2040 Socioeconomic Data Forecasting and Scenario Planning ... · 7/9/2014 · for transportation...

2040 Socioeconomic Data Forecasting and Scenario Planning Technical Memorandum Prepared For:

601 East Kennedy Boulevard Tampa, FL 33602 July 9, 2014

Hillsborough MPO 2040 Socioeconomic Date Forecast July 9, 2014 Technical Memorandum Page i

Table of Contents

1.0 Introduction ........................................................................................................................... 1 1.1 Purpose ......................................................................................................................................... 2 1.2 Organization of the Report ........................................................................................................... 2 1.3 Key Considerations ....................................................................................................................... 2 1.4 Population Forecasts (BEBR) ........................................................................................................ 2 1.5 Document Review ......................................................................................................................... 2 1.6 Stakeholder Interview Guidance .................................................................................................. 3 1.7 Population and Control Total Assumptions .................................................................................. 4 1.8 Summary of the Scenarios ............................................................................................................ 6

2.0 Methodology .......................................................................................................................... 7 2.1 Methodology Overview ................................................................................................................ 7 Forecast Traffic Analysis Zones (TAZ)............................................................................................... 7

2.2 Greenfield (Vacant Developable Land) Forecasting Procedures .................................................. 8 Vacant Developable Lands Methodology ........................................................................................ 8 Population and Employment Allocation Methodology ................................................................... 9 Allocation of Population and Employment to Traffic Analysis Zones ............................................ 12

2.3 Redevelopment Forecasting Procedures .................................................................................... 13 2.4 Center Forecasting Approach ..................................................................................................... 14 Bustling Metro ............................................................................................................................... 14 New Corporate Centers ................................................................................................................. 14 Preferred Hybrid Scenario ............................................................................................................. 14

2.5 School Enrollment ....................................................................................................................... 14 2.6 Hotel/Motel Units ....................................................................................................................... 14 2.7 Review and Adjustment Process ................................................................................................ 14 2.8 Conversion of Forecast Data to Travel Demand Model Inputs .................................................. 15

3.0 Scenario Forecasts ............................................................................................................... 16 3.1 Scenario Overview ...................................................................................................................... 16 3.2 Suburban Dream ......................................................................................................................... 16 Methodology and Assumptions ..................................................................................................... 17

3.3 Bustling Metro ............................................................................................................................ 18 Methodology and Assumptions ..................................................................................................... 18

3.4 New Corporate Centers .............................................................................................................. 20 Methodology and Assumptions ..................................................................................................... 20

3.5 Preferred Hybrid Scenario .......................................................................................................... 22 Methodology and Assumptions ..................................................................................................... 23 Next Steps ...................................................................................................................................... 25

List of Figures Figure 1: Comparison of Historical and Projected Growth Rates (Hillsborough County and Florida) ......... 4 Figure 2: Comparison of Historical and Projected Growht Rates (Hillsborough, Florida, and Key Metro) . 5 Figure 3: Land Use Allocation Process ........................................................................................................ 10 Figure 4: Redevelopment Propensity Index Criteria ................................................................................... 13

Hillsborough MPO 2040 Socioeconomic Date Forecast July 9, 2014

Final Document Page ii

Figure 5: Scenario comparison table .......................................................................................................... 16

List of Maps Map 1: Hillsborough County Traffic Analysis Zones ..................................................................................... 8 Map 2: Hillsborough County Planning Areas .............................................................................................. 11

List of Appendices (provided as separate documents)

Appendix A – Units per Acre ......................................................................................................... 26

Appendix B ‐ Redevelopment Propensity Index (RPI) model ....................................................... 28

Appendix C ‐ Calculation of Attractiveness Index Model ............................................................. 29

Appendix D ‐ 2040 Suburban Dream Population and Employment Forecast Results .................. 31

Appendix E ‐ 2040 Bustling Metro Population and Employment Forecast Results ...................... 42

Appendix F ‐ 2040 New Corporate Centers Population and Employment Forecast Results ........ 53

Appendix G ‐ The TAZs identified for transit stations ................................................................... 64

Appendix H ‐ The TAZs identified as possible expansion areas for the urban service boundary . 65

Appendix I ‐ 2040 Preferred Hybrid: Population and Employment Forecast Results .................. 66

Hillsborough MPO 2040 Socioeconomic Date Forecast July 9, 2014 Technical Memorandum Page 1

1.0 Introduction

The Hillsborough County Metropolitan Planning Organization (MPO) is responsible for coordinating transportation planning for Hillsborough County. As part of this effort, the MPO is responsible for updating the federally‐mandated Long Range Transportation Plan (LRTP) every five years. Related to the 2040 LRTP update, the Planning Commission is also working on simultaneous updates for the Comprehensive Plans for Plant City, Temple Terrace, Tampa and Unincorporated Hillsborough County. The purpose of the LRTP is to identify needed transportation improvements within the county. The Comprehensive Plan guides public policy in terms of transportation, utilities, land use, recreation, and housing. Together, these documents guide future development of the county.

One of the first steps in the LTRP process is to develop a forecast of the geographic distribution of the county’s population and employment over the LRTP timeframe. These “socioeconomic” data document anticipated population and employment concentrations and are used to forecast future travel patterns.

Because public policy and transportation investment decisions can influence how the county grows, the MPO planning staff, with the assistance of the Planning Commission and other community members and stakeholders, began the LRTP planning process by drafting a series of 2040 population and employment forecasts identified as “Alternative Futures” for Hillsborough County. These alternative forecasts start by using the population and employment data from 2010 as a basis. Then, population and employee growth based on population projections developed by the Florida Department of Transportation (FDOT) and the University of Florida’s Bureau of Economic and Business Research (BEBR) were allocated to show how population and employees could be distributed in the future based on different assumptions and community values. This effort resulted in the development of three alternative futures scenarios for 2040, described briefly below. Based on an extensive public engagement and preference survey process, a final preferred scenario was developed using elements of the three alternatives:

Suburban Dream is primarily 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, occuring closer to the urban centers. Employment occurs 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 consumed by development.

The data sets and scenarios represent a cooperative effort among the Hillsborough MPO, FDOT District 7, local municipalities and the local government jurisdictions in Hillsborough County.

The scenarios were also reviewed by the public. The MPO engaged the community in a series of workshops and online survey to gather feedback on the three scenarios and help identify the characteristics that should be included in the final preferred Hybrid Scenario.


1.1 Purpose

The purpose of this report is to document the assumptions, methodology, and resulting forecast scenario for input into the Tampa Bay Regional Planning Model (TRBPM). The TRBPM uses existing and forecasted population and employment data to predict travel patterns and thereby identify the demand for transportation system investments.

1.2 Organization of the Report

This report is organized into four chapters. The first chapter describes the key considerations, the base data, and background research. Chapter 2 describes the methodology for allocating the different types of development. Chapter 3 details the different alternative future forecasts that were developed by the MPO and its stakeholders. The final chapter describes the preferred hybrid scenario.

1.3 Key Considerations

Key considerations for the development of the alternative socioeconomic forecast scenarios begin with the population forecast provided by BEBR but also consider past planning studies and initiatives as well as the input of knowledgeable public and private sector stakeholders. These considerations apply to both the overall amount of population and employment growth expected during the planning timeframe as well as to how that growth should be allocated throughout the county. The combination of these factors was used to establish different population and employment growth “control totals” for each of the alternative scenarios.

1.4 Population Forecasts (BEBR)

Historically, Hillsborough County population has grown by more than two percent per year. While annual growth rates decreased in the short‐term due to the Great Recession, recent data suggest that the county is now experiencing a recovery—home sales are increasing and unemployment rates are decreasing. According to the BEBR’s medium forecast, Hillsborough County is expected to add nearly 600,000 persons from 2010 to 2040. The middle range of BEBR’s population projections is used in planning efforts such as this one. Table 1 shows the BEBR population forecasts for Hillsborough County.

Table 1: BEBR Population Forecast Projections for Hillsborough County

2010 2025 2040

2010–2040 Growth

Low 1,129,226 1,358,000 1,440,300 211,074

Medium 1,129,226 1,543,100 1,823,200 593,974

High 1,129,226 1,728,300 2,206,100 976,874 Source: Florida Population Studies Volume 46, Bulletin 165, March 2013.

1.5 Document Review

To better understand the vision for the future of Hillsborough County, the following documents were reviewed:

Locational Assessment and Recommended Strategic Plan for Economic Growth, (Tampa Hillsborough Economic Development Corporation, (2010);

Economic Potential Evaluation of the Future of Hillsborough County Comprehensive Plan(Hillsborough County City‐County Planning Commission, 2011);

Economic Development Area Analysis & Mapping (Imagine 2035) (Hillsborough County City‐County Planning Commission, 2011);


Economic Prosperity Stakeholder Committee Recommendations (Economic Prosperity Stakeholder Committee, 2012)

Northeast Plant City Area Master Plan (City of Plant City, Hillsborough County City‐County Planning Commission, Hillsborough County MPO, 2008);

I‐4 Economic Corridor Study (Hillsborough County City‐County Planning Commission ,2009);

various Comprehensive Plans (Unincorporated Hillsborough County, Tampa, and the cities of Plant City and Temple Terrace)

State of the Port (Tampa Port Authority, 2012);

Urban Service Area Capacity Study (University of South Florida, 2011); and the

Comprehensive Plan Density Analysis Report, developed ( University of South Florida, 2005)

There are a number of common themes in these documents, and they all support the vision of an economically vibrant Hillsborough County. To this end, they identify both opportunities and challenges with the current ways of doing business. Opportunities include diverse economic development areas and strong institutional drivers throughout the region. Challenges include some of the current policies and a lack of overall comprehensive economic development strategy. The initial alternative futures, as well as the preferred Hybrid Scenario identified by the MPO staff and its stakeholders reflect different ways the county can develop based on an overall vision of transportation and land use.

1.6 Stakeholder Interview Guidance

Early in the project, Hillsborough County MPO staff identified stakeholders to be interviewed about the Long Range Transportation Plan and the challenges relating to population and job growth facing the county. The following individuals were asked to identify areas of growth and what types of growth and development they felt were appropriate. They were also asked about job creation initiatives, infrastructure investments, and redevelopment opportunities and challenges that may be faced by the county as it continues to grow.

Table 2: Stakeholders Interviewed

Stakeholders Interviewed Agency Lucia Garsys, with Gene Boles, Mike Williams Hillsborough County

Brad Parrish, Charles Stephenson City of Temple Terrace

Greg Horwedel City of Plant City

Brian Grady, Joe Mareda, Frank Braux, Paula Harvey Hillsborough County

Ron Barton Hillsborough County

Bob McDonaugh; CRA Directors Vince Pardo, Ed Johnson, Jeannette Fenton City of Tampa

Rick Homans Tampa Hillsborough EDC

Cathy Coyle (Randy Goers,Thom Snelling) City of Tampa

Jennifer Doerfel Tampa Bay Builders Association

Jeff Rogo Bay Area Apartment Association

Bruce Erhardt Cushman & Wakefield

Kami Corbett and/or Keith Bricklemeyer, Michael Brooks NAIOP

Ron Rotella, with Al Austin and/or other major private interests Westshore Alliance

Chris Burdick Downtown Partnership

Rick Harcrow Newland Communities

Dave Mechanik Florida Fairgrounds

Jerome Ryans and Leroy Moore Tampa Housing Authority


1.7 Population and Control Total Assumptions

As noted above, the BEBR Medium population estimate has traditionally been used as the basis for LRTP socioeconomic forecasting; however, several factors suggested the use of alternative, lower growth forecasts for some of the alternative scenarios. These factors include:

uncertainty on how quickly Florida’s economy (and related growth) will recover following the Great Recession

questions of how land supply and land use policy may affect the county’s growth forecast

questions related to the relationship between the county’s growth rate and the growth rate of the state as a whole

questions regarding how overall regional growth will be distributed among counties in the Tampa Bay metropolitan area

To understand these issues, it is helpful to look at both historical and projected growth in Hillsborough County compared to Florida and other large, metro counties. Figure 1 compares Hillsborough County’s growth rate per decade to the statewide growth rate. In general, the shape of the Hillsborough County curve mimics the statewide curve from the 1970 through 2000—when the state grew quickly, the county grew quickly as well, albeit at a slower rate than the state as a whole. From 2000 to 2010, however, the relationship reverses with the Hillsborough County’s growing at a faster rate than the state as a whole.

Projecting forward from 2010, the BEBR Medium estimate maintains the county’s relatively higher growth rate as a starting position and then trends the county’s rate downward in a way that suggests parity with the state’s growth rate sometime beyond the 2040 planning timeframe. By locking in the (relatively) higher growth rate experienced by Hillsborough from 2000 to 2010 and only gradually setting Hillsborough County’s growth rate to intercept with the state’s, the BEBR Medium projection implies a structural shift in how statewide population growth will be allocated going forward. Otherwise, Hillsborough County could regress to the mean or resume its status as growing more slowly than the state on average.

Figure 1: Comparison of Historical and Projected Growth Rates (Hillsborough County and Florida)

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1970 1980 1990 2000 2010 2020 2030 2040Hillsborough Growth Rate Florida Growth Rate


To better understand the shift implied by the Hillsborough County forecast, it is helpful to look at the history and forecasts of other metropolitan counties. Figure 2 adds the combined growth rates for Miami‐Dade, Broward, and Palm Beach counties to the graph as well as the historical and projected growth for Orange County. From these data, the cause in the shift in Hillsborough County’s projected growth rate relative to the state as a whole is apparent—Southeast Florida can no longer absorb the statewide demand in the way it did prior to the 1980s, and other counties are expected to grow more quickly relative to the state as a result. Figure 2: Comparison of Historical and Projected Growht Rates (Hillsborough, Florida, and Key Metro)

More explicitly, the seven most populous counties—Miami‐Dade, Broward, Palm Beach, Hillsborough, Orange, Pinellas, and Duval—account for more than 50 percent of the state’s total population. Of these dominant metro counties, the three Southeast Florida counties and Pinellas County are very nearly out of vacant, developable land. Conventional wisdom suggests that infill and redevelopment is more costly and harder to mass‐produce than the “greenfield” development that has dominated Florida since the 1950s, and, therefore, these counties will not grow as quickly going forward as they did in the past. Assuming that the statewide growth forecast is correct, the impact of this is that Hillsborough and Orange counties, as well as other Tampa Bay, Southwest, and Central Florida counties such as Lee, Polk, and Pasco, will absorb a proportionally greater share of Florida’s growth than was the case prior to 2000.

While this “macro‐level” analysis suggests that Hillsborough County will maintain the posture established from 2000 to 2010 and grow at a faster rate than the state going forward, other considerations suggest that a more moderate growth rate is likely. One such factor is the extent to which the development of “residential estate” housing outside of the urban services boundary is cost‐feasible. If the higher land and horizontal infrastructure costs of this development form, compared with small‐lot single family detached development, cannot be sustained by the market, the demand for housing assumed by the BEBR Medium forecast may not be satisfied. With only the remaining land within the urban services boundary available for “greenfield” development, more development will be concentrated in the urban centers; however, as with the Southeast Florida case, this development will occur more slowly than the growth rates estimated in the BEBR Medium estimate. For this reason, an

0%

10%

20%

30%

40%

50%

60%

1970 1980 1990 2000 2010 2020 2030 2040Hillsborough Growth Rate Florida Growth Rate

Orange Dade, Broward, Palm Beach


average of the BEBR Low and BEBR Medium estimates was considered in two of the Alternative Scenarios.

Along similar lines, growth allocated in the Tampa Bay region by the BEBR Medium forecast may shift between counties in the region. The combined 2040 BEBR Medium forecast for Hillsborough, Pinellas, and Pasco counties is approximately 3,477,000 persons, reflecting a projected increase of approximately 866,500. However, the growth forecasts likely to be promulgated as part of Pinellas and Pasco’s LRTPs are more aggressive than the BEBR Medium forecasts for those counties and combined account for 656,000 new persons. If growth in these two counties is balanced between the BEBR High that these counties are forecasting with their default (BEBR Medium forecasts), then the remainder of the BEBR Medium forecast for the region again suggests that an average between BEBR Low and BEBR Medium may be a reasonable alternative for Hillsborough County.

From the perspective of employment growth, Hillsborough County is expected to continue as the principal employment center in the region and, as the county grows in population, it is normal for the ratio of jobs to population to increase somewhat. However, as the Pasco County SR 54 Corridor and Pinellas County Gandy Gateway areas become more competitive, a greater share of the region’s jobs may accrue to these counties—especially without action to facilitate the development of additional employment centers within Hillsborough County.

These assumptions have led to different “control totals” as the basis for the three Alternative Scenarios and the Preferred Scenario discussed below. Additional information regarding the individual forecasts is

provided in Chapter 3 of this report.

1.8 Summary of the Scenarios

Each of the scenarios describes a possible future for Hillsborugh County. Different areas of emphasis were identified for each of the first three which led to a Preferred Scenario. Table 3 compares the employment and population growth for each vision as well as the Preferred Hybrid.

Table 3: Summary of Scenarios

Scenario 2010 2040 Growth

Suburban Dream Population 1,229,226 1,823,200 593,974

Employment 711,400 1,094,138 382,738

Bustling Metro Population 1,229,226 1,631,750 402,524

Employment 711,400 1,094,138 382,738

New Corporate Centers Population 1,229,226 1,631,750 402,524

Employment 711,400 1,145,195 433,795

Preferred Hybrid

Scenario

Population 1,229,226 1,815,964 586,738

Employment 711,400 1,112,059 400,659


2.0 Methodology

2.1 Methodology Overview

This section describes the methodology of the Land Use Allocation tool and how population and employment were allocated when special circumstances did not require manual intervention. This methodology applies to all four scenarios. Any differences in application of the methodology will be noted in the appropriate section.

Forecast Traffic Analysis Zones (TAZ)

This methodology was used to develop population and employment forecasts at the Traffic Analysis Zone (TAZ) level the years 2015, 2020, 2025, 2030, 2035, and 2040. TAZs are a basic geographic unit for studying demographic and land use data within a study area. The employment forecast included the three broad groupings of employment categories: Industrial, Commercial, and Service. Once the forecasts of the three standard employment categories are complete, this information will be included in the TBRPM travel demand model to predict travel patterns. FDOT is currently developing a more refined Activity Based Model (ABM) that will predict when specific travel occurs for individuals to activities such as work, shopping, leisure, etc. and allows for trip chaining travel patterns in which travel to multiple locations are linked together into one “trip” to be forecasted. The three employment categories will be expanded into nine to be used for modeling through the ABM.

Control totals of countywide employment by category were developed from the methodology and results described in the previous section of this report. As previously described, the base of the population and employment data forecasts was a 2010 population and employment data file developed by FDOT. Population and employment growth was then allocated to the TAZ level. Each TAZ has an ability to accommodate growth based on its future land use and ability to accommodate or attract development. This methodology is described in the following sections.

The 2010 TAZ zonal structure is illustrated in Map 1.


Map 1: Hillsborough County Traffic Analysis Zones

2.2 Greenfield (Vacant Developable Land) Forecasting Procedures

Using the approved control totals for population and employment, the initial allocation of population, dwelling units, and employment for the Industrial, Service and Commercial categories was made using the methodology discussed in the previous section. This included an allocation for approved development and an allocation to vacant lands.

Vacant Developable Lands Methodology

The first step in determining a TAZ’s growth potential was to quantify the amount of vacant developable acres by Future Land Use category. This was done using information from the Hillsborough County Property Appraiser's files. Land was determined to be vacant by using the Department of Revenue (DOR) code. In addition, single residential parcels greater than five acres were also considered as vacant due to the potential for being subdivided into additional residential parcels for development in the


future. When this occurred, the parcel was treated as partially vacant and available for development even though it had a structure on it.

Once the vacant land by TAZ was determined, the following adjustments were made to calculate the total developable land by Future Land Use Category:

Roadway right‐of‐way acreage was removed,

Government‐owned properties were removed,

Conservation and environmentally‐sensitive areas were removed; and

Wetlands were removed based on the National Wetlands Inventory maintained by the U.S. Fish & Wildlife Service.

The adjustments resulted in the vacant developable acres by Future Land Use category by TAZ. Appendix A contains a listing of the vacant developable acres by TAZ and Future Land Use Category.

Estimated land‐use densities and multiplier factors were applied to unoccupied developable land based on what is expected to reasonably occur. The factors were applied because many land use categories do not develop at their maximum allowable levels. For example, if a specific TAZ has 10 acres of unoccupied developable land designated for residential uses at an approved density of 2 dwelling units per acre and a multiplier factor of 80%, the maximum allowable number of new dwelling units for this TAZ is 16 dwelling units. Employment intensities were applied to developable acreage of land uses that generate employees (commercial, industrial, and service). If there was a mix of uses allowed in the Future Land Use category of a particular parcel, assumptions were made related to the make‐up of allowed land uses. The percentages differ based on the different land use categories that allow a mix of uses. From this information, allowable employee growth was estimated.

Land use densities were obtained from the Countywide Future Land Use Plan and Hillsborough County and Jurisdictional staff, as well as from the relationship of general land use densities provided in the Institute of Transportation Engineers (ITE) Trip Generation Manual (7th Edition). These densities and intensities are illustrated in Appendix A. The land use densities contained in the Countywide Future Land Use Plan were adjusted to reflect existing built‐out densities within Hillsborough County. Reduction factors were applied to reflect more reasonable densities, as frequently parcels are not built out to the maximum allowable densities. The maximum development for each TAZ was estimated by adding the allowable growth to the existing land use components (from 2010 county population, dwelling units, and employment categories). The maximum development was used to determine if the allocated growth was physically possible within the TAZ. If the growth was not possible, the model reallocated it to other TAZs.

Population and Employment Allocation Methodology

The allocation methodology for population and employment to vacant developable lands was accomplished using a multi‐step process that culminated in the allocation of growth based on the results of a gravity model. The process used to complete the allocation to vacant developable land is illustrated in Figure 3. The gravity model distributes growth based on the “mass” (or attractiveness) of a TAZ multiplied by the “mass” of an activity centroid divided by the square of the distance between the two. The results of the TAZ distribution were reviewed in several meetings with staff from the Hillsborough MPO, Hillsborough County, and staff from the local municipalities. Where appropriate, adjustments were made to individual TAZs based on the feedback received from staff.


Figure 3: Land Use Allocation Process

Identification of Planning Areas and Centroids The County is delineated into 25 Planning Areas developed by Hillsborough County staff. Planning Areas represent a set of TAZs that have been grouped together based on a number of factors:

Hillsborough County Planning Areas

City boundaries

City of Tampa Impact Fee Districts

These Planning Areas are illustrated in Map 2.


Map 2: Hillsborough County Planning Areas

Activity centroids were developed for each Planning Area for dwelling units and for industrial, commercial, and service employment. The activity centroids were found by weighting the geographic center of each TAZ by these land use components (dwelling units and industrial, commercial, and service employment) within the Planning Area for the year 2010. Stated another way, each TAZ has its own weighted centroid for each category. Centroids were calculated for each Planning Area based on the location of the existing units, which relates to population, as well as for each of the three employment categories based on the weighted centroids for each TAZ. The weighted geographic centers of each TAZ were then combined to find the center of mass for each Planning Area for population and the individual employment categories.

Thus, the centroid of the Planning Area does not represent the geographical center of the area, but, rather, a more realistic center based on the existing concentration of each land use component. Generally, these centroids represent locations of existing urbanized development or locations that will


likely become more urbanized in the future. Due to the concentric allocation procedure, it was not necessary to redefine regions or centroids for each planning year of the socioeconomic data sets. The allocation methodology simulates compact growth patterns from the centroid of the Planning Area outward.

Calculation of Attractiveness Index

As mentioned previously, the land use allocation process was based on a gravity model concept. An “attractiveness” index was found for each TAZ and divided by the sum of all the Attractiveness Indexes for each TAZ. This ratio was then multiplied by the growth increment for the specific year to determine the quantity of growth to allocate to each TAZ. If the sum of existing development plus the allocated growth exceeded the maximum development in the TAZ, then the model reallocated the growth to other TAZs. The maximum allowable development in a TAZ can be exceeded by applying a manual adjustment within the spreadsheet. The Attractiveness Index is described in further detail in the Appendix.

Permanent and Household Population

For the purposes of this analysis only the permanent population—residents living in the region for more than six months per year—was forecasted. Permanent population includes Household population and Group Quarters population. The U.S. Census Bureau defines Household population as “all the people who occupy a housing unit as their usual place of residence.” A housing unit, according to the U.S. Census Bureau is:

…a house, an apartment, a mobile home or trailer, a group of rooms, or a single room occupied as separate living quarters, or if vacant, intended for occupancy as separate living quarters. Separate living quarters are those in which the occupants live separately from any other individuals in the building and which have direct access from outside the building or through a common hall....

The U.S. Census Bureau also describes:

…all people not living in households as living in group quarters. There are two types of group quarters: institutional (for example, correctional facilities, nursing homes, and mental hospitals) and non‐institutional (for example, college dormitories, military barracks, group homes, missions, and shelters).

Attention is directed to the fact that seasonal population is included in the travel demand model through adjustments to the dwelling unit vacancy rates.

Allocation of Population and Employment to Traffic Analysis Zones

Based on the control totals and maximum allowable development for each TAZ, dwelling units and employment were allocated to each TAZ. The allocation was based on an iterative process that uses the attractiveness index in combination with how close the TAZ is to the Planning Area’s centroid. This process simulates compact urban development by first allocating growth to, or filling, TAZs closest to each Planning Area’s centroid.

Manual adjustments or overrides to the allocation process were then made, as necessary, to reflect projected growth in areas approved for large‐scale developments such as DRIs and Master Planned Unit Developments (MPUD). The resulting allocations were subsequently converted into socioeconomic data sets.

Staff from the Planning Commission, Hillsborough County, and from the local municipalities then reviewed the initial projections. This was accomplished with interactive working sessions using a series


of maps illustrating the increment of growth in dwelling units, and service, commercial, and industrial employment for each planning year horizon.

2.3 Redevelopment Forecasting Procedures

Growth in commercial and service employment categories was allocated to vacant developable areas as well as areas with redevelopment potential. Redevelopment is considered a change in property use that results in a changed land use type (residential to commercial employment for example) or a more intensive existing land uses.

The redevelopment allocation methodology is a multi‐step allocation procedure based on data available from the Hillsborough County MPO and the Hillsborough County Property Appraiser. The redevelopment methodology starts with a data file containing records for each parcel in Hillsborough County. These files were modified to identify land use types and TAZs. A query was tabulated to remove all vacant lands from the file since allocations of employment growth to vacant developable lands were completed using a separate methodology. The remaining records included only developed parcels.

The RPI is an index score value that weights criteria related to the 1) age of structures, 2) the relationship between the value of structures and the value of the property, and 3) access to major transportation facilities and services.

Figure 4: Redevelopment Propensity Index Criteria


2.4 Center Forecasting Approach

The center forecasting approach was utilized to reflect policy and/or desired development objectives. In both cases, reflecting targeted growth within identified infill areas or new employment centers is the goal of this approach. Based on the review of plans and guidance from MPO staff, employment and population were allocated to the identified areas reflecting the emphasis on residential development for the Bustling Metro scenario and job growth in the New Corporate Centers scenario. The preferred Hybrid Scenario includes both a residential center and an employment center approach. The incorporation of this approach is outlined below for each of the three scenarios mentioned.

Bustling Metro

The Bustling Metro alternative future was developed to reflect a vision of Hillsborough County that integrates future development with a multimodal transportation network. A large portion of the development was directed towards station areas identified by MPO staff. Identification of these station areas is based on previous studies and the amount of development allocated to each was coordinated with the future land use plans.

New Corporate Centers

The New Corporate Centers alternative future was developed to analyze a future reflecting an emphasis on economic development and job creation. To assist in this analysis, jobs were added to areas of economic emphasis that had been previously identified by studies or by MPO staff. Identified target industries were also reflected in the types of jobs allocated within the industrial, service and commercial categories.

Preferred Hybrid Scenario

The Hybrid scenario was developed 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.

2.5 School Enrollment

The distribution of school enrollment in all scenarios was accomplished manually. The base year data for the population and school enrollment (private schools, public schools, and community colleges) was the 2010 Hillsborough County school enrollment file provided by FDOT. School enrollment was determined as a percent of total population based on the base year data and was “grown” at the same level as the population.

2.6 Hotel/Motel Units

The distribution of hotel units was accomplished manually. The base year data for the hotel/motel units was a 2010 Hillsborough County hotel/motel units location file provided by FDOT. The units were “grown” according to the increase in population. The service employment control total includes future school and hotel employees.

2.7 Review and Adjustment Process

The socioeconomic data development process was supported by a series of interactive review workshops and meetings conducted by the consultant with staff from the Planning Commission, Hillsborough County, and local municipalities in the county. During these workshops, control totals and zone by zone data forecasts were reviewed. Adjustments, such as revising employment numbers in TAZs


that are areas of economic emphasis or reducing the percentage of industrial jobs forecast for 2040, were made to the forecasts to better define the scenario and develop the final preferred hybrid scenario.

2.8 Conversion of Forecast Data to Travel Demand Model Inputs

Input into the travel demand model requires the conversion from three employment categories to five as shown in Table 4. Both commercial and service employment categories are broken down into regional and local segments; local service includes school employment. This categorization is done because the trips generated by each type of employment have certain similar characteristics ‐ shorter or longer distances for example. Zones with higher growth tend to be more regional in nature meaning the employees may travel farther to and from work.

Table 4: Conversion from 3 to 5 Employment Categories

3 Categories 5 Categories

Industrial No split

Commercial Regional Commercial Local Commercial

Service Regional Service Local Service


3.0 Scenario Forecasts

3.1 Scenario Overview

As described earlier in this report, the Hillsborough County MPO, along with the consultant team, developed three different visions of how the county could develop. These alternative futures reflect three different ways that population, employment, and transportation related improvements could occur by the year 2040.

Suburban Dream (A) is the trend or “business as usual” scenario and forecasts growth based on current land use policy. Residential growth continues to be mostly single‐family suburban development on formerly agricultural land. Infrastructure investment is focused primarily on new roadways, roadway widening, and congestion management. The Urban Service Boundary and, along with it, water and sewer systems may have to be expanded to accommodate the growth. The automobile is the primary mode of transportation.

Bustling Metro Focused around Transit (B) assumes that travel patterns will change with the development of a more robust transportation system that will encourage redevelopment of land closer to urban centers. The Urban Service Area boundary is maintained as new population is accommodated near activity centers and transportation hubs. In addition to roadway investment, the County invests in additional bus and rail transit options. Jobs are added to existing employment centers, and neighborhoods are located closer to major destinations, encouraging the use of all transportation modes and shorter commute distances and time.

New Corporate Centers (C) reflects an approach more oriented to developing concentrated areas of employment around the county. While primarily within the Urban Service Area boundary, employment centers were identified along I‐4 and in Plant City. These new economic areas allow for diversity in employment, targeting sectors such as biotechnology and clean manufacturing. Population growth would be located mostly with the Urban Service Area boundary and near these centers and would be accommodated in a more urban growth pattern with some traditional suburban growth.

3.2 Suburban Dream

This alternative future assumes a development pattern similar to the last 25 years, with an emphasis on accommodating growth in new suburban‐style communities built on previously undeveloped land. Redevelopment and rehabilitation of existing buildings, as well as urban infill, would continue to occur in limited amounts in Tampa and in surrounding older communities.

Figure 5: Scenario comparison table


Methodology and Assumptions

This development scenario forecasts population and employment through 2040 as if no major changes to land use policy and development regulations are implemented, other than expanding the Urban Services Area. This forecast was developed using the BEBR medium population projection, and a job to population ratio that recovers from the recession and then stabilizes. Forecasted employment for this scenario also considers a regional balance in future jobs that considers expanded job centers in Pasco and Pinellas Counties.

Table 5 summarizes the population and employment projections for this scenario. The employment/population ratio is assumed to increase in the first half of the study period before leveling out in the latter half.

Table 5: Suburban Dream: Countywide Population and Employment Totals

2010 2040 Growth

Household population 1,207,161 1,790,382 583,221

Group quarters population 22,065 32,818 10,753

Total population 1,229,226 1,823,200 593,974

Total employees 711,400 1,094,138 382,738

Employment/population ratio .59 .61 Source: Florida Population Studies Volume 46, Bulletin 165, March 2013.

These control totals are based on a ratio to total employment and follow statewide and national trends. According to these trends, more employment is expected to occur in the Service sector, with a reduction in the Industrial sector as summarized in Table 6 below. Industrial employment (manufacturing, warehousing, etc.) is assumed to decrease as a percentage of total employment, but will still grow by more than 62,000 employees by 2040 (approximately 20 percent of employment in 2040). Commercial employment (retail, restaurants, etc.) is also forecasted to decrease slightly as a percentage of total employment but will grow by nearly 65,000 employees (approximately 18 percent of employment in 2040). The Service employment sector (e.g., educational, medical, and professional services) is forecasted to increase as a percentage of total employment and will add more than 255,000 employees (approximately 62 percent of employment in 2040).

The forecasted population and employment for Hillsborough County from 2010 to 2040 represents a growth of 48 percent for population and almost 54 percent for employment.

The employment control totals were developed based on a total employee to population ratio and the assumption that unemployment will decrease and stabilize at five percent.

Table 6: Suburban Dream: Countywide Employment Control Totals by Employment Type

2010 2040 Growth

Total employees 711,400 1,094,138 382,738

Industrial employees 156,600 218,828 62,228

Commercial employees 132,000 196,945 64,945

Service employees 422,800 678,366 255,566

Industrial/total employee ratio 22% 20%

Commercial/total employee ratio 19% 18%

Service/total employee ratio 59% 62%

School enrollment was assumed to increase in proportion to the general population. It is forecast that school enrollment for the 2040 Hillsborough County kindergarten through 12th grade, including


enrollment for both public and private schools, will be 324,059, an increase of 48 percent. Higher education enrollment is forecasted for 2040 at 118,165 students, an increase of 39 percent.

Table 7 summarizes the school enrollment forecasts for the Suburban Dream alternative future.

Table 7: School Enrollment Totals

2010 2040 Growth

K‐12 enrollment 218,231 324,059 105,828

Higher education enrollment 84,863 118,165 33,302

Total school enrollment 303,094 442,224 139,130

Table 8 summarizes the recommended hotel/motel unit forecasts for Hillsborough County. It is forecasted that there will 12,843 additional units in Hillsborough County by 2040, approximately a 56 percent increase.

Table 8: Hotel/Motel Unit Control Totals

2010 2040 Growth

Hotel/motel units 22,965 35,808 12,843

The following is a list of maps documenting the distribution of population and employment growth that are provided in Appendix D.

Figure 6, Suburban Dream Household Population Color

Figure 7, Suburban Dream Household Population Dot Density

Figure 8, Suburban Dream Total Employment Color

Figure 9, Suburban Dream Total Employment Dot Density

Figure 10, Suburban Dream Commercial Employment Color

Figure 11, Suburban Dream Commercial Employment Dot Density

Figure 12, Suburban Dream Industrial Employment Color

Figure 13, Suburban Dream Industrial Employment Dot Density

Figure 14, Suburban Dream Service Employment Color

Figure 15, Suburban Dream Service Employment Dot Density

3.3 Bustling Metro

In this alternative, redevelopment of parcels and infill in areas already developed would be the primary means of accommodating new jobs and population growth. This development pattern would be primarily urban, with an emphasis on activity centers, transit hubs, and development of a robust multi‐modal transportation system. The Urban Service Area might be expanded in select areas but for the most part would remain unchanged. Employment growth would be focused primarily in existing centers.


This scenario is exploring a future for Hillsborough County that is focused on filling in the development pattern, rather than expanding it. This “filling in” of remaining parcels and the redevelopment of land closer to the transit system is anticipated to help slow the demand to develop outward. Accordingly, in this analysis, approximately 40 percent of the growth anticipated over the next 30 years was added to areas that were identified as having fixed‐guide way transit potential, using the comprehensive plan policies for transit oriented development. This additional population would be accommodated primarily by redevelopment of land in the station areas. The redevelopment of land in TAZs identified for


potential transit accounts for approximately 20 percent of the allocation of population. In addition to a shift in allocation towards redevelopment and infill, this scenario was developed using an average of the BEBR low and BEBR medium population projection. Forecasted employment for this scenario is based on balancing the region’s future job growth as was done in the Suburban Dream Scenario.

Table 9 summarizes population and employment projections for this scenario.

Table 9: Bustling Metro: Population and Employment Forecast Projections

2010 2040 Growth




Total employees 711,400 1,094,138 382,738

Employment/ population ratio .59 .68 Source: Florida Population Studies Volume 46, Bulletin 165, March 2013.

These control totals are based on a ratio to total employment and follow statewide and national trends. Consistent with these trends, more employment is expected to occur in the Service sector with a reduction in the Industrial sector. Industrial employment (manufacturing, warehousing, etc.) is assumed to decrease as a percentage of total employment, but will still grow by more than 62,000 employees by 2040 or 40 percent. Commercial employment (retail, restaurants, etc.) is also forecasted to decrease slightly as a percentage of total employment but will also grow by more than 64.000 employees or 49 percent. The Service employment sector (e.g., educational, medical, and professional services) is forecasted to increase as a percentage of total employment and will add more than 255,000 employees, or 60 percent

Table 10 shows the breakdown of employment types.

Table 10: Bustling Metro Focused Around Transit: Employment Totals by Type

2010 2040 Growth

Total employees 711,400 1,094,138 382,738




Industrial/total employee ratio 22% 20%

Commercial/total employee ratio

19% 18%

Service/total employee ratio 59% 61%

School enrollment was assumed to increase in proportion to the general population. It is forecast that school enrollment for the 2040 Hillsborough County kindergarten through 12th grade, including enrollment for both public and private schools, will be 290,031, an increase of 33 percent. Higher education enrollment is forecasted for 2040 at 105,757 students, an increase of 25 percent.

The distribution of school enrollment was accomplished manually. The base year data for the population and school enrollment (private schools, public schools, and community colleges) was the 2010 Hillsborough County school enrollment file provided by FDOT. School enrollment was determined as a percent of total population based on the base year data and was “grown” at the same level as the population with an emphasis on the TAZs identified for population growth.

Table 11 summarizes the school enrollment forecasts for Hillsborough County.



2010 2040 Growth

K‐12 enrollment 218,231 290,031 71,800

Higher education enrollment

84,863 105,757 20,894


The distribution of hotel and motel units was accomplished manually. The base year data for the hotel/motel units was a 2010 Hillsborough County hotel/motel units location file provided by FDOT. The units were “grown” according to the increase in population and allocated to TAZs with an emphasis on the TAZs identified for population growth.

Table 12 summarizes the hotel/motel unit forecasts for Hillsborough County. It is forecasted that there will 9,083 additional units in Hillsborough County by 2040, a 39 percent increase.


2010 2040 Growth

Hotel/motel units 22,965 32,048 9,083

The following is a list of maps documenting the distribution of population and employment growth that are provided in Appendix E.

Figure 16, Bustling Metro Household Population Color

Figure 17, Bustling Metro Household Population Dot Density

Figure 18, Bustling Metro Total Employment Color

Figure 19, Bustling Metro Total Employment Dot Density

Figure 20, Bustling Metro Commercial Employment Color

Figure 21, Bustling Metro Commercial Employment Dot Density

Figure 22, Bustling Metro Industrial Employment Color

Figure 23, Bustling Metro Industrial Employment Dot Density

Figure 24, Bustling Metro Service Employment Color

Figure 25, Bustling Metro Service Employment Dot Density

3.4 New Corporate Centers

The third alternative focuses on economic development and job growth within the urban service boundary, but also explores expansion to areas outside the urban core along interstate highway corridors. These new economic areas allow for diversity in targeted employment sectors such as biotechnology, medicine, computer industries, and clean manufacturing. Population growth would be located mostly within the Urban Service Area boundary, with some growth occurring near these new economic centers.


This scenario emphasizes economic growth and therefore assumes that specific areas in Hillsborough County will be targeted to encourage the development and growth of employment centers. A certain amount of residential growth would occur near these centers, but for the most part residential growth was allocated using the Land use allocation process. The employment forecast for this scenario is based maintaining a job to population ratio that recovers from the recession and then stabilizes. This ratio was based on a population forecast equal to the BEBR medium population projection. For population,


however, an average of the BEBR low and BEBR medium population projection was projected. These two assumptions emphasized the preference of this scenario towards increased employment.


Table 13: New Corporate Centers: Population and Employment Forecast Projections

2010 2040 Growth




Total employees 711,400 1,145,195 433,795

Employment/ population ratio .59 .71 Source: Florida Population Studies Volume 46, Bulletin 165, March 2013.

Total employment was broken out into Industrial, Commercial, and Service employment categories.

These control totals are also based on a ratio to total employment and follow statewide and national trends. Consistent with these trends, more employment is expected to occur in the Service sector with a reduction in the Industrial sector. Industrial employment (manufacturing, warehousing, etc.) is assumed to decrease as a percentage of total employment, but will still grow by more than 72,000 employees by 2040 (equaling approximately 20% of employment in 2040). Commercial employment (retail, restaurants, etc.) is also forecasted to decrease slightly as a percentage of total employment but will also grow by more than 74,000 employees (equaling approximately 18% of employment in 2040). The Service employment sector (e.g., educational, medical, and professional services) is forecasted to increase as a percentage of total employment and will add more than 287,000 employees (equaling approximately 62% of employment in 2040).


Table 14: New Corporate Centers: Employment Totals by Type

2010 2040 Growth

Total employees 711,400 1,145,195 433,795


Commercial employees

132,000 206,135 74,135


Industrial/total employee ratio

22% 20%


19% 18%

Service/total employee ratio

59% 62%

Given the emphasis on jobs in this scenario, this forecast assumes that there will be an increase in the student population, particularly in higher education, to support the economy. School enrollment and the resulting service employment for the Jobs Centers Alternative Future were developed using a slightly higher growth rate to reflect the increased emphasis on education required to support the emphasis on jobs. It is forecast that school enrollment for the 2040 Hillsborough County kindergarten through 12th grade, including enrollment for both public and private schools, will be 290,031, an increase of 71,800 students. Higher education enrollment is forecasted for 2040 at approximately 152,066 students, an increase of 67,203 students.


Table 15 summarizes the recommended school enrollment forecasts for Hillsborough County.


2010 2040 Growth

K‐12 enrollment 218,231 290,031 71,800



The distribution of hotel and motel rooms was accomplished manually. The base year data for the hotel/motel units was a 2010 Hillsborough County hotel/motel units location file provided by FDOT. The units were “grown” according to the increase in population and allocated to TAZs with an emphasis on the TAZs identified for population growth.

Table 16 summarizes the recommended hotel/motel unit forecasts for Hillsborough County. It is forecasted that there will be 9,083 additional units in Hillsborough County by 2040, a 40 percent increase.


2010 2040 Growth

Hotel/Motel Units 22,965 32,048 9,083

The following is a list of maps documenting the distribution of population and employment growth that are provided in Appendix F.

Figure 26, New Corporate Centers Household Population Color

Figure 27, New Corporate Centers Household Population Dot Density

Figure 28, New Corporate Centers Total Employment Color

Figure 29, New Corporate Centers Total Employment Dot Density

Figure 30, New Corporate Centers Commercial Employment Color

Figure 31, New Corporate Centers Commercial Employment Dot Density

Figure 32, New Corporate Centers Industrial Employment Color

Figure 33, New Corporate Centers Industrial Employment Dot Density

Figure 34, New Corporate Centers Service Employment Color

Figure 35, New Corporate Centers Service Employment Dot Density

These data should also be reviewed periodically to ensure that ongoing growth is adequately provided for in the data files at the TAZ level. This is especially recommended for areas of the county that are experiencing significant changes in population or employment due to development approvals or changes in land use policies. These socioeconomic data are recommended for use in the Tampa Bay Regional Planning Model for the purposes of transportation planning. Application of these data for other uses should be carefully reviewed prior to use.

3.5 Preferred Hybrid Scenario

The final step in the development of the 2040 socio‐economic data was to develop a preferred hybrid scenario that incorporates the elements of each of the alternative scenarios based on feedback from the public engagement process – Imagine 2040.

This scenario emphasizes economic growth and residential development in targeted areas. Residential development is focused in areas identified as having the potential for future fixed‐guideway transit service. Jobs are located in specific areas in Hillsborough County that will be targeted to encourage the


development and growth of employment centers. Growth is targeted first within the existing Urban Service Area. Areas were identified as targets for potential future expansion in the event that growth does in fact follow the BEBR Medium forecast rate. Using the comprehensive plan policies for transit oriented development; population growth was directed towards redevelopment of land around future station areas. The TAZs identified as areas for transit oriented development are listed in Appendix G. The final strategy for accommodating growth in this scenario was to identify potential expansion areas of the Urban Services Area. TAZs considered for this expansion are listed in Appendix H.

This preferred scenario will guide the development of the 2040 Long Range Transportation Plan. In doing so, the preferred scenario will assist in streamlining the Plan update by placing emphasis on the types of transportation investments that are appropriate for Hillsborough County and the MPO’s LRTP update.


The control totals used for this scenario are generally based on the BEBR medium forecast, but take into consideration the growth vision resulting from the Imagine 2040 visioning and scenario exercise. Generally, an average 2.2 persons were assumed in each dwelling unit. In the areas identified as station areas, the persons per dwelling unit were lower at 1.8 persons per dwelling unit.


Table 17: Preferred Hybrid: Population and Employment Forecast Projections

2010 2040 Growth




Total employees 711,400 1,112,059 400,659

Employment/ population ratio .59 .62

Total employment was broken out into Industrial, Commercial, and Service employment categories.

These control totals are also based on a ratio to total employment and follow statewide and national trends. Consistent with these trends, more employment is expected to occur in the Service sector with a reduction in the Industrial sector. Industrial employment (manufacturing, warehousing, etc.) is assumed to decrease as a percentage of total employment, but will still grow by more than 85,000 employees by 2040 (equaling approximately 18% of total employment in 2040). Commercial employment (retail, restaurants, etc.) is also forecasted to decrease slightly as a percentage of total employment but will also grow by more than 69,000 employees (equaling approximately 14% of employment in 2040). The Service employment sector (e.g., educational, medical, and professional services) is forecasted to increase as a percentage of total employment and will add more than 245,000 employees (equaling approximately 52% of employment in 2040).



Table 18: Preferred Hybrid: Employment Totals by Type

2010 2040 Growth

Total employees 711,400 1,815,964 400,659




Industrial/total employee ratio

22% 22%


19% 18%

Service/total employee ratio

59% 60%

This forecast assumes a student population that reflects the population growth trends. It is forecast that school enrollment for the 2040 Hillsborough County kindergarten through 12th grade, including enrollment for both public and private schools, will be 218,231, an increase of more than 105,000 students. Higher education enrollment is forecasted for 2040 to be more than 118,000 students, an increase of 33,300 students or 46 percent.

Table 19 summarizes the recommended school enrollment forecasts for Hillsborough County.


2010 2040 Growth

K‐12 enrollment 218,231 324,055 105,824



The distribution of motel rooms was accomplished manually. The base year data for the hotel/motel units was a 2010 Hillsborough County hotel/motel units location file provided by FDOT. The units were “grown” according to the increase in population and allocated to TAZs with an emphasis on the TAZs identified for population growth.

Table 20 summarizes the hotel/motel unit forecasts for Hillsborough County. It is forecasted that there will be 12,844 additional units in Hillsborough County by 2040, a 56 percent increase.


2010 2040 Growth

Hotel/Motel Units 22,965 35,809 12,844

The following is a list of maps documenting the distribution of population and employment growth that are provided in Appendix I.

Figure 36, Preferred Hybrid Household Population Color

Figure 37, Preferred Hybrid Household Population Dot Density

Figure 38, Preferred Hybrid `Total Employment Color

Figure 39, Preferred Hybrid Total Employment Dot Density

Figure 40, Preferred Hybrid Commercial Employment Color

Figure 41, Preferred Hybrid Commercial Employment Dot Density

Figure 42, Preferred Hybrid Industrial Employment Color


Figure 43, Preferred Hybrid Industrial Employment Dot Density

Figure 44, Preferred Hybrid Service Employment Color

Figure 45, Preferred Hybrid Employment Dot Density

These data should also be reviewed periodically to ensure that ongoing growth is adequately provided for in the data files at the TAZ level. This is especially recommended for areas of the county where significant changes in population or employment are anticipated due to development approvals or changes in land use policies. These socioeconomic data are recommended for use in the Tampa Bay Regional Planning Model for the purposes of transportation planning. Application of these data for other uses should be carefully reviewed prior to use.

Next Steps

This preferred scenario will guide the development of the MPO’s assessment of needed future transportation system improvements and, ultimately, the development of the Cost Affordable Long Range Transportation Plan. In doing so, the preferred scenario will assist in streamlining the Plan update by placing emphasis on the types of transportation investments that are appropriate for Hillsborough County and the MPO’s LRTP update.


Appendix A – Units per Acre

Land Use Code

Adjusted Forecasted Units per Acre

DwellingUnits

Industrial Employees

CommercialEmployees

Service Employees

Hillsborough

County

Land Use

Agricultural/Mining‐1/20 AM‐1/20 0.0 0.0 0.0 0.0

Agricultural‐1/10 A‐1/10 0.1 1.1 0.0 0.1

Agricultural/Rural‐1/5 AR‐1/5 0.1 1.0 0.1 0.2

Agricultural Estate‐1/2.5 AE‐1/2.5 0.1 0.9 0.1 0.2

Planned Environmental Community‐1/2 PEC 1/2 0.1 0.0 1.7 2.9

Residential‐1 RES‐1 1.0 0.0 0.3 0.9


Residential Planned‐2 RP‐2 1.2 0.0 1.7 2.8

Wimauma Village Residential‐2 WVR‐2 1.1 0.2 1.2 2.0


Neighborhood MixedUse‐4(3) NMU‐4(3) 1.6 0.0 4.8 12.0


Suburban Mixed Use‐6 SMU‐6 2.9 2.5 3.6 6.0


Residential‐12 RES‐12 7.2 0.0 1.7 5.7

Residential‐16 RES‐16 9.6 0.0 1.7 5.7

Community Mixed Use‐12 CMU‐12 6.2 1.2 5.1 8.6

Residential‐20 RES‐20 12.0 0.0 1.7 8.6

Residential‐35 RES‐35 21.0 0.0 2.4 17.2

Office Commercial‐20 OC‐20 1.6 0.0 9.6 43.0

Urban Mixed Use‐20 UMU‐20 4.0 7.5 17.1 40.1

Regional Mixed Use‐35 RMU‐35 4.2 10.0 41.0 103.2

Citrus Park Village CPV 0.0 0.0 0.0 0.0

Research/Corporate Park RCP 0.0 14.9 1.4 68.8

Light Industrial LI 0.0 14.9 1.4 17.2

Light Industrial ‐ Planned LI‐P 0.0 14.9 1.7 17.2

Heavy Industrial HI 0.0 17.4 0.9 5.7

Energy Industrial Park EIP 0.0 12.2 0.3 19.7

Electrical Power Generating Facility EPGF 0.0 16.2 1.7 8.6

Natural Preservation N 0.0 0.0 0.0 0.0

Major Public/Quasi‐Public P 0.0 0.0 0.0 0.0

Tampa

Central Business District CBD 24.0 0.0 205.0 458.5

Residential‐83 Res‐83 49.8 0.0 4.4 7.5

Residential‐50 Res‐50 30.0 0.0 4.1 11.5

Residential‐35 Res‐35 21.0 0.0 4.1 6.9

Residential‐20 Res‐20 12.0 0.0 3.4 5.7

Residential‐10 Res‐10 5.6 0.0 1.2 2.0

Residential‐6 Res‐6 3.4 0.0 1.2 2.0



Rural Estate‐10 RE‐10 0.1 0.0 0.0 0.0

Regional Mixed Use RMU‐100 24.0 0.0 71.7 120.4


Community Commercial‐35 CC‐35 2.8 0.0 82.0 57.3




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 Compatibility M‐AP 0.0 44.8 10.2 51.6

Heavy Industrial HI 0.0 52.3 5.1 8.6

Light Industrial LI 0.0 44.8 5.1 25.8

Public/Semi‐Public P/SP 0.0 0.0 6.8 11.5

Recreation/Open Space R/OS 0.0 0.0 0.7 1.1

Environmentally Sensitive Areas ESA 0.0 0.0 0.0 0.0

Temple Terrace

Residential‐4 R‐4 2.2 0.0 0.0 0.0

Residential‐9 R‐9 5.0 0.0 0.9 1.4

Residential‐18 R‐18 10.8 0.0 2.4 4.0

Commercial C 0.4 0.0 20.5 14.3

Office/Institutional O‐I 0.4 0.0 6.8 34.4

Research Corporate Park RCP 0.0 2.5 6.8 68.8

Public/Semi‐Public P‐SP 0.0 0.0 6.8 11.5

Parks, Recreation and Open Space P‐R‐OS 0.0 0.0 6.8 11.5




Downtown Mixed Use‐25 DMU‐25 6.0 0.0 25.6 51.6

Plant City




Residential‐12 Res‐12 6.7 0.0 1.2 2.0

Residential‐20 Res‐20 12.0 0.0 2.4 4.0

Mixed Use ‐ Residential/Commercial MU‐R/C 2.9 0.0 9.6 8.0

Mixed Use ‐ Residential/Commercial/Ind MU‐R/C/I 2.9 3.5 7.2 8.0

Mixed Use ‐ Gateway MU‐G 2.6 2.6 4.8 16.0

Light Commercial/Office LC/O 0.0 0.0 7.2 16.0

Commercial C 0.6 0.0 14.3 10.0

Downtown Core DC 3.0 12.4 85.4 171.9

Industrial I 0.0 14.9 3.4 11.5

Public/Semi‐Public P/S‐P 0.0 0.0 2.4 4.0

Parks, Recreation and Open Space P/R/OS 0.0 0.0 1.7 2.9

Natural Preservation NP 0.0 0.0 0.0 0.0


Appendix B - Redevelopment Propensity Index (RPI) model

The RPI calculation is with a selection of all developed parcels that contain structures 25 years or older. This acreage was then split up into four quartiles based on their RPI score (highest to lowest). A cross‐tabulation analysis was performed to calculate the number of acres by TAZ by quartile RPI score for dwelling units, commercial employees, and service employees. 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 a quartile. For the highest quartile, 25% of the acres were considered as having a high propensity to redevelop. The second and third quartiles, 10% and 5% of the acres, respectively, were considered as having a high propensity to redevelop. No acres (0% weighting) from the lowest quartile range (lowest propensity to redevelop) were included as having propensity to redevelop. The total number of acres with a propensity to redevelop from each quartile was summed by TAZ. The number of dwelling units and employees to be allocated based on redevelopment potential were allocated to TAZ based on each TAZ’s percentage share of the total acres with a propensity to redevelop.


Appendix C - Calculation of Attractiveness Index Model The new growth was determined by dividing the total Attractiveness Index for a TAZ by the sum of the total Attractiveness Index for all TAZs in the county. This ratio developed for each TAZ was then multiplied by the growth increment (GIX) for the year (X) analyzed. The new growth formula is:

NGix = TAZ(AIij)x x GIx ∑TAZ(AJij)x

This calculation was repeated for each TAZ in the county. The new growth was added to the current development checking against the maximum development, or

(NGix + Current Developmentix) < Maximum Developmenti

where i represents each TAZ. After the new development was allocated and the maximum development was checked, a visual inspection of the allocation process was performed to determine if any spreadsheet errors had occurred. If the current development plus new growth that was allocated to the TAZ was greater than the maximum development, then the model reallocated the new growth to other TAZs.

The variables used in the model were:

i = TAZ number (1-780) j = Activity centroid (A-J) AIij = Attractiveness index between TAZi and centroidj F(AIij) = Function of attractiveness index (see below) AGi = Allowable growth for TAZi (units population) Dij = Straight line distance from geographical center of TAZi to centroidj Ffij = Friction factor based on the function e-kD, where D is the distance from the geographical center of the TAZ to the centroid and k is a constant NGi = New growth for TAZi TAZ(AI)I = Total attractiveness for TAZi (F(AIiA) + F(AJiB) + f(AIiC) + F(AIiD)... ∑TAZ(AI) = Sum of all total attractiveness indexes for each TAZ in the county GIx = Growth increment for year x

The attractiveness index (AIij) is a number that can start from zero and continue until it approaches infinity. An Attractiveness Index of zero has no “attractiveness.” As the index increases, the “attractiveness” of the TAZ increases as well. The function of the attractiveness index (F(AIij) is the question used to develop the attractiveness index. It is defined as follows:

F(AIij) = AGj X CUj X FFij Dij


The variable AGj is the first “mass” or maximum allowable growth in the gravity model calculations. The centroid units (CUj) is the second “mass” in the gravity model and is the total sum of all the land use components under analysis (employees by category) for the particular region. The above mass components were multiplied together, divided by the distance (Dij), and multiplied by the friction factor (FFij) to determine the attractiveness index.

For the function of Attractiveness Index (F(AIij)), i remains constant for each TAZ, whereas j flows through each activity centroid. Starting with TAZ Number 1, the function would be F(AI1A), F(AI1B), F(AI1D), F(AI1E), F(AI1F), F(AI2A), F(AI2B) ... until all TAZs were completed. Friction factors (FFij) further weight distances that are closer to an activity centroid. As the distance increases, its potential for development is less likely. Friction factors are determined by the function e‐kD, where D is the distance from geographical center of the TAZ to the centroid. When the constant “k” is small, the model places less emphasis on the proximity of the TAZ to the centroids. As “k” increases, the importance of the proximity of the TAZ to the centroid also increases.


Appendix D - 2040 Suburban Dream Population and Employment Forecast Results

Figure 6, Suburban Dream Household Population Color

Figure 7, Suburban Dream Household Population Dot Density

Figure 8, Suburban Dream Total Employment Color

Figure 9, Suburban Dream Total Employment Dot Density

Figure 10, Suburban Dream Commercial Employment Color

Figure 11, Suburban Dream Commercial Employment Dot Density

Figure 12, Suburban Dream Industrial Employment Color

Figure 13, Suburban Dream Industrial Employment Dot Density

Figure 14, Suburban Dream Service Employment Color

Figure 15, Suburban Dream Service Employment Dot Density


Appendix E - 2040 Bustling Metro Population and Employment Forecast Results

Figure 16, Bustling Metro Household Population Color

Figure 17, Bustling Metro Household Population Dot Density

Figure 18, Bustling Metro Total Employment Color

Figure 19, Bustling Metro Total Employment Dot Density

Figure 20, Bustling Metro Commercial Employment Color

Figure 21, Bustling Metro Commercial Employment Dot Density

Figure 22, Bustling Metro Industrial Employment Color

Figure 23, Bustling Metro Industrial Employment Dot Density

Figure 24, Bustling Metro Service Employment Color

Figure 25, Bustling Metro Service Employment Dot Density


Appendix F - 2040 New Corporate Centers Population and Employment Forecast Results

Figure 26, New Corporate Centers Household Population Color

Figure 27, New Corporate Centers Household Population Dot Density

Figure 28, New Corporate Centers Total Employment Color

Figure 29, New Corporate Centers Total Employment Dot Density

Figure 30, New Corporate Centers Commercial Employment Color

Figure 31, New Corporate Centers Commercial Employment Dot Density

Figure 32, New Corporate Centers Industrial Employment Color

Figure 33, New Corporate Centers Industrial Employment Dot Density

Figure 34, New Corporate Centers Service Employment Color

Figure 35, New Corporate Centers Service Employment Dot Density


Appendix G - The TAZs identified for transit stations

87 303 384 764

88 304 385 765

121 305 386 766

122 307 387 767

123 308 388 768

127 309 389 769

129 310 390 770

130 311 391 790

131 312 392 791

133 313 393 792

134 314 394 793

187 315 395 794

188 316 396

189 317 397

190 318 398

191 323 399

192 324 400

193 325 401

200 332 402

201 344 404

211 346 405

212 355 406

224 356 407

238 365 412

240 368 413

242 369 415

289 372 416

291 373 417

292 374 418

293 375 419

294 376 420

295 377 421

296 378 422

297 379 423

299 380 435

300 381 759

301 382 760

302 383 763


Appendix H - The TAZs identified as possible expansion areas for the urban service boundary

486

498

499

500

502

538

539

542

552

553

554

583

592

741

746

747

751


Appendix I - 2040 Preferred Hybrid: Population and Employment Forecast Results

Figure 36, Preferred Hybrid Household Population Color

Figure 37, Preferred Hybrid Household Population Dot Density

Figure 38, Preferred Hybrid Total Employment Color

Figure 39, Preferred Hybrid Total Employment Dot Density

Figure 40, Preferred Hybrid Commercial Employment Color

Figure 41, Preferred Hybrid Commercial Employment Dot Density

Figure 42, Preferred Hybrid Industrial Employment Color

Figure 43, Preferred Hybrid Industrial Employment Dot Density

Figure 44, Preferred Hybrid Service Employment Color

Figure 45, Preferred Hybrid Service Employment Dot Density

2040 Socioeconomic Data Forecasting and Scenario Planning ... · 7/9/2014 · for transportation...

Documents

Transcript of 2040 Socioeconomic Data Forecasting and Scenario Planning ... · 7/9/2014 · for transportation...