PINELLAS COUNTY METROPOLITAN PLANNING ORGANIZATION FORECAST 2035 EMPLOYMENT SOCIOECONOMIC DATA FINAL REPORT

Transcription

1 PINELLAS COUNTY METROPOLITAN PLANNING ORGANIZATION FORECAST 2035 EMPLOYMENT SOCIOECONOMIC DATA FINAL REPORT PREPARED FOR: Pinellas County Metropolitan Planning Organization 600 Cleveland Street, Suite 750 Clearwater, FL Ph (727) , Fax (727) December 2008 Tindale-Oliver & Associates, Inc N. Ashley Drive, Suite 100 Tampa, FL Ph (813) , Fax (813)

2 PINELLAS COUNTY METROPOLITAN PLANNING ORGANIZATION FORECAST 2035 EMPLOYMENT SOCIOECONOMIC DATA FINAL REPORT Table of Contents Chapter 1: Introduction Chapter 2: Countywide Employment Control Totals Methodology Summary Allocation of Employment to Industrial, Commercial, and Service Categories Chapter 3: Forecast Traffic Analysis Zone Employment Data Allocation to Vacant Developable Lands Methodology Allocation to Lands Subject to Potential Redevelopment Methodology Initial Allocation Response to Final Initial Allocation from Governmental Agencies Forecast Employment Data Chapter 4: Conclusion Recommendations Appendix A: Estimation of Maximum Land Use Densities by Land Use (Units Per Acre)...A-1 Appendix B: Approved Development Assumptions...B-1 Appendix C: Summary of Outreach Meetings... C-1 Appendix D: Forecast Socioeconomic Data Results by TAZ (Employment)... D-1 Appendix E: Forecast Socioeconomic Data Results by TAZ (School Enrollment and Hotel/Motel Units)...E-1 Appendix F: Forecast Socioeconomic Data Results by Planning Sector (Employment, School Enrollment, and Hotel/Motel Units)...F-1 Appendix G: Forecast Socioeconomic Data Results by TAZ (Interim Year)... G-1 i Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\ \Pinellas SEDATA Report.doc

3 List of Figures Figure 1-1 Traffic Analysis Zones Figure 3-1: Employment Forecast Methodology Figure 3-2: Planning Areas Figure 3-3: Redevelopment Employment Forecast Methodology Figure 3-4: Industrial Employment Estimate, Forecast, and Growth Figure 3-5: Commercial Employment Estimate, Forecast, and Growth Figure 3-6: Service Employment Estimate, Forecast, and Growth Figure 3-7: Total Employment Estimate, Forecast, and Growth Figure 3-8: K-12 School Enrollment Estimate, Forecast, and Growth Figure 3-9: Higher Education Enrollment Estimate, Forecast, and Growth Figure 3-10: Total Hotel/Motel Unit Estimate, Forecast, and Growth List of Tables Table 2-1: Employment Forecast by Employee Type Table 2-2: Employment Forecast by Employee Type (Percent of Population) Table 2-3: Employment Forecast by Employee Type (Percent of Employees) Table 2-4: Employment Forecast Cumulative Growth from Table 2-5: Employment Forecast Growth since Previous Time Period Table 2-6: School Enrollment Forecast Table 2-7: Hotel/Motel Unit Forecast Table 3-1: Redevelopment Propensity Index ii Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\ \Pinellas SEDATA Report.doc

4 CHAPTER 1 INTRODUCTION Socioeconomic data such as population and employment information are a vital component of travel demand forecasting models used for transportation planning. The Pinellas County Metropolitan Planning Organization (MPO) participates in the development and maintenance of this information for the Tampa Bay Regional Planning Model, which is typically updated on a three- to five-year cycle, thus requiring an update to the input data including base year and forecast socioeconomic data. The socioeconomic data used in this model are referred to as ZDATA, since they are developed at the Traffic Analysis Zone (TAZ) level. A map illustrating the locations of Pinellas County s 741 Traffic Analysis Zones is provided in Figure 1-1. This technical memorandum summarizes the methodologies used in the development of the 2035 socioeconomic employment data and presents the resulting data that will be used to develop the 2035 Long Range Transportation Plans (LRTPs) for the Tampa Bay area including Pinellas County. These data were developed through a joint coordinated effort between Pinellas County and the Florida Department of Transportation (FDOT) with assistance from Tindale-Oliver & Associates, Inc. This technical memorandum summarizes the forecast of the standard five employment categories used in the Tampa Bay Regional Planning Model. These five employment categories are summarized below: Industrial Employment All full-time and regular part-time employees, and self-employed persons by job location (i.e., place of work), whose job is an industry classified in the North American Industry Classification System (NAICS) categories 11, 21, 23, 31-33, 42, and 48 (i.e., agriculture, forestry, fisheries, mining, contract construction, manufacturing, freight transportation & warehousing, and wholesale trade). Regional Commercial Employment All full-time and regular part-time employees and selfemployed persons by job location (i.e. place of work), whose job is an industry classified in the NAICS categories 411, 4412, , 448, 4511, 4521, 4529, , 4539, 454 (i.e., retail trade that typically attracts trips from a regional market like shopping malls). Local Commercial Employment All full-time and regular part-time employees and selfemployed persons by job location (i.e., place of work), whose job is an industry classified in the NAICS categories 4413, , 4512, 722 (i.e., retail trade that typifies more local, short-distance travel for goods.) Regional Service Employment All full-time and regular part-time employees and selfemployed persons by job location (i.e., place of work), whose job is an industry classified in the NAICS categories 481, 4881, 22, 811, , 32, 721, 511, 512, , , , 54, 55, 5611, 5612, , 71, 813, 99, and 5613 (i.e., transportation, communication and utilities; hotels; repair services; health, legal and social services; insurance and real estate services; tourism and recreational services; government service). 1-1 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

5 Local Service Employment All full-time and regular part-time employees and selfemployed persons by job location (i.e., place of work), whose job is an industry classified in the NAICS categories 491, 521, 522, 812, 814, 519, and 61 (i.e., veterinary and pet services, landscape and horticulture services, postal and banking services, selected personal services, and educational services). (Source: Florida Department of Transportation District 7, 06EmplmtCategories.xls, 5/24/2007) Employment was initially forecast using three general categories of industrial employment, commercial employment, and service employment. The forecasted commercial and service employment categories where then split by TAZ into their respective regional and local employment categories. School enrollment was forecast using two categories: kindergarten through 12th grade (k-12) enrollment and higher education enrollment, which includes adult education. Allocation of students to TAZs was based on where the most population growth was occurring between 2006 and Hotel/motel units were allocated manually based on service and population growth. For the purpose of the Tampa Bay Regional Planning Model (TBRPM), hotel/motel units are classified into three categories. The Tampa Bay Regional Planning Model Cube Voyager Conversion Technical Report No. 1 Validation Report, produced by Gannett Fleming in November 2007, offers the following descriptions for each category: Resort Hotels/Motels: These cater primarily to tourists and vacationers and are generally located near the beaches or major tourist attractions. The majority of guests at these hotels/motels stay for two or more nights. Business Hotels/Motels: These cater primarily to business travelers and convention delegates and are usually located near major business centers. The majority of guests at these hotels/motels stay for two or more nights. Economy Hotels/Motels: These cater primarily to through travelers looking for a place to spend the night or to persons on a more limited budget and are generally lower priced than resort and business categories of hotels/motels. While they may be located throughout the area, they are usually located along major travel routes and are often clustered at freeway interchanges. The majority of guests at these hotels and motels stay for only one night. As a result, these hotels and motels generally offer far fewer amenities than either resort or business hotels/motels. 1-2 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

6 Gulf of Mexico PASCO COUNTY HILLSBOROUGH COUNTY Tampa Bay Traffic Analysis Zones Figure 1-1 Tindale-Oliver & Associates Planning & Engineering Miles ± Traffic Analysis Zones 1-3

7 CHAPTER 2 COUNTYWIDE EMPLOYMENT CONTROL TOTALS Introduction The current economic climate of the nation, state, and region pose special circumstances for Pinellas County that must be considered when forecasting employment out to the year For instance, the population has decreased by nearly 10,000 persons between 2006 and 2008, and school enrollment also has decreased. Factors and assumptions influencing the control totals are provided below. The assumptions of this forecast include: The economic downturns will balance with the boom periods. Over the next 30 years, employment in Pinellas County will grow at a slightly faster rate than population. This is a common feature of highly urbanized counties that are surrounded by counties with growing populations. Because of redevelopment trends, industrial employment as a ratio of total employment will likely decrease by 2035, but the total number of industrial employees will increase. The ratio of commercial employment to total employment will increase slightly by 2035, and the service ratio will remain stable. Pinellas County is mostly built-out, meaning most new employees will be accommodated by redevelopment activity. A detailed description of the methodology used to calculate redevelopment propensity can be found in Chapter 3. It is forecasted that Pinellas County employment in 2035 will be roughly 671,000 employees. This implies gains of roughly 105,600 employees from 2006 to The methodology for developing the employment forecast is described in greater detail below. Methodology Summary Source Data The data for the analysis to develop countywide employment forecasts included the following sources: The University of Florida s Bureau of Economic and Business Research (BEBR) Florida Statistical Abstract (FSA), annual data from 2000 to This information was used to analyze population trends. Pinellas County population projections were used to forecast employment based on the relationship between population growth and employment growth. ZDATA files from which the employment forecasts were grown (Technical Memorandum, Base Year (2006) Employment Socioeconomic Data ). 2-1 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

8 Allocation of Employment to Industrial, Commercial, and Service Categories The TBRPM uses employment that is classified into five subsets: Industrial Regional Commercial Local Commercial Regional Service Local Service Allocations to these five employment categories were made using data from NAICS codes as well as the 2006 adjusted control total of the TBRPM 2006 Base Year. Forecasts for each type of employment were developed in five-year increments through the year Initial control totals of employment by type were adjusted to reflect the recommendation of local jurisdictions in specific locations. Table 2-1 summarizes the employment forecast by employee type. Again, the control totals for employment were calculated for the three standard employment categories of industrial, commercial, and service, then later split into five employment categories. Table 2-1: Pinellas Employment Forecast by Employee Type : Total Population 944, , , ,134 1,001,713 1,010,263 1,017,262 Total Employees 565, , , , , , ,000 Industrial Employees 115, , , , , , ,490 Commercial Employees 111, , , , , , ,910 Service Employees 339, , , , , , ,600 Table 2-2 summarizes the employment percent by employee type as a percent of population. This table indicates that service employment is projected to have the greatest percent increase when compared to population. Also, note that the overall ratio of employment to population is increasing just 1 percent over the next 30 years. Table 2-2: Employment Forecast by Employee Type (Percent of Population) Industrial Employment 12.2% 12.1% 12.1% 12.2% 12.3% 12.4% 12.5% Commercial Employment 11.8% 11.8% 12.2% 12.5% 13.0% 13.4% 13.9% Service Employment 35.9% 35.4% 35.8% 36.3% 38.0% 38.8% 39.6% Total Employment 59.9% 59.3% 60.1% 61.1% 63.3% 64.6% 66.0% Table 2-3 summarizes the employment forecast as a percentage by type of employee. This table shows that service employment is increasing its share of total employment. Table 2-3: Employment Forecast by Employee Type (Percent of Total Employees) Industrial Employees 20% 20% 20% 20% 19% 19% 19% Commercial Employees 20% 20% 20% 21% 20% 21% 21% Service Employees 60% 60% 60% 59% 60% 60% 60% 2-2 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

9 Table 2-4 summarizes cumulative total of employment by employment type over the next 30 years. Table 2-4: Cumulative Growth from 2006 : Total Population n/a 16,841 33,674 46,933 57,512 66,061 73,060 Total Employees n/a 4,252 22,322 40,391 68,858 87, ,600 Industrial Employees n/a 1,481 3,662 6,244 8,326 10,408 12,490 Commercial Employees n/a 1,618 7,436 12,954 18,472 23,991 29,510 Service Employees n/a 1,153 11,224 21,193 42,060 52,830 63,600 Table 2-5 summarizes the growth in employment by employment type for each five-year period beginning in 2006 and ending in the year Table 2-5: Growth for Each Five-Year Time Period : Total Population n/a 16,841 16,833 13,259 10,579 8,549 6,999 Total Employees n/a 4,252 18,070 18,069 28,467 18,371 18,371 Industrial Employees n/a 1,481 2,181 2,582 2,082 2,082 2,082 Commercial Employees n/a 1,618 5,818 5,518 5,518 5,519 5,519 Service Employees n/a 1,153 10,071 9,969 20,867 10,770 10,770 Allocation of School Enrollment and Hotel/Motel Units Table 2-6 presents the recommended school enrollment forecasts for Pinellas County. It is forecasted that Pinellas County 2035 kindergarten to 12th grade (K-12) enrollment will be approximately 140,382 students from a population of 1,017,262 persons. This implies an increase of approximately 9,917 students from 2006 to Higher education enrollment is forecast for 2035 at approximately 45,777; the resulting increase from 2006 to 2035 is approximately 2,987 students. Table 2-6: School Enrollment Forecast : K-12 Enrollment 130, , , , , , ,382 Higher Ed. Enrollment 42,790 43,247 44,004 44,601 45,077 45,462 45,777 Total School Enrollment 173, , , , , , ,159 Table 2-7 summarizes the recommended hotel/motel unit forecasts for Pinellas County. New hotel/motel units were allocated manually based on population growth and service sector employment growth. A total of approximately 3,900 hotel/motel units are forecasted for 2035 in Pinellas County. It is forecasted that from 2006 to 2035, 1,850 of these units will be business units and 2,052 will be economy units. There is no forecasted growth in resort units. Table 2-7: Hotel/Motel Unit Forecast : Business Units 2,796 2,796 2,796 3,396 4,246 4,246 4,646 Economy Units 6,962 6,962 6,962 7,412 7,412 8,112 9,014 Resort Units 12,879 12,879 12,879 12,879 12,879 12,879 12,879 Total Hotel/Motel Units 22,637 22,637 22,637 23,687 24,537 25,237 26, Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

10 CHAPTER 3 FORECAST TRAFFIC ANALYSIS ZONE EMPLOYMENT DATA This chapter describes the methodology used to develop the year 2025 and 2035 employment forecasts. The forecast included the three standard FSUTMS employment categories of industrial, commercial, and service employment. The three standard employment categories were later split into five employment categories of industrial, regional commercial, local commercial, regional service, and local service employment for use with the TBRPM. Countywide employment control totals were developed from the methodology and results described in Chapter 3 of this report. The base of the employment data forecasts was a 2006 employment data file provided by FDOT. Future employees were allocated to TAZs using two complementary methodologies. The first methodology allocated a majority of employment growth to TAZs based on the availability of vacant developable land and the relative attractiveness for development. The remainder of the employment growth was allocated to the TAZ level based on anticipated propensity to accommodate or attract redevelopment. These methodologies are described in the sections below. Allocation to Vacant Developable Lands Methodology The allocation methodology for 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 allocations to vacant developable land is illustrated in Figure 3-1. 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 in order to allocate expected growth. This allocation model was performed in a Microsoft Excel workbook. The results of the TAZ distribution were reviewed in several meetings with staff and the staffs of local governments with in the county. Where appropriate, adjustments were made to individual TAZs based on staff input. For this analysis, vacant developable acres by future land use were provided by Pinellas County staff for The vacant acreage totals by TAZ were multiplied by the land use densities in the Pinellas Countywide Plan Rules to calculate the maximum allocable employment per TAZ. Reduction factors were then applied to estimate the reasonable effective intensity that will actually be built. The land use densities are summarized in Appendix A. 3-1 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\ \Pinellas SEDATA Report.doc

11 Figure 3-1 Employment Forecast Methodology 3-2 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

12 The following describes the technical process the gravity model used for the distribution of growth to the Traffic Analysis Zones. Identification of Planning Areas and Centroids To support the development of the gravity model, it was necessary to develop planning areas and to calculate their activity centroids. These planning areas are geographical regions of the county that were developed based on similar densities or that represent common geographic regions (such as a city or major roadway corridor). For Pinellas County, 14 planning areas were developed. In general, the planning areas represent unique geographic concentrations of employment characteristics. The planning areas are illustrated in Figure 3-2 and were used by the allocation model for estimating the attraction of different sub-areas of the county. Activity centroids were developed for each planning area for industrial, commercial, and service employment. The activity centroids were found by weighting each geographical center of each TAZ by these land use components (industrial, commercial, and service employment) within the planning area for the year The weighted geographical centers of each TAZ were combined to find the center of mass for each planning area. Thus, the centroid of the planning area does not represent the geographical center of the planning 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 forecasts compact growth patterns from the center of the planning area then outward and thus discourages urban sprawl. Calculation of Attractiveness Index As mentioned previously, the Land Use Allocation Model was based on the 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 variables used in the model were: i j = TAZ number (1-741) = Activity Centroid (A-F) AI ij = Attractiveness index between TAZ i and centroid j F(AI ij ) = Function of Attractiveness Index (see below) AG i = Allowable Growth for TAZ i (units population) D ij = Straight line distance from geographical center of TAZ i to centroid j Ff ij = 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 NG i = New Growth for TAZ i 3-3 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

13 Gulf of Mexico PASCO COUNTY HILLSBOROUGH COUNTY Tampa Bay Traffic Analysis Zones & Planning Sectors Figure 3-2 Tindale-Oliver & Associates Planning & Engineering Miles ± TAZ's

14 TAZ(AI) I = Total Attractiveness for TAZ i (F(AI ia ) + F(AJ ib ) + f(ai ic ) + F(AI id )... TAZ(AI) = The sum of all total attractiveness indexes for each TAZ in the county GI x = Growth Increment for year x The attractiveness index (AJ ij ) is a number, which 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. When applying a gravity-based model as a general rule, TAZs or regions that are closer in proximity or have a greater quantity of existing development (mass) are more attractive. The function of the attractiveness index (F(AI ij ) is the question used to develop the attractiveness index. It is defined as follows: F(AI ij ) = AG j X CU j X FF ij D ij The variable AG j is the first mass or maximum allowable growth in the gravity model calculations. The centroid unit (CU j ) 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 (D ij ), and multiplied by the friction factor (FF ij ) to determine the attractiveness index. For the function of attractiveness index (F(AI ij )), i remains constant for each TAZ while j flows through each activity centroid. Starting with TAZ Number One, the function would be F(AI 1A ), F(AI 1B ), F(AI 1D ), F(AI 1E ), F(AI 1F ), F(AI 2A ), F(AI 2B )... until all TAZs were completed. Friction factors (FF ij ) 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. The constant k is based on the allocation preference and may be established by the local governing agency. When the constant k is small, the model placed 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. Distribution of Growth to TAZs 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: NG ix = TAZ(AI ij )x x GI x TAZ(AJ ij ) 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 (NG ix + Current Development ix ) < Maximum Development i 3-5 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

15 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. staff reviewed the initial projections for each planning year iteration of the model. This was accomplished with interactive review sessions using a series of maps illustrating the growth increment in population and industrial, commercial, and service employment for each planning year horizon. Adjustments to specific areas of the county were recommended by staff to more accurately reflect future year patterns. These adjustments were also made to include approved Developments of Regional Impact (DRIs) and other developments. Allocation of growth for each increment of time utilized the results of the development totals resulting from the preceding growth allocation iteration. This allowed manual data adjustments to maximum allowable development and manual attractiveness factors to be preserved throughout each analysis period. Forecasted Approved Development and Redevelopment Pinellas County is unique because it has very little vacant land available for brand new development. Most new employment growth will come from areas already reserved and approved for new development or the redevelopment and infill of existing developed areas. Approved developments included DRIs. The assumptions used for approved developments were developed with the assistance of and Planning staff, as well as the staff of local governments within the county. These assumptions can be found in Appendix B. In addition to allocations to vacant developable lands, commercial and service employment growth also was allocated based on the potential for redevelopment. This methodology was not applied to industrial employment since redevelopment of industrial land uses is not perceived as a significant factor. For the purposes of this analysis, redevelopment is considered a change in land use that results in changes in land use type (residential to commercial employment for example) or results in an intensification of existing land uses. The redevelopment allocation methodology is a multi-step allocation procedure based on data available from the and the Pinellas County Property Appraiser. This redevelopment methodology is illustrated in Figure 3-3. The redevelopment methodology starts with a data file containing records for each parcel in Pinellas 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 where completed using a separate methodology. The remaining records included only developed parcels. For each of these developed parcels, a Redevelopment Propensity Index (RPI) was calculated based on the criteria summarized in Table 3-1. The RPI is an index score value that weights criteria related to the age of structures, the relationship between the value of structures and the value of the property, and access to major transportation facilities. 3-6 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

16 Figure 3-3 Redevelopment Propensity Forecast Methodology Parcel Data File Remove Vacant Lands Developed Parcels Calculate RPI by Parcel Stratify By Existing Res/Non-Res Types Commercial Existing Non-Res. Commercial Existing Residential Service Existing Non-Res. Service Existing Residential SUM Acres by TAZ and Quantile SUM Acres by TAZ and Quantile SUM Acres by TAZ and Quantile SUM Acres by TAZ and Quantile Apply Quantile Weights Apply Quantile Weights Apply Quantile Weights Apply Quantile Weights Normalize TAZ Scores (Relative Propensity) and Allocate Redevelopment Following the calculation of the RPI for individual parcels, a series of queries was performed to separate the parcel records into four sub-categories (Commercial Existing Non Residential, Commercial Existing Residential, Service Existing Non Residential, and Service Existing Residential). This step was necessary because commercial and service employment was allocated separately and since redevelopment resulting in the removal of existing residential units would require redistribution of dwelling units and population to other TAZs. 3-7 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

17 Table 3-1 Redevelopment Propensity Index (RPI) RPI = aw[age of Structure] + ac[access] + av[(land Value/Structure Value] + [Redevelopment Area] where Values RPI = Redevelopment Propensity Index aw = Age of Structure Weight 20% ac = Access Weight 30% av = Value Weight 50% Age of Structure (Year 2035) Access Land Value/Structural Value Range Range Score Low High Score Value Score Low High (I) Interstate Interchange.5 mi (A) Non Interstate Arterial 0.25 mi (C) Collector 0.25 mi Redevelopment Area Score Value 5 In High Performing Redevelopment Area 2 In Low Performing Redevelopment Area 0 Not In Redevelopment Area Input Values Value Description Score 1950 Year Structure Built 4 I Access 4 $1,000,000 Land Value $500,000 Structure Value 3 High Redevelopment Area 5 16 <- SAMPLE TOTAL PARCEL RPI Quantile Percentages Quantile 1st Lowest 2nd Low 3rd High 4th Highest Percent of Acres 0.00% 5.00% 10.00% 25.00% For each of the four sub-categories, a cross tabulation analysis was performed to tabulate the number of acres by Traffic Analysis Zone by quantile RPI score. For each quantile range, a weighting factor was developed for use in calculating the percentage of the total acres that would be considered for redevelopment within a quantile. If a parcel was identified as being located in an officially designated redevelopment area, then that parcel was automatically added to the highest quantile. Thus, no acres from the lowest quantile range (lowest propensity to redevelop) were included as having propensity to redevelop, while 25 percent of the acres in the highest quantile were considered as having a propensity to redevelop. The total number of acres with a propensity to redevelop from each quantile was summed together by TAZ. The number of 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. This approach does not take into direct consideration the relative intensities or densities within a given TAZ for a given land use; however, the relationship between land value and structure value is partly a surrogate for the existing intensity of development. 3-8 Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

18 Initial Allocation Using the approved control totals for employment, the initial allocation of employment for the industrial, commercial, and service employment categories was made using the methodology discussed in the previous sections of this chapter. This included an allocation for approved development, allocation of employment to vacant lands, and allocation of employment to TAZs anticipated to have a propensity to attract redevelopment. In July 2008, workshops were held for the MPO, Planning staff, and the municipalities. The workshops focused on identifying areas having too much or too little growth being added and on receiving guidance from those most knowledgeable in various areas of the county. The objective was to ensure a consensus on when and where growth will occur in future years. These changes prompted a reallocation of employment, which resulted in the Final Allocation. The Final Initial Allocation was presented to the TCC Review Board in August Handouts included a table of the employment forecast for each type of employment and a set of maps illustrating the growth in employment between 2006 and A summary of all meetings that took place during the allocation process is located in Appendix C. Forecast Employment Data The forecasted industrial, commercial, and service employment by TAZ are summarized in Appendix D for the estimated 2006 and forecast 2035 years. Maps were produced illustrating the forecasted data, including permanent population which was provided by the Pinellas MPO. These maps (Figures 3-4 through 3-8) illustrate the 2006 base year, the 2035 forecast year and the difference between the base year and the forecast year. In Appendix G, the 2025 interim year employment data is summarized by TAZ. Allocation of School Enrollment The distribution of school enrollment was accomplished with guidance from the Pinellas School Board staff demographer. The future school enrollment was tabulated for each educational facility, not the student s residence. Forecasts of population growth, provided by the Pinellas MPO, were used as the primary input for forecasting school enrollment. This information was used to correlate the need for future school enrollment to the areas with the highest projected dwelling unit growth. The base year data for the school enrollment (private schools, public schools, and higher education) was the 2006 Pinellas County school enrollment file provided by FDOT. Existing 2006 school enrollment figures were received by the Pinellas County School Board. Per School Board staff direction, forecast enrollment for K-12 schools would experience very minimal growth. Three schools that would be closing for the school year were identified by the School Board. For the purposes of forecasting enrollment, Largo Central Elementary (TAZ 1269) and Riviera Middle (TAZ 1520) are assumed to reopen as new school sites by the year 2020, since these sites are already owned by the School Board. South Ward Elementary (TAZ 1214) also closed but, because the building is on the National Historic Register, it will not remain under School Board ownership and no students are forecasted to be enrolled at that location in Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\ \Pinellas SEDATA Report.doc

19 In general, school enrollment was determined as a percent of total population based on historic school enrollment data. Education facilities, at the TAZ level, were grown at the same level as the population in the immediate surrounding areas. Approved developments, including DRIs, were reviewed to determine if a new education facility is proposed as a part of the development. These new education facilities were added to the appropriate TAZ and the additional students, as indicated by the approved development, were included in the allocation. The estimated 2006 and forecasted school enrollment by Traffic Analysis Zone is summarized in Appendix E for Figures 3-9 and 3-10 illustrates the 2006 base year, the 2035 forecast year, and the difference between the base year and the forecast year for the total school enrollment. Allocation of Hotel and Motel Units Hotel and Motel Units are categorized into three categories for use in the Tampa Bay Regional Planning Model. The three categories include business, economy, and resort hotel and motels. The majority of existing hotel and motel units in Pinellas County are in the resort category. However, due to the limited availability of vacant land on the beaches for development, a majority of the growth is forecasted to occur in business and economy hotel and motel units. The distribution of hotel and motel units for each category was accomplished manually. The base of the hotel and motel units was the 2006 Pinellas County Hotel and Motel units location file provided by FDOT. Future growth of hotel and motel units was tied to growth in service employment and population. A review of approved developments, including DRIs, was completed to determine the likely locations of future hotel and motel units. The remainder of the hotel and motel units was then allocated to TAZs based on the location of future service employment, future land use patterns in the County, and input from the County staff. The estimated 2006 and forecasted hotel/motel units by TAZ are summarized in Appendix E for Map 3-11 illustrates the 2006 base year, the 2035 forecast year, and the difference between the base year and the forecast year for total hotel/motel units. Planning Sector Summary For this project, 14 planning sectors were identified. These planning sectors consist of similar areas generally defined by their jurisdictional boundaries. Planning sectors are used as another level of analysis in the planning process by Pinellas County. The forecasted industrial, commercial, service employment, school enrollment, and hotel/motel units by Planning Sector are summarized in Appendix F for the estimated 2006 and forecast 2035 years Forecast Employment Data December 2008 Technical Memorandum G:\ _Pinellas_Employment_Forecast\Docs\Reports\Final\Pinellas SEDATA Report.doc

20 PASCO COUNTY ± 2006 Industrial Employment 2035 Industrial Employment Industrial Employment Growth PASCO COUNTY PASCO COUNTY HILLSBOROUGH COUNTY HILLSBOROUGH COUNTY HILLSBOROUGH COUNTY Gulf of Mexico Gulf of Mexico Gulf of Mexico 2006 Industrial Employees Tampa Bay 2035 Industrial Employees Tampa Bay Additional Employees Tampa Bay , , ,001-1,500 1,001-1, , Miles 1, Pinellas County Employment Forecast Tindale-Oliver & Associates Planning & Engineering Total Industrial Employees 2006: 115, : 127,490 Growth: 12,490 Figure 3-4 Industrial Employment Forecast

21 PASCO COUNTY ± 2006 Commercial Employment 2035 Commercial Employment Commercial Employment Growth PASCO COUNTY PASCO COUNTY HILLSBOROUGH COUNTY HILLSBOROUGH COUNTY Gulf of Mexico Gulf of Mexico Gulf of Mexico 3-12 HILLSBOROUGH COUNTY 2006 Commercial Employees Tampa Bay 2035 Commercial Employees Tampa Bay Additional Employees Tampa Bay , , , Miles 1, Pinellas County Employment Forecast Tindale-Oliver & Associates Planning & Engineering Total Commercial Employees 2006: 111, : 140,910 Growth: 29,510 Figure 3-5 Commercial Employment Forecast

22 PASCO COUNTY ± 2006 Service Employment 2035 Service Employment Service Employment Growth PASCO COUNTY PASCO COUNTY HILLSBOROUGH COUNTY HILLSBOROUGH COUNTY Gulf of Mexico Gulf of Mexico Gulf of Mexico 3-13 HILLSBOROUGH COUNTY 2006 Service Employees Tampa Bay 2035 Service Employees Tampa Bay Additional Employees Tampa Bay , , ,001-1,500 1,001-1, ,501-2,000 1,501-2, , Miles 2, Pinellas County Employment Forecast Tindale-Oliver & Associates Planning & Engineering Total Service Employees 2006: 339, : 402,600 Growth: 63,600 Figure 3-6 Service Employment Forecast

23 PASCO COUNTY ± 2006 Total Employment 2035 Total Employment Total Employment Growth PASCO COUNTY PASCO COUNTY HILLSBOROUGH COUNTY HILLSBOROUGH COUNTY Gulf of Mexico Gulf of Mexico Gulf of Mexico 3-14 HILLSBOROUGH COUNTY 2006 Total Employees Tampa Bay 2035 Total Employees Tampa Bay Additional Employees Tampa Bay , , ,001-2,000 1,001-2, ,001-4,000 2,001-4, ,000 4, Miles 4, ,001 + Pinellas County Employment Forecast Tindale-Oliver & Associates Planning & Engineering Total Employees 2006: 565, : 671,000 Growth: 105,600 Figure 3-7 Total Employment Forecast

24 PASCO COUNTY ± 2006 K-12 Enrollment 2035 K-12 Enrollment Enrollment Growth PASCO COUNTY PASCO COUNTY HILLSBOROUGH COUNTY HILLSBOROUGH COUNTY Gulf of Mexico Gulf of Mexico Gulf of Mexico 3-15 HILLSBOROUGH COUNTY 2006 Number of Students Tampa Bay 2035 Number of Students Tampa Bay Additional Students Tampa Bay Miles Pinellas County Employment Forecast Tindale-Oliver & Associates Planning & Engineering Total K-12 Enrollment 2006: 130, : 140,382 Growth: 9,917 Figure 3-8 K-12 School Enrollment Forecast