980s. Also starting in the 1980s, GIS utilization has come to be widespread in municipal planning and academic departments, which has allowed the development of low-cost GIS applications (Malczewski, 2004). Accompanied by evolving GIS technology, the planning process has become more complex but more integrated as well.

GIS-Based Land-Use Analysis

In general, land-use planning is meant to coordinate current and future societal needs while minimizing conflicts (Matternicht, 2017). Since the 1960s, GIS-based land- use analysis techniques have become increasingly integral components of urban, regional, and environmental planning activities (Malczewski, 2004).

In detail, GIS-based land-use analysis involves, first, a process of converting data into information “that adds extra values to the original data” (Malczewski, 2004); second, interpreting and analyzing the original data to produce new information that is useful to those involved the planning process. In the conversion process, the information output is determined by the need and nature of the planning problems

(Malczewski, 2004).

Throughout the process, the information that is deployed can be divided into

“soft” and “hard” information, sometimes called “subjective” and “objective.” Hard

information is usually extracted from reported facts, quantitative estimates, and systematic opinion surveys—for example, census data, remote sensing data, and meteorological surveys—which cannot be optionally altered or deleted. Soft information represents the opinions, preferences, priorities, and judgments of particular interest groups and decision makers, and is based on intuition, ad hoc surveys, questionnaires, comments, and similar sources. This type of information is used in the planning process out of social and political consideration, and also enters into the decision calculus as weighting factors (Malczewski, 2004).

The GIS-based land-use analysis combines hard and soft information into the planning process, and finds the right balance between the two by using a technical spatial analysis tool. Accordingly, information systems for planning, whether in general or for land-use analysis particularly, can be constructed with at least two interrelated perspectives: (i) technical perspectives on GIS, and (ii) sociopolitical, participatory GIS perspectives (Malczewski, 2004).

Land-Use Model

In general, a land-use model is a simplified quantitative method to predict and explain future land-use change, based on economic theories and social behaviors. The first land-use models were introduced around the 1960s and were aggregate models of spatial interaction and gravity models that mimic Issac Newton’s gravitational interaction

(Golias, Mishra, & Psarros, 2016).

The model of Metropolis developed by Lowry in 1964 is the first operational land- use model that is a tool for making predictions about growth and change in the distribution and intensity of land-use activities. Around the 1980s, a new approach was introduced suggesting the development of econometric and discrete-choice models that

were based on utility theory (Golias et al., 2016). Since the late 1980s, more advanced models were gradually developed. These were mainly micro-simulation disaggregate models, including agent and rule-based systems and cellular automata, which refers to the grid cells. Figure 1-1 provides an overview of the evolution of land-use models.

In the evolving process of land-use analysis, LUCIS turns out to be similar to a combination of numerous models. It shares and has integrated other professions. And it combines the characteristics of agent- and rule-based systems, predicting the growth and change in the distribution of certain activities.

Land-use conflict identification strategy (LUCIS)

Derived from the work of Eugene P. Odum’s “The Strategy of Ecosystem

Development,” the land-use conflict identification strategy (LUCIS) classifies three categories for land-use analysis (Zwick, Patten, & Arafat, 2016).

In LUCIS, the Agriculture category serves the “productive” category. Odum introduced it as a succession continually retarded by human controls to maintain a high level of productivity (as cited in Carr & Zwick, 2007, p.10). The Conservation category includes land that is publicly owned and has conservation purposes or is privately owned but whose future use is constrained by easement or deed constriction. The

Urban category is derived from Odum’s urban/industrial definition, which supports high- density human activity (Carr & Zwick, 2007). Table 1-1 provides an overview of two kinds of land use classification.

The five-step LUCIS process for identifying and developing a better visualization and understanding of land-use conflicts is as follows:

i. Define goals and objectives. ii. Complete data inventory and preparation. iii. Define and map land-use suitability.

iv. Integrate community values to determine land-use preferences. v. Identify potential land-use conflicts. (Carr & Zwick, 2007)

LUCIS plus

Going beyond the LUCIS process, which is meant to identify conflicts between stakeholders, LUCISplus is a land-use planning scenario approach that allows users to indicate custom policy options outside the suitability models, to allocate population and employment and to calculate the market value produced by land-use changes. This is achieved through a user-generated table called a criteria evaluation matrix (CEM). In the LUCISplus process, infill, redevelopment, and greenfield CEMs are created with their

“conflict/opportunity, individual land use suitability, and other data to make land use decisions and to spatially allocate residential population and regional and local employment” (Zwick et al., 2016, p. 89).

Before running LUCISplus, it is necessary to understand infill, redevelopment, and greenfield. Infill lands consist of currently vacant urban lands. Previously developed lands are counted as redevelopment lands. And greenfield land is undeveloped land that was previously open space or agricultural land outside the urban service area and city limits (Zwick et al., 2016).

Population and Employment

The term “population” biological and sociological characteristics. In this research, the term is meant sociologically, referring to a collection of humans from cities, towns, regions, countries, or the world. Usually, a government counts its people through censuses, which involves collecting, analyzing, compiling, and publishing data

(Britannica, 2010). The American Community Survey (ACS) publishes the premier source for detailed population and housing information about the United States, to help

officials, community leaders, and businesses understand the changes taking place in their communities.

The University of Florida’s Bureau of Economy and Business Statistics (BEBR) uses multiple methods and averages the results to develop county and state population projections, and then provides annual high, medium, and low population projections.

Since MPO’s TAZ-level land-use analysis uses the medium population projection, which is neither the most conservative nor the most radical choice, this research uses the medium data as well to control the variables for further comparison.

Employment is an essential part of census information too. Although it is not fully determined by the local economy, it can be seen as an index for re-evaluating changes in an area. Integrating employment projections into land-use analysis would help planners understand potential opportunities in a region and make better decisions on land-use policy (Zwick et al., 2016).

2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum

In 2014, the Hillsborough MPO and Hillsborough County Planning Commission completed the 2040 Socioeconomic Data Forecasting and Scenario Planning Technical

Memorandum. This report developed a forecast of the geographic distribution of the county’s population and employment from 2010 to 2040. These socioeconomic data were prepared for further updating the federally-mandated Long Range Transportation

Plan (LRTP).

Using the 2010 population and employment data as a basis, MPO, with the assistance of the County Planning Commission and community members and stakeholders, drafted a series of 2040 population and employment forecasts described

as “alternative futures” for Hillsborough County. Then, on the basis of population projections developed by BEBR and the Florida Department of Transportation (FDOT), they allocated population and employment growth from 2010 to 2040 to illustrate how these could be distributed in the future under a variety of assumptions. Their effort led to three alternative scenarios for 2040, described below.

• Suburban Dream consist primarily of low‐density residential growth, with employment spread across the county. This vision, because it tends towards low‐density residential development, will consume the most agricultural and rural land of the three.

• Bustling Metro is a much higher-density approach to residential development, occurring closer to the urban centers. Employment arises primarily in the existing economic centers. These factors result in little demand to expand the urban service area boundary, and agricultural and rural lands are protected.

• New Corporate Centers envisions somewhat denser residential development, with most new jobs created in identified job centers. There may be a moderate need to expand the Urban Service Area boundary around the interstate highway and interchanges to accommodate these centers. Because much of the residential growth will continue in a suburban pattern, some agricultural and rural lands will be consumed by development. (Metropolitan Planning Organization for Hillsborough County, 2014)

After an extensive public engagement and preference survey process, a final preferred scenario, called the “Preferred Hybrid,” was developed using elements of all three but emphasizing Bustling Metro and New Corporate Centers. (Metropolitan

Planning Organization for Hillsborough County, 2014)

Table 1-1. The LUCIS approach and Odum Comparison Carr&Zwick Smart Land-Use Analysis: The LUCIS Model Odum’s compartment model land-use LUCIS land-use classification classification Productive Agriculture: Lands that produce food, fuel and fiber Protective Conservation: Environmentally Significant Compromise lands Urban/Industrial Urban: Lands that supports relatively intense human activity like residential, commercial, and industrial uses Source: Advanced Land-Use Analysis for Regional Geodesign Uning LUCIS plus. Redlands/California: ESRI Press.2016

Source: A Guidebook for Best Practices on Integrated Land Use and Travel Demand Modeling. National Center for Freight & Infrastructure Research & Education, Department of Civil and Environmental Engineering, College of Engineering, University of Wisconsin-Madison. 2016

Figure 1-1. Land Use Models Evolution

CHAPTER 2 METHODOLOGY AND PROCESS

Research Design

This research aims to answer the following questions:

• How can one conduct parcel-level land-use analysis through LUCISplus?

• How are overall regional population and employment growth distributed among counties in the two scenarios?

• How are parcel-level and TAZ-level land-use analyses different?

To do so, the research pursues the following objectives:

Objective 1. Identify the areas suitable for infill, redevelopment, and greenfield plus development by using the LUCIS .

Objective 2. Calculate the population and employment growth in the two scenarios.

Objective 3. Map and visualize the population and employment growth in the two scenarios.

Objective 4. Compare the two scenarios from a spatial distribution perspective and discuss the reasons for their differences.

For further comparison between parcel-level and TAZ-level land-use analysis, certain variable of the analysis should be controlled. The research uses the 2010 population data as a basis and set the MPO-developed 2040 Preferred Hybrid alternative population and employment projections as the projection goal for this research.

The next step is data inventory and preparation. The GIS data on 2018 parcels, roads, and historic districts are available from the Hillsborough County Property

Appraiser (HCPAFL). Transportation data, such as the Hillsborough Area Regional

Transit Plan (HART), are available from the Florida Transit Information System of

FDOT. The future land use map, urban service area, and census are available from the

Hillsborough Planning Commission. The TAZ data are available from FDOT.

The land use analysis would be divided into three parts:

• Introduce the MPO-developed TAZ-level Preferred Hybrid scenario. • Conduct parcel-level land-use analysis based on the LUCISplus model. • Compare the two scenarios and discuss potential reasons for their differences.

Hillsborough County, Florida is the study area. Figure 2-1 shows its location.

Located on the west-central coast of the state, Hillsborough County is rich with natural features. As the fourth most populous county in the state, it has steadily increased in wealth since 2006, except during the recession period of 2008–10. The diversity of businesses, including financial services, bioscience, technology, and international trade, contributes to its wealth. Due to rapid development in the mid-2000s, GIS-based decision-support tools for land-use analysis have been supported since 2002 (Zwick et al., 2016). In this research, Hillsborough County was chosen as the study area for its technical-support and context needs.

LUCIS Plus Model

This research applies LUCIS plus model to predict Hillsborough County population and employment growth from 2010 to 2040. Eight steps are included in this model: criteria evaluation matrix construction, the region growth projection determination, population and employment density determination, allocation sequence, infill allocation processing, redevelopment allocation processing, greenfield allocation processing and employment allocation processing.

LUCISplus allows users to indicate custom policy options outside the suitability models, to allocate population and employment, and to calculate the market value produced by land-use changes. The core step of the LUCISplus model is to create a

user-generated table called a criteria evaluation matrix (CEM; Zwick et al., 2016).

In the LUCISplus model, infill, redevelopment, and greenfield CEMs are created with their “conflict/opportunity, individual land use suitability, and other data to make land use decisions and to spatially allocate residential population and regional and local employment” (Zwick et al., 2016, p. 89).

Criteria Evaluation Matrix Construction

To allocate and adjust the land-use, population, and employment in each parcel, the first step is to create CEMs for LUCISplus allocation analysis. Allocation of land use requires three types of CEM: infill, redevelopment, and greenfield. LUCISplus combines all three, along with their conflict/opportunity, individual land use suitability, and other data to make land-use decisions and to spatially allocate residential populations and regional and local employment. For more detailed employment projection analysis, each

CEM allocates the following land uses: (1) residential, (2) commercial, and (3) industrial

(Zwick, et al., 2016). The industrial employment sector is easy to distinguish. The commercial sector includes retail, restaurants, and the like. The service sector includes educational, medical, and professional services (Metropolitan Planning Organization for

Hillsborough County, 2014).

The infill CEM works on the same three land-use categories, but with a fundamental difference: the locations must be identified in the property tax data or the future land-use data as existing vacant or platted lands. In addition, vacant plotted lands have been adopted for specific uses, such as residential, commercial, institutional, or industrial use. Moreover, selecting the vacant lands that are devoid of structures is the essential conceptual foundation of the term “vacant” (Zwick et al., 2016).

The greenfield CEM includes the three land-use types but generally produces

less dense development (Carr & Zwick, 2007) because the primary land use is single- family residential (Zwick et al., 2016). Most greenfield developments are outside of urban service areas and city limits.

Redevelopment happens mostly within city limits. The redevelopment CEM is based on the simple, fundamental concept that redevelopment occurs in denser, developed urban areas and is used to increase urban density, especially of mixed-use development. Redevelopment requires the destruction or adaptive reuse of existing structures. Usually, the destruction of structures must be thoughtful and follow increased reliance on transit-oriented development (TOD), increasing urban density where appropriate. This kind of replacement takes into consideration not only the buildings but the displacement of uses and existing residents or employees as well, using either a simple or a complex replacement strategy (Zwick et al., 2016).

The Region Projection Determination

According to the Hillsborough 2040 Socioeconomic Data Forecasting and

Scenario Planning Technical Memorandum (2014), the BEBR’s medium forecast data projected that Hillsborough County would gain nearly 600,000 people from 2010 to

2040. Table 2-1 displayed the BEBR forecast in detail.

As Table 2-2 shows, Hillsborough County is expected to gain 586,738 people and 400,659 jobs in total from 2010 to 2040, according to the Hillsborough

Socioeconomic Forecast Memorandum (2014). Specifically, it would gain 83,147 industrial jobs, 69,418 commercial jobs, and 248,094 service jobs, as Table 2-3 shows.

This research uses the projection data from the Preferred Hybrid scenario developed by

MPO as its projection goal. This decision was made to control the variable for later comparison.

Population and Employment Density

Once the region’s population and employment projections are determined, the next step is to calculate the density of population and employment for further allocation analysis. The population density, also called the dwelling density, reflects the residential density in the county. The employment density reflects the acres used to support employment in commercial, industrial, and service areas in the base year of 2010

(Zwick et al., 2016). In short, both of these can be regarded as land-use density, since they represent the existing build-out density of each land-use category.

This research applies land-use densities from the 2040 Socioeconomic Data

Forecasting and Scenario Planning Technical Memorandum (2015), whose data were obtained from the Countywide Future Land Use Plan and the Hillsborough County and

Jurisdiction staff, and from the relationships among general land use densities provided in the Institute of Transportation Engineers’ (ITE) Trip Generation Manual (7th Edition).

Hillsborough’s land-use density is presented in Table 2-4.

Allocation Sequence Determination

The sequence of infill, redevelopment, and greenfield allocation analysis is not optional but is determined by the goal and theories involved. In short: what kind of future you want to see will determine which sequences you choose.

For example, in conducting the Suburban Dream scenario, only infill and greenfield allocations would be taken, as the goal is “primarily low-density residential growth with employment spread across the county (Metropolitan Planning Organization for Hillsborough County, 2014).This vision would consume large amounts of agricultural and rural land. Accordingly, the development on infill and greenfield lands, which are both vacant and spread out over the county, satisfies the suburban sprawl needs.

In this research, given the goals of mixed-use, high density, and sustainability, infill and redevelopment lands will be used for allocating people at first. If the output projection does not reach the goal, then the greenfield allocation analysis will be conducted. This running sequence ensures the least consumption of agriculture lands in greenfield.

Infill Population Allocation Procedure

As discussed before, the infill CEM creation is an infill development suitability analysis. Infill locations must be identified in the property tax data or the future land use data as existing vacant or platted lands. In addition, vacant platted lands have been adopted for specific uses such as residential, commercial, institutional, or industrial.

Selecting vacant lands that are devoid of structures is the essential idea of the term

“vacant” (Zwick et al., 2016)

Based upon created infill CEMs, residential parcels of less than 6 units per acre are selected to calculate the single-family population allocation. Parcels having 6 units per acre or more are selected for multi-family allocation. Then the total dwelling units are calculated by multiplying acres and effective unit density. Finally, the allocated people are calculated by multiplying dwelling units by average household size. Figure 2-

2 shows the entire infill allocation procedure.

Redevelopment Population Allocation Procedure

The first step of population allocation analysis is to create the redevelopment

CEM, which is based on the simple idea that redevelopment occurs in previously developed but aged areas and requires the destruction or adaptive reuse of existing structures. This kind replacement takes into consideration not only the buildings, but the

displacement of uses and existing residents or employees as well, using either a simple or a complex replacement strategy (Zwick et al., 2016).

In this research, the redevelopment allocation adapts the Hillsborough Planning

Commission and MPO’s redevelopment propensity index (RPI) criteria. Figure 2-3 gives an overview of the concept and details of the criteria.

Adapting the similar categories and weighting, this research applies age of structure, transit accessibility, roadway accessibility, land value/structural value, redevelopment potential, and historic district impacts as an RPI. Unlike MPO’s RPI, which has a range setting for each index, I divide roadway access into five ranges.

Table 2-5 presents the details:

• Parcels within the ¼-mile buffer area of local roads get 1 point.

• Parcels within the ¼-mile buffer area of collectors, which includes neighbor collectors, get 2 points.

• Parcels within the ¼-mile buffer area of minor arterials get 3 points.

• Parcels within the ¼-mile buffer area of principle arterials get 4 points.

• Parcels within the ¼-mile buffer area of freeways get 5 points.

The RPI is then altered as follows:

• Parcels within the redevelopment CEM-covered area and also within the urban service area and city limits get 5 points.

• All other parcels get 0 points.

Each index is weighted differently: “age of structure” takes 30%, “transit access” takes 10%, “roadway access” takes 10%, and land value/structure value takes 30%.

This weighting follows the MPO’s RPI criteria, which are the results of discussion and analysis between different stakeholders and community leaders.

After setting down the RPI criteria, this research calculates the RPI score and then sort the parcels into four quartiles based on RPI scores. For each quartile range, a weighting factor (25%, 10%, 5%, and 0%) was developed for use in calculating the percentage of the total acres that would be considered for redevelopment within the range. For the highest quartile, 25% of the acres are considered to have a high propensity for redevelopment. For the second and third quartiles, 10% and 5% respectively are considered high propensity. No acres (0% weighting) from the lowest quartile range are considered to have redevelopment propensity. Finally, this research multiplies the weighted acreage for redevelopment use by dwelling density and average household size to get the population growth.

Greenfield Population Allocation Procedure

Vacant residential, acreage, and agriculture lands are selected outside the urban service area and city limits to create the greenfield CEM. For this CEM, the research follows the steps in Figure 2-4 to calculate the dwelling units by multiplying the acres and effective dwelling unit density. Then the research allocates people in greenfield areas equal to the units multiplied by the average household size.

Not all greenfield will be used for allocation in this research. Given the goal of creating a mixed-use, high-density, and sustainable land-use scenario, greenfield land consumption should be controlled. After several adjustments, the research found that a population allocation to parcels within a 3-km buffer of the urban service area and city limits could meet the projection goal, which is same as the Preferred Hybrid scenario: growth of 586,738 people from 2010 to 2040. These are the reasons for controlling greenfield consumption:

• Developing previously undeveloped greenfield areas would cost significantly

more than directing new development to infill lands or redevelopment lands, due to the higher cost of transportation, public services, and infrastructure (Zwick et al., 2016).

• Dunham and Williams noted that a huge amount of greyfield, which is the dead commercial area within the city, will be available in the next 15 years (as cited in Zwick et al., 2016).

• Directing development to greenfields would lead to greater environmental problems than developing infill and redevelopment lands. Dunham-Jones and Williamson pointed out that “sprawled development patterns account for the largest per-capita carbon footprints among any type of development” (Zwick et al., 2016, p. 3).

Employment Allocation Procedure

The employment allocation procedure is easier than the population allocation.

The acreage supposed to be allocated to each category has already been settled in the population allocation analysis. What is left for employment allocation is multiplying the employment density of commercial, service, and industrial areas with the allocated acreage to get the opportunity employment. This can usually be adjusted by changing the density, depending on whether the opportunity allocation has reached the projection goal. Figure 2-5 overviews the employment process.

One type of land is different. For greenfield employment allocation, only service employment is considered. Commercial and industrial employment growth can happen outside the city, but the growth is so subtle that it can be ignored, especially when these two categories of employment growths are fully satisfied by infill and redevelopment allocation. Figure 2-6 presents the greenfield employment process.

The above employment allocation process is designed for computing opportunity employment growth; the actual allocated employment needs ratio adjustment based on the projection goal. In this research, the allocated employment is selecting based on the standard deviation level of opportunity employment. The data above the standard

deviation of opportunity employment is kept for further allocation calculation. Then, to take ratio adjustment on the opportunity employment data to fit the goal. In this research, 27% of opportunity commercial employment would be counted into actual allocated commercial employment; 70% of opportunity industrial employment would be counted into actual allocated industrial employment; and 40% of opportunity service employment would be counted into actual allocated service employment.

Table 2-1. BEBR Population Forecast Projections for Hillsborough County 2010 2025 2040 2010-2040 Growth Low 1,129,226 1,358,000 1,340,300 211,074 Medium 1,129,226 1,543,100 1,723,200 593,974 High 1,129,226 1,728,300 2,106,100 976,874 Source: Florida Population Studies Volume 46, Bulletin 165, March 2013

Figure 2-1. Hillsborough County

Table 2-2. Preferred Hybrid: Population and Employment Forecast Projections 2010 2040 Growth Total population 1,229,226 1,815,964 586,738 Total employment 711,400 1,112,059 400,659 Source: 2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum, July 2014

Figure 2-2. Infill Allocation Process

Table 2-3. Preferred Hybrid: Employment Totals by Type 2010 2040 Growth Total employees 711,400 1,112,059 400,659 Industrial employees 156,600 239,747 83,147 Commercial employees 132,000 201,418 69,418 Service employees 422,800 670,894 248,094 Industrial/total employee ratio 22% 22% Commercial/total ratio 19% 18% Service/total ratio 59% 60% Source: 2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum, July 2014

Figure 2-3. Redevelopment Propensity Index Criteria of MPO

Table 2-4. Units Per Acre in Hillsborough County Land Use Land Use Adjusted Forecasted Units per Acre Code Dwelling Industri- Commer Service units al -cial employ- employe employe ees es es Agricultural/Mining-1/20 AM-1/2 0 0 0 0 Agricultural-1/10 A-1/10 0.1 1.1 0 0.1 Agricultural/Rural-1/5 AR-1/5 0.1 1 0.1 0.2 Agricultural Estate-1/2.5 AE-1/2.5 0.1 0.9 0.1 0.2 Planned Environmental PEC-1/2 0.1 0 1.7 2.9 Community-1/2 Residential-1 RES-1 1 0 0.3 0.9 Residential-2 RES-2 1.5 0 0.9 1.4 Residential Planned-2 RP-2 1.2 0 1.7 2.8 Wimauma Village WVR-2 1.1 0.2 1.2 2 Residential-2 Residential-4 RES-4 2.2 0 0.9 1.4 Neighborhood MixedUse- NMU-4(3) 1.6 0 4.8 12 4(3) Residential-6 RES-6 3.4 0 0.9 1.4 Suburban Mixed Use-6 SMU-6 2.9 2.5 3.6 6 Residential-9 RES-9 5 0 3.4 5.7 Residential-12 RES-12 7.2 0 1.7 5.7 Residential-16 RES-16 9.6 0 1.7 5.7 Community Mixed Use-12 CMU-12 6.2 1.2 5.1 8.6 Residential-20 RES-20 12 0 1.7 8.6 Residential-35 RES-35 21 0 2.4 17.2 Office Commercial-20 OC-20 1.6 0 9.6 43 Urban Mixed Use-20 UMU-20 4 7.5 17.1 40.1 Regional Mixed Use-35 RMU-35 4.2 10 41 103.2 Citrus Park Village CPV 0 0 0 0 Research/Corporate Park RCP 0 14.9 1.4 68.8 Light Industrial LI 0 14.9 1.4 17.2 Light Industrial-Planned LI-P 0 14.9 1.7 17.2 Heavy Industrial HI 0 17.4 0.9 5.7 Energy Industrial Park EIP 0 12.2 0.3 19.7 Electrical Power Generating EPGF 0 16.2 1.7 8.6 Facility Natural Preservation N 0 0 0 0 Major Public/Quasi-Public P 0 0 0 0 Central Business District CBD 24 0 205 458.5 Residential-83 Res-83 49.8 0 4.4 7.5

Table 2-4. Continued Land Use Land Use Adjusted Forecasted Units per Acre Code Dwelling Industrial Commer- Service units employee cial employe -s employe- -es es Residential-50 Res-50 30 0 4.1 11.5 Residential-35 Res-35 21 0 4.1 6.9 Residential-20 Res-20 12 0 3.4 5.7 Residential-10 Res-10 5.6 0 1.2 2 Residential-6 Res-6 3.4 0 1.2 2 Residential-3 Res-3 1.7 0 1.2 2 Rural Estate-10 RE-10 0.1 0 0 0 Regional Mixed Use RMU-100 24 0 71.7 120.4 Urban Mixed Use-60 UMU-60 14.4 0 55.5 111.8 Community Commercial-35 CC-35 2.8 0 82 57.3 Community Mixed Use-35 CMU-35 5.6 0 41 91.7 Suburban Mixed Use-6 SMU-6 1 0 13.7 22.9 Suburban Mixed Use-3 SMU-3 0.6 0 5.1 8.6 General Mixed Use-24 GMU-24 1.9 14.9 30.7 51.6 Transitional Use-24 TU-24 1.9 14.9 30.7 51.6 Municipal Airport M-AP 0 44.8 10.2 51.6 Compatibility Heavy Industrial HI 0 52.3 5.1 8.6 Light Industrial LI 0 44.8 5.1 25.8 Public/Semi-Public P/SP 0 0 6.8 11.5 Recreation/Open Space R/OS 0 0 0.7 1.1 Environmentally Sensitive ESA 0 0 0 0 Areas Residential-4 R-4 2.2 0 0 0 Residential-9 R-9 5 0 0.9 1.4 Residential-18 R-18 10.8 0 2.4 4 Commercial C 0.4 0 20.5 14.3 Office/Institutional O-I 0.4 0 6.8 34.4 Research Corporate Park RCP 0 2.5 6.8 68.8 Public/Semi-Public P-SP 0 0 6.8 11.5 Parks, Recreation and Open P-R-OS 0 0 6.8 11.5 Space Community Mixed Use-12 CMU-12 1.9 1.2 10.2 22.9 Urban Mixed Use-20 UMU-20 4.8 2.5 17.1 34.4 Urban Mixed Use-25 UMU-25 6 0.5 3.4 6.9 Downtown Mixed Use-25 DMU-25 6 0 25.6 51.6

Table 2-4. Continued Land Use Land Use Adjusted Forecasted Units per Acre Code Dwelling Industrial Commer- Service units employee cial employe -s employe- -es es Residential-4 Res-4 2.2 0 0 0 Residential-6 Res-6 3.4 0 0.9 1.4 Residential-9 Res-9 5 0 1.2 2 Residential-12 Res-12 6.7 0 1.2 2 Residential-20 Res-20 12 0 2.4 4 Mixed Use- MU-R/C 2.9 0 9.6 8 Residential/Commercial Mixed Use- MU-R/C/I 2.9 3.5 7.2 8 Residential/Commercial/Ind Mixed Use-Gateway MU-G 2.6 2.6 4.8 16 Light Commercial/Office LC/O 0 0 7.2 16 Commercial C 0.6 0 14.3 10 Downtown Core DC 3 12.4 85.4 171.9 Residential-4 Res-4 2.2 0 0 0 Residential-6 Res-6 3.4 0 0.9 1.4 Residential-9 Res-9 5 0 1.2 2 Residential-12 Res-12 6.7 0 1.2 2 Source: 2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum, July 2014

Figure 2-4. Greenfield Allocation Process

Table 2-5. Adapted Redevelopment Propensity Index Criteria RPI Data Calculation Range Score Weight Age of structure Parcel_18 2040—actual 25 35 1 30% built year 36 50 2 51 75 3 76 76+ 4 (year) Roadway Parcel_18; ¼ mile buffer Local; 1 10% access roads from picked Collector; 2 roads Minor arterial; 3 Principal arterial 4 Freeway 5

Transit access Parcel_18; ¼ mile buffer Normal bus 3 10% HART_sto from picked stops; ps stops Transit transfer 5 center, park&ride

Value weight Parcel_18 Land 0 0.5 1 50% value/structure 0.51 1.25 2 value 1.26 2.5 3 2.51 5 4 5.01 5.01+ 5 Historic district Parcel_18; Select parcels Parcels are 1 -1 historic within historic within the district district historic district Redevelopment Parcel_18 Developed and Redevelopment 5 1 area within the urban parcels within service area city limits and urban service Source: 2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum, July 2014

Figure 2-5. Infill and Redevelopment Employment Allocation Procedure

Figure 2-6. Greenfield Employment Allocation Procedure

CHAPTER 3 ANAYLSIS AND CONCLUSION

Parcel-Level LUCIS Plus Scenario

This research follows the previous steps of LUCISplus to allocate population and employment to 2040 Hillsborough County at the parcel level.

Population Allocation

To calculate the population growth, this research multiplies the GIS acres of the developed area by the dwelling density (units per acre) and then the average household size. The result is shown in Table 3-1. The MPO goal for population growth is 586,738, and the allocated population in the parcel-level LUCISplus scenario is 596,647, which meets the goal of the TAZ-level Preferred Hybrid scenario developed by MPO.

As Figure 3-1 shows, 39% of the growth population is allocated to the redevelopment area, 33% to infill lands, and 28% to greenfield lands. The proportions are quite close. The slightly larger allocation to redevelopment land indicates that the parcel-level LUCISplus scenario emphasizes dense development over suburban sprawl.

Visualizing the distribution at the parcel level is not as intuitive as on the TAZ level, since the parcel scale is too small to sketch the symbology changes for the entire county. The parcel-level LUCISplus scenario summarizes the data then relates it to the

TAZ level for better visualization and comparison.

Figure 3-2 maps out the population growth distribution in the parcel-level

LUCISplus scenario displayed in TAZ. As more people are allocated, the color moves from green to red. In southern Hillsborough County, 18,068 people are allocated to zone

600, which has the greatest population growth in the parcel-level LUCISplus scenario within Hillsborough County. Within this zone, 11,551 people are allocated into

greenfield, 2,296 into redevelopment, and 4,219 into infill lands.

Employment Allocation

The results of the employment allocation analysis are displayed in Table 3-2.

Commercial employees would increase by 69,495 by 2040, industrial employees by

83,283, and service employees by 296,638. Jobs of all types shot the MPO Preferred

Hybrid goals for employment growth.

According to Figure 3-3, the ratio of commercial employment to total employment should increase from 18.6% to 17.4% from 2010 to 2040. That of industrial employment should decrease from 22% to 20.7%, and that of service employment should increase from 59.4% to 62%. This change to employment structure indicates that the analysis satisfies the goal of increasing mixed-use in the county.

Figure 3-4 presents the employment allocation distribution of the parcel-level

LUCISplus scenario displayed in TAZ. The employment growth is within or around service areas.

TAZ-Level Preferred Hybrid Scenario

Before the Preferred Hybrid scenario developed by the Hillsborough Planning

Commission and MPO is introduced, the Bustling Metro and New Corporate Centers scenarios needs to be introduced. The Bustling Metro future was developed to reflect a vision of combining future development with a multimodal transportation network. It emphasizes development in station areas identified by the MPO staff. The New

Corporate Centers alternative was designed for economic development and job creation, with jobs added to areas of economic emphasis that had been previously identified by studies or by MPO staff. Employment was allocated in the industrial, service, and commercial categories.

The Preferred Hybrid scenario was developed by extracting the essence of each of these plans and adding some concerns of the suburban dream model,

as a result of community feedback showing a preference for homes near the urban core and employment at corporate centers. Dwelling units were added to areas around potential transit stations and jobs were added to areas of economic emphasis. (Metropolitan Planning Organization for Hillsborough County, 2014)

The Preferred Hybrid scenario emphasizes economic growth and residential development focused on areas identified as having “the potential for future fixed- guideway transit service” (Metropolitan Planning Organization for Hillsborough County,

2014, p. 23).

Population Allocation

Table 3-1 presents the Preferred Hybrid scenario’s projected population growth of 586,738 people from 2010 to 2040.

As Figure 3-5 shows, most of the allocated population in this scenario is in or near the urban service area. However, little within the Tampa service area indicates that most of the Tampa area is fully developed.

According to the growth data, one zone in the south part near Ruskin grows the most. It is supposed to increase by 28,042 people. The main land-use group for this zone is agricultural, except that the north of the district includes riverside recreational facilities and resort areas, as presented in Figure 3-6. In the Preferred Hybrid scenario, the greater population allocation in this zone 612 may be due to the River Vista RV

Village, a complex recreational district with a resort, secluded beach, campground, golf courses, and fishing area. The riverside recreational area may be enlarged in further county development plans.

Employment Allocation

Table 3-2 shows the breakdown of employment growth from 2010 to 2040 in the

Hybrid scenario. Commercial employees would increase by 69,418, industrial employees by 83,147, and service employees by 248,094. According to Figure 3-7, the ratio of commercial employment to total employment should decrease from 18.6% to

18.1%, that of industrial employment from 22% to 21.6%, and that of service employment from 59.4% to 60.3%.

Figure 3-8 presents the 2040 employment growth distribution in the Preferred

Hybrid scenario. Most growth is allocated to the urban service area or around the Plant

City. The zone that contains River Side RV Village not only has certain population growth, but large employment growth too. This indicates that River Side RV Village will play an important role in future regional comprehensive plans.

Comparison and Conclusion

The TAZ-level and parcel-level land-use analyses have each presented distribution characteristics and allocation outputs.

From an allocation output perspective, the TAZ-level population projection is the reference line for the parcel-level allocation analysis, it is good to see the parcel-level allocation analysis output satisfies the population growth goal. In addition, the parcel- level scenario’s changes to the proportions of the employment category is more obvious than the TAZ-level’s, especially the increased service employment ratio, which satisfies one of the goals of increasing mixed-use areas.

As for distribution characteristics, both population and employment growth in the parcel-level scenario are not as concentrated as in the TAZ-level scenario. In the Figure

3-9 of population growth distribution comparison, though the parcel-level scenario is

more sprawling than the TAZ-level scenario, it does not mean the parcel-level scenario allocation is unreasonable. Moreover, the spread light green and yellow zone in parcel- level scenario shows the population development potentials in these areas which are not presented in TAZ-level scenario. Figure 3-10 shows that the allocation trend in the parcel-level scenario is around the I-4 and I-75, which means this parcel-level scenario has opportunity to boost employment growth along these two highways. In addition, the

Figure 3-10 shows there are high employment growth areas along the Tampa shoreline.

The red high employment growth area shows the potential development in Tampa seaside district.

To discuss the population and employment growth distribution in detail, the zone

765 is a good example to show two scenario’s differences.

From the Figure 3-11, In the TAZ-level scenario, zone 765 has the most allocated employment, with 12,391 employees: 9,205 service, 1,500 commercial, and

1686 industrial. In parcel-level scenario, this zone allocated 6,060 employees: 4,036 service, 1,317 commercial, and 707 industrial. The existing land use category of this area is the vacant commercial lands with stores. The parcels in zone 765 are panned to be residential mixed-use (RMU) land in future, along with the nearby redevelopment lands. Table 3-3 shows the zone 765 employment allocation result of parcel-level scenario analysis. While TAZ-level analysis allocated two times of employment for zone

765 in parcel-level scenario. The result of TAZ-level scenario exceeds the fixed density index which is set by Hillsborough County. In this case, parcel-level land-use analysis is more rational and reasonable than the TAZ-level land-use analysis. Accordingly, TAZ- level land-use analysis is not suitable for detailed allocation analysis since it may result

in density conflicts with existing land-use plan.

Reasons behind these differences between parcel-level and TAZ-level land-use analysis outputs are as follows:

First, since the TAZ-level scenario is a hybrid scenario that integrates the

Bustling Metro and New Corporate Centers and a portion of the Suburban Dream scenario, the rules, models, and goals that make up the scenario’s theoretical base are different from those of the parcel-level scenario, which uses the LUCISplus model. And this parcel-level analysis does not consider future transportation network areas in

Hillsborough County, which is one of the main points of the TAZ-level scenario. As a result, the allocation result and distribution trend are different.

Second, the practical approaches taken in the two scenarios are different. The land-use categories are also different. The TAZ-level Preferred Hybrid scenario developed its allocation on vacant lands, whereas the parcel-level LUCISplus scenario split the vacant lands into infill and greenfield. In LUCISplus, the criteria and weight of infill and greenfield were different. Given the different goals, different weights could be applied for infill and greenfield.

Third, given that almost all the transportation analysis zones are larger than the parcels, the parcel-level land use analysis allows for sketching micro changes through

GIS. If the two scenarios’ outputs were compared at the parcel level, the distribution center might be skewed to a certain degree because the parcels reveal more land-use details, whereas the TAZ displays degrees of concentration on a larger scale.

Finally, the allocation decision about MPO’s TAZ-level scenario planning is affected by suggestions from community leaders, stakeholders, and commission staffs.

Public engagement is fully applied by MPO’s TAZ-level scenario planning, which results in different allocation outputs because the parcel-level LUCISplus scenario is mainly controlled by the data itself. What this parcel-level scenario presents is not a planning decision, but an alternative future based mainly on data characteristics, not stakeholders’ opinions. The best example of this is the fact that Figure 3-6 zone 612 is supposed to increase by a large number of people and jobs, even though it is not within an urban service area and more than 70% of the acreage in this district is agriculture land. These characteristics could easily close the door to zone 612 being allocated a large number of new residents and new jobs, on the basis of the theory and rules of sustainable high-density planning. However, the Preferred Hybrid scenario sets this zone as the one of the critical development areas. The Riverside Club of this district was included early as one of the stakeholders. Public engagement also puts limitations on simple land-use data analysis.

In conclusion, the parcel-level land-use analysis meets Hillsborough County development goal. From the comparison between parcel-level and TAZ-level land-use analysis, the research found the parcel-level scenario’s population and employment growth distribution are more sprawling than the TAZ-level which indicates the parcel- level scenario is not as dense as the TAZ-level scenario. However, the parcel-level land-use scenario provides alternative future for the county which focuses on infill and redevelopment within city limits instead of sprawling into suburban area. The reasons that leads to the differences between two scenarios are: the different conceptual framework, analysis approach, and analysis scale affect the concentration degree and output distribution. Lessons are learned from the comparisons that public engagement

is necessary to planning because it makes up for the limitations of pure data-based analysis. Also, different scales and methods could be deployed in different planning processes for different purposes. For example, when discussing the entire county’s allocation trend, the TAZ-level display is appropriate, but when discussing each community’s allocation trend, the parcel-level display is better.

For the further research into this issue, the redevelopment index could be updated from the roadway access analysis. The roadway concurrency, which is the case of roadway bearing two or more different route numbers (Esri, 2014), would have impact on the nearby district’s redevelopment. The roadway characteristics instead of its types could be considered as redevelopment allocation index. In this perspective,

Roadway Characteristics Inventory (RCI) published by FDOT provides roads features and characteristics which is meaningful for land redevelopment.

All in all, this research conducts a parcel-level land use allocation scenario which provides a dense development model for Hillsborough County. Being different from the sprawl development model typical of the U.S, this parcel-level scenario focuses on allocating growing population and employment into infill and redevelopment lands in place of consuming the agricultural greenfield lands.

By comparing the parcel-level and TAZ-level land-use analysis, though tow scenarios’ outputs are different, both scenarios complete good job in allocating growing people since they provide reasonable alternative futures of Hillsborough County. This research proves that planning development scenarios and comparing land-use models in detail are excellent processes for coming to understand land-use policy, models, and related theories.

Table 3-1. Parcel-Level LUCIS plus Population Allocation Category Allocated Person Infill 198,696 Redevelopment 230,220 Greenfield 167,731 Total allocated people 596,647 Goal (TAZ-level data) 586,738

Parcel-Level Allocation: Population Growth

28% 33%

39%

Infill Redevelopment Greenfield

Figure 3-1. Parcel-Level LUCISplus Scenario: Population Growth Proportion of Each Land Use Category

Table 3-2. Parcel-level land use analysis Employment Allocation and Goal Category 2010 2040 Growth Growth Goal Commercial employees 132,000 201,495 69,495 69,418 Industrial employees 156,600 239,883 83,283 83,147 Service employees 422,800 719,438 296,638 248,094 Total allocated employees 711,400 1,160,816 449,416 400,659

Allocated Population Growth

Figure 3-2. Parcel-Level LUCISplus Scenario: Population Growth Distribution Map

Table 3-3. The Employment Allocation of Zone 765—Redevelopment Section Field Value FID 12609 LU_GRP (Land use group) VACANT COMM, STORE/SHP TAZPOPID 765 TAZEMPID 765 Future Land Use RMU-35 Industrial employees’ density 10 Service employee’s density 103.2 Commercial employee’s density 41 Effective dwelling units (density) 4.2 Allocated commercial employees 1,317 Allocated industrial employees 707 Allocated service employees 4,036 Allocated employees 6,060 Allocated employees in TAZ scenario 12,391

Parcel-Level scenario: Employment Growth 70.0% 60.0% 62.0% 50.0% 59.4% 40.0% 30.0% 20.0% 22.0% 20.7% 10.0% 18.6% 17.4% 0.0% Commercial/total employee Industrial/total employee Service/total employee ratio ratio ratio

2,010 2,040

Figure 3-3. Parcel-Level LUCISplus Scenario: Employment Allocation/Total Employment Ratio

Allocated Employment Growth

Figure 3-4. Parcel-Level LUCISplus Scenario: Employment Growth Distribution Map

Figure 3-5. 2040 Hillsborough County Preferred Hybrid Scenario Population Growth Map

Figure 3-6. Transportation Analysis Zone 612 with greatest population growth in Preferred Hybrid scenario from MPO

Preferred Hybrid Scenario: Employment Growth 70.0% 59.4% 60.3% 60.0% 50.0% 40.0%

30.0% 22.0% 21.6% 18.6% 18.1% 20.0% 10.0% 0.0% Commercial/total employee ratio Industrial/total employee ratio Service/total employee ratio

2010 2040

Figure 3-7. Preferred Hybrid Scenario: Each Type of Employment Allocation /Total Employment Ratio

Figure 3-8. 2040 Hillsborough County Preferred Hybrid Scenario Employment Growth Distribution Map

Figure 3-9. Population Growth Distribution Comparison: Parcel-level scenario vs. TAZ- level scenario.

Figure 3-10. Employment Growth Distribution Comparison: Parcel-level scenario vs. TAZ-level scenario.

Figure 3-11. Employment Growth Comparison: Zone 765 Example Case Map

LIST OF REFERENCES

Britannica. (2010, February 3). Compton's Encyclopedia. Retrieved from https://www.georgiastandards.org/resources/Lexile_in_Action/MM1D3_The- Census.pdf

Carr, M. H., & Zwick, P. D. (2007). Smart Land-Use Analysis: The LUCIS Model. Redlands, Carlifornia: ESRI Press.

Esri. (2014, March 4). Realigning Concurrent Routes. Retrieved from ArcGIS Resources: http://resources.arcgis.com/en/help/main/10.2/index.html#//02230000002300000 0

Golias, M., Mishra, S., & Psarros, I. (2016). A Guidebook for Best Practices on Integrated Land Use and Travel Demand Modeling. National Center for Freight & Infrastructure Research & Education, Department of Civil and Environmental Engineering, College of Engineering, University of Wisconsin-Madison.

Malczewski, J. (2004, July). GIS-based land-use suitability analysis: a critical overview. Progress in Planning.

Matternicht, G. (2017). Global Land Outlook Working Paper: Land Use Planning. United Nations.

Metropolitan Planning Organization for Hillsborough County. (2014). 2040 Socioeconomic Data Forecasting and Scenario Planning. Tampa.

Zwick, P. D., Patten, E. I., & Arafat, A. (2016). Advanced Land-Use Analysis for Regional Geodesign Uning LUCIS plus. Redlands/Carlifornia: ESRI Press.

BIOGRAPHICAL SKETCH

Ke Lin was grew up in Wuhan, an ancient sprawling commercial city of Central

China. She experienced its peak and trough period. With the interest of how to make the hometown better, Ke Lin pursued Master of Urban and Regional Planning at

University of Florida after received the bachelor’s degree in English Literature from

Wuhan University. Ke Lin concentrated on the GIS and land-use regional analysis.