TABLE OF CONTENTS Page 1.0 Introduction 2.0 Data Review 3.0 Land Use Model Development 4.0 Allocation Results 5.0 Land Use Alternative

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3 TABLE OF CONTENTS Page 1.0 Introduction Data Review Land Use Model Development Base Year Data Highway Networks ULAM Default Values Population and Employment Control Totals Vacant Land Data Bay County Vacant Land Data Redevelopment Land Use Densities Approved Development Building Permits DRI Projects School Enrollment Data Other Trip Generation Data Allocation Results Land Use Allocation School Enrollment Projections Land Use Alternative Bay County Land Use Alternative Comparison Comparison of Historical Trend and Land Use Alternative Summary Appendix Appendix A School Enrollment Projections Appendix B Resolutions iii

4 FIGURES Page Figure 1 Bay County TAZs... 3 Figure 2 Comparison of Population Control Totals... 6 Figure 3 Vacant Parcels... 8 Figure 4 Wetlands... 9 Figure 5 Conservation Areas Figure 6 Urban Service Areas Figure 7 Building Permits Figure 8 DRI Projects Figure Population and Employment Growth Figure Population Growth Figure Employment Growth Figure Growth in Single Family DUs Figure Growth in Multi-Family DUs Figure Growth in Hotel Units Figure Growth in Industrial Employment Figure Growth in Commercial Employment Figure Growth in Service Employment Figure 18 Redevelopment Corridors Figure 19 Redevelopment Areas Figure 20 Land Use Alternative Population and Employment Growth Figure 21 Land Use Alternative Population Growth Figure 22 Land Use Alternative Employment Growth Figure 23 Population Growth by Traffic Zone Figure 24 Employment Growth by Traffic Zone Figure 25 Compare Land Use Alternatives Population Figure 26 Compare Land Use Alternatives Employment iv

5 TABLES Page Table 1 Base Year (2006) Zonal Data for Bay County... 6 Table 2 Base Year Socio-Economic Ratios... 7 Table 3 DRIs and Other Approved Development Projects Table 4 Bay County-Wide Growth Table 5 Land Use alternative Adjustments v

6 1.0 Introduction To ensure continued State and Federal funding for highway and other transportation projects, the Bay County Transportation Planning Organization (TPO) is required to prepare a Long Range Transportation Plan which identifies the system improvements needed to meet the existing and future transportation needs of the area. Federal requirements state that this Plan must consider the inter-relationship between land use and transportation. To address this requirement, the TPO is using the Urban Land Use Allocation Model (ULAM) to create a financially feasible long range transportation plan that addresses the transportation needs of Bay County with limited financial resources, it is critical that the recommendations on which transportations improvements should be funded and built be based upon the best available information. The ULAM model provides the TPO with a systematic approach that uses the most current land use information to generate the future year socio-economic data needed as input into the travel forecasting model. By improving the quality of the future year land use data, the TPO can help ensure that the travel projections used in the development of the long range plan will accurately reflect the future transportation needs of the area and will help the TPO to determine what are the most critical and cost effective improvements to address those needs. The purpose of the ULAM model is to provide an automated process to allocate future growth in the form of regional or county-wide population and employment control totals at the traffic analysis zone (TAZ) level producing data files for input into the TPOs Travel Forecasting Model. The ULAM process utilizes parcel level land use data and converts that data to traffic zone based information about vacant land, densities and approved development. The ULAM output files for each county were then merged into a regional file and then converted into a trip generation file need by the travel forecasting model to evaluate the transportation impacts. An important aspect of the project was the Land Use Subcommittee. The TPO Land Use Subcommittee, made up of local planning staffs, FDOT, University of West Florida Haas Center, St Joe Paper, Tyndall Air Force base, the Port of Panama City and West Florida Regional Planning Council Comprehensive Planning Staff was used to oversee the development of the inputs used in the land use model and to provide policy direction on model inputs and assumptions. The Land Use Subcommittee also reviewed the results from the model and provided recommendations on adjustments to the model to more accurately reflect local land use policies. This outreach and coordinating effort with various planning agencies and the private sector helped to ensure the best available data

7 was used in the development of these land use projections and for the long range transportation plan. It was determined by the Land Use Subcommittee that that the Historical Trend alternative be used for the development of the 2035 Long Range Transportation Plan. It was determined that the long range transportation plan and recommendations on what projects should be funded and built should be based on the most realistic future year development pattern which is reflected in the Historical Trend alternative. The following sections of this report show the land use allocation results for the Bay County TPO study area. Figure 1 shows the Bay County TPO study area. Bay County is sub-divided into 239 TAZs. 2

8 Figure 1 Bay County TAZs 3

9 2.0 Data Review The first step in this study was to collect and review all documents and data related to land development patterns and redevelopment activities and any previous work by both the Cities and the County regarding land planning. Available information was then evaluated to determine data needs, data collection procedures, necessary policy decisions and legal requirements regarding land use and growth assumptions. Recognizing that the Counties and Cities have conducted numerous land use studies and have an ongoing data collection process, every effort was made not to duplicate work already completed or currently underway. As a result, the following sub-tasks were undertaken: 1. Meetings were set up with representatives from the County and Cities to become familiar with all conditions affecting land development patterns. 2. Available documents from all agencies having an interest in the land use allocation process were collected and reviewed. The documents included local land use plans, development reviews, land development regulations and redevelopment plans. 3. An analysis of available information was undertaken to determine data needs, data collection procedures, necessary policy decisions and legal requirements regarding land use growth assumptions. 4

10 3.0 Land Use Model Development The Bay County ULAM model was developed and tested using input files that were created as follows: 3.1 Base Year Data The 2006 base year data was developed by the TPO staff for the validation of the travel demand model. The same 2006 data was converted into the ULAM zonal data (UZD) format and used as the base year data set upon which new development was allocated by the ULAM allocation model 3.2 Highway Networks The 2006 highway network was used to compute the highway travel times for the urban area. These travel times were used to compute the real estate market index based upon the market area which is defined as a 20 minute travel time contours around each zone and the direct drive time from each TAZ to a variety of major land use activity centers in the county. 3.3 ULAM Default Values All default values for the ULAM model were developed by TPS for the initial setup and testing of the allocation model. Additional refinement of these default values will be needed after further refinement of the vacant land and densities information. 3.4 Population and Employment Control Totals Future year regional and county-wide control totals must be provided for all future years. The population totals were taken from the University of Florida, Bureau of Economic and Business Research (BEBR) medium projections and used based upon the recommendations of the Land Use Subcommittee. Total employment and the mix of industrial, commercial and service employment were derived by using the base year population to employment ratios. Future year Hotel/Motel control totals were developed using population to hotel room ratios A comparison of the 2010 population and employment projections from BEBR and the projections used for the previous LRTP study was undertaken for Bay County. The results of that comparison are shown in Figure 2. Note that the BEBR 2006 data is not BEBR but rather the 2006 base year traffic zone data developed by the WFRPC staff. The 5

11 BEBR employment projections were computed using the BEBR population and the employment/population ratio taken from the base year traffic zone data to compute the employment. Figure 2 Comparison of Population Control Totals Population Projections 270, , ,000 Annual Increase1.4 % 210, , ZDATA Files Annual Increase 1.2 % BEBR Florida Population Studies, Volume 2, Bulletin 154, June , , Previous Study New BEBR Medium Table 1 Base Year (2006) Zonal Data for Bay County Single Family Population 111,037 Multi-Family Population 73,941 Total Population 184,978 Hotel/Motel Units 9,289 Industrial Employment 10,796 Commercial Employment 22,303 Service Employment 52,545 Total Employment 85,644 School Enrollment 68,527 In the previous study, total employment and the mix of industrial, commercial and service employment were derived by using the base year population to employment ratios. Future year Hotel/Motel control totals were developed using employment to hotel room ratios. Future year school enrollment control totals were computed using student to population ratios. Table 2 shows the updated ratios computed for Bay County using the 2006 zonal data. 6

12 Table 2 Base Year Socio-Economic Ratios Socio-Economic Ratios Computed Single Family/ Total Population Employment/Population Industrial/Total Employment 12.61% Commercial/Total Employment 26.04% Service/Total Employment 61.35% % School Enrollment/Population Hotel Units/Employment Vacant Land Data The vacant acreage information input into the ULAM model is developed using a series of GIS overlays that identify the "vacant buildable" acres available by traffic zone based on physical, environmental and institutional constraints. The first GIS coverage consists of a map of the vacant parcels within the County using the property appraiser s records to determine where and how much vacant land exists. The next step is a filtering process used to remove any vacant land that would not typically be developed. The purpose of this effort is to determine what the physical constraints to land development are, and to ensure new growth is not allocated to areas which have no vacant land available or are already built-out. Next, a map of the wetlands and environmentally sensitive areas is overlaid on top of the vacant land coverage to exclude those environmentally sensitive areas from development. A coverage showing zoning restrictions is overlaid and added to the vacant parcel file to incorporate the public sector institutional constraints, and to identify what type of land development the vacant land can be used for the given land use policies set forth by the county and cities. Vacant buildable acres by TAZ must then be converted from the land use categories used in the County's zoning code to the land use categories required for ULAM and the travel demand forecasting model. In allocating growth 25 to 30 years in the future it is not uncommon to have a situation where there is not enough vacant land to support the population or employment controls totals. In those situations agricultural is sometimes rezoned to increase the inventory of vacant buildable land to meet the control totals. The criteria used to identify agricultural land that may be rezoned and developed: vacant parcels inside the Urban Growth Boundary, within the water and sewer service area, non-agricultural adjoining land uses, within a major travel corridor, high growth area. 7

13 3.5.1 Bay County Vacant Land Data Figure 3 shows the vacant parcels used for the allocation process. Figure 4 through 6 shows the areas where growth was restricted to reflect the physical, environmental and institutional constraints identified by the County planning staff. Figure 3 Vacant Parcels 8

14 Figure 4 Wetlands 9

15 Figure 5 Conservation Areas 10

16 Figure 6 Urban Service Areas Adjustments to vacant land and densities were made for Tyndall Air Force base and the naval training center. All parcels in the noise impacted areas were identified. Since densities are applied in the ULAM model at the TAZ level the parcels affected were adjusted by factoring down the acreage for each parcel. Adjustments were only made to parcels with residential zoning not industrial or commercial properties. 11

17 3.6 Redevelopment Additional vacant land was added to the vacant land inventory as potential redevelopment in downtown Panama City, and at the old airport. 3.7 Land Use Densities Densities were developed by Transportation Planning Services, Inc. using the existing densities as computed from the county's parcel data files summarized by TAZ. One problem is using the FLUM land use classifications and the densities associated with the FLUM classification is that they represent maximum allowable densities and not the densities that the vacant parcels will actually be built at. This issue was addressed by using the existing densities identified in the county and cities zoning code. 3.8 Approved Development The TPO has created an on-going development tracking system to monitor different types of development approvals. This tracking system is setup in a number of spreadsheets that track building permits on a monthly basis for single family, multi-family, hotel/motel units and commercial development by square footage. This database also includes the traffic zone information for each of these approved developments. Additional information is also maintained by the TPO staff regarding the Developments of Regional Impact Building Permits The building permit data for 2007, 2008 and 2009 up to July was obtained and applied in the allocation model. Figures 7 shows the building permits for this period for the county DRI Projects Development of Regional Impact projects were identified and added to the building permit data. Vacant parcels associated with DRI projects were removed from the vacant land inventory to ensure that double counting did not occur. The DRI projects were assumed to be build out at 50 percent during the planning horizon. Figure 8 shows the DRI projects in the county. Table 3 lists the DRI project, the amount of development associated with each of those projects. 12

18 Figure 7 Building Permits 13

19 Figure 8 DRI Projects 14

20 Table 3 DRIs and Other Approved Development Projects TAZ 1345 Single Family Multi Family Hotel Industrial Commercial Service Industrial Park US , Navy Training Center , Panama City Beach , , Seahaven ,000 12,000 Lake Powell Golf 1269 Community , Pier Park ,073, Sheraton Hotel Bay Point Airport Redevelopment ,000 70, Huckelberry Creek 1, ,680 39, Bear Creek 1, , , Sector Plan Sector Plan ,275 1,125, Sector Plan , , Sector Plan , , Sector Plan , ,364 * Note: Single Family, Multi-Family and Hotel are in dwelling units. Industrial, Commercial and Service are shown in square feet. 3.9 School Enrollment Data The 2006 base year school enrollment data was obtained by the TPO staff. Addition GIS data was obtained for all school facilities in the area and the existing service area boundaries for the public schools. The school concurrency reports developed by each county school board was obtained and used to provide the enrollment projections for the public schools. For the non-public schools, the population growth rate identified in the control totals was used to project the future year enrollment for those facilities. Appendix A shows the enrollment projections developed using this process Other Trip Generation Data The trip generation data in the 2006 base year data includes: percent vacant, percent seasonal, percent auto ownership data for single and multi-family for each traffic zone. Data is also included for hotel occupancy rates in this file. These percentages were not changed and were carried over to the future year data files. 15

21 4.0 Allocation Results The historical trend from 2000 to 2009 provides a frame of reference as to what development trends have been occurring in recent years. This trend was identified using information contained in the property appraiser data for each county. Using the existing use code and effective year built information in the parcel data file for each county, the net increase in dwelling units and commercial square footage was identified for each traffic zone and used in establishing the historical trend regarding how growth has been geographically occurring in the study area. Using the parcel data and current zoning information for each of the counties, a land use alternative was developed to reflect the historical growth patterns of the area. This alternative included filtering out of wetlands, environmentally constrained areas and conservation areas. Since this alternative is based on the current zoning it does reflect the existing land use policies of the counties and cities in the study area. In allocating growth 25 to 30 years into the future, it is not uncommon to have a situation where there is not enough vacant to support the population or employment controls totals. In those situations agricultural is sometimes rezoned to increase the inventory of vacant buildable to meet the control total. The criteria used to identify agricultural land that may be rezoned and developed: vacant parcels inside the Urban Growth Boundary, within the water and sewer service area, non-agricultural adjoining land uses, within a major travel corridor, high growth area. The Future Land Use Map (FLUM) from the comprehensive plan for each county was used to identify parcels that are currently zoned agricultural or timberland but have a FLUM that would allow those parcels to be rezoned in the future. The ULAM model is an allocation model that allocates population and employment control totals from the county level down to the TAZ level. To do this the population and employment control totals must be provided as one of the inputs to the model. Future year regional and county-wide control totals must be provided for all future years. A set of preliminary model runs were made and results in the form of color maps showing the growth allocated by the model were generated. These maps were sent to the members of the Land Use Sub-Committee to review and provide comments about the allocation results. Some additional fine tuning of the model inputs were made based upon the comments received from the sub-committee. The model was run again, the result of this effort was a set of 2015, 2025 and 2035 zonal data files based upon the recommended Historical Trend alternative. The allocation model was run for each county. The allocation results were then merged into a regional file and then 16

22 converted into the ZDATA format for input into the Northwest Florida regional FSUTMS travel demand model Land Use Allocation The Table 4 on this page is a Summary Report that shows the county allocation results based upon the 2035 model run. Figure 9 is a dot density that shows the concentration of both population and employment growth in the county. Figures 10 and 11 show the county allocation results for total population and total employment by traffic zone. Each blue dot represents a population increase of 20 persons and each red dot represents an increase of 20 employees. Table 4 Bay County-Wide Growth Growth Single Family Population 111, ,819 22,782 Multi-Family Population 73,941 89,112 15,171 Total 184, ,931 37,953 Hotel Units 9,289 11,195 1,906 Industrial Employment 10,796 13,011 2,215 Commercial Employment 22,303 26,879 4,576 Service Employment 52,545 63,326 10,781 Total 85, ,216 17,572 Figures 12 through 17 are color TAZ maps showing the growth for each of the six ULAM land use classifications for the Historical Trend alternative. When compared to the historical growth patterns from the property appraiser data, the building permit data and the approved development data, the geographical distribution of future growth does appear to be very similar. Note no increase is shown in service employment for Tyndall Air Force based upon the information provided by the base staff. 17

23 Figure Population and Employment Growth 18

24 Figure Population Growth 19

25 Figure Employment Growth 20

26 Figure Growth in Single Family DUs 21

27 Figure Growth in Multi-Family DUs 22

28 Figure Growth in Hotel Units 23

29 Figure Growth in Industrial Employment 24

30 Figure Growth in Commercial Employment 25

31 Figure Growth in Service Employment 4.2 School Enrollment Projections Appendix A shows the school enrollment projections that were developed and used in the ZDATA files for Bay County. 26

32 5.0 Land Use Alternative A land use subcommittee, made up of local planning staff, Saint Joe Paper, Tyndall Air Force Base, the Port of Panama City and FDOT was established to oversee the development of the inputs used in the land use model to provide policy direction on decisions on default values. The subcommittee also reviewed the results from the model and provided recommendations on adjustments to the model to more accurately reflect local land use policies. Working with the land use sub-committee a second land use alternative was developed and tested for the TPO study area. This alternative was setup, tested and evaluated with the Urban Land Use Allocation Model developed for each county. As a part of this process the year 2035 population and employment projections were developed by traffic analysis zone using the ULAM. Both the historical trend and the land use alternative were developed and summarized by traffic zone in the standard ZDATA format for direct input into the FSUTMS travel forecasting model to evaluate the traffic impacts of each land use alternative. 5.1 Bay County Land Use Alternative A land use alternative was created to evaluate land use policies and impacts using existing land use studies. A number of corridor studies have been developed including the US 98 Corridor Study and SR 77 Corridor Study. Figure 18 shows the location of the corridor studies. Also a number of economic and redevelopment plans have been developed and those studies were also incorporated into the land use alternative and are shown in Figure 19. Finally, updated information for the new airport sector plan as also incorporated into the land use alternative. Table 5 shows the adjustments made to the land use data to reflect these various land use studies and to incorporate them into the ULAM model allocation run. The ULAM model was run for the land use alternative with the changes described previously. Figure 20 is a dot density map showing the allocation results in terms of the net increase from 2006 to 2035 for both population and employment. Figure 21 and 22 shows the same information for population and employment separately. Figures 23 and 24 show the same information as color traffic zone maps with numeric value of the net increase. 27

33 Figure 18 Redevelopment Corridors 28

34 Figure 19 Redevelopment Areas 29

35 Table 5 Land Use alternative Adjustments INFILDESC RESID_MIXNON_RESIDMIN_DU MIN_FARPCT_IND PCT_COMPCT_SER Downtown CRA/DIB Downtown Extension CRA Downtown North CRA Panama City Beach CRA Pier Park CRA St. Andrews CRA Old Airport Redevelopment Beach Condo Conversion US 98 Corridor - Downtown US 98 Corridor - Millville US 98 Corridor - St. Andrews SR 77 Infill Corridor Resid_Mix Percent residential development Non_Resid Percent non-residential or commercial development Min_DU Minimum dwelling units per acre Min_FAR Minimum floor area ratio for commercial development PCT_IND Percent of non-residential development industrial PCT_COM Percent of non-residential development commercial or retail PCT_SER Percent of non-residential development service or office 30

36 Figure 20 Land Use Alternative Population and Employment Growth 31

37 Figure 21 Land Use Alternative Population Growth 32

38 Figure 22 Land Use Alternative Employment Growth 33

39 Figure 23 Population Growth by Traffic Zone 34

40 Figure 24 Employment Growth by Traffic Zone 35

41 6.0 Comparison 6.1 Comparison of Historical Trend and Land Use Alternative Figure 25 on the following page shows the redistribution of the population growth with the land use alternative as opposed to the historical trend alternative. Figure 26 shows the redistribution of the employment growth under the same conditions. The largest impact is the redistribution of growth to the sector plan area. In the historical trend alternative the sector plan area which contains a considerable amount of vacant buildable land was assumed to be built at the densities based upon the current zoning which is in most cases 1 unit per 20 acres or in some cases 1 unit per 5 acres. In the land use alternative, all this vacant land is assumed to be rezoned to a density of 5 units per acre. The net result of this rezoning is a major redistribution of growth to the sector plan area as shown in the maps. It is not clear if the sector plan will in fact buildout by 2035 or if the current real estate market will support the higher densities and intensity of use described in the sector plan. Likewise, gives the economic conditions and the impact on the real estate market it is not clear if the previous trend of condo conversion will continue in the future. 36

42 Figure 25 Compare Land Use Alternatives Population 37

43 Figure 26 Compare Land Use Alternatives Employment 38

44 7.0 Summary The TPO Land Use Subcommittee, made up of local planning staff, FDOT, University of West Florida Haas Center, St Joe Paper, Tyndall Air Force base, the Port of Panama City and West Florida Regional Planning Council Comprehensive Planning Staff was used to oversee the development of the land use model and to provide policy direction on the use of the model. The sub-committee also reviewed the results from the model and provided recommendations on adjustments to the model to more accurately reflect local land use policies. Every effort was made to generate the most accurate and realistic land use projections for the long range transportation plan update. The travel demand model is the primary tool used to evaluate the transportation needs and effectiveness of various transportation improvements, is only as reliable as the land use projections put into the model. The most current land use data and a proven methodology were used to project the growth in each traffic zone in each county. Those projections were reviewed by the planning professionals with the county as well as numerous other agencies to ensure the accuracy and reliability of those projections. It was determined that that the Historical Trend alternative summarized in this report be used for the development of the 2035 Long Range Transportation Plan. It was determined that the long range plan and recommendations on what projects should be funded and built should based on the most likely future year development pattern that is reflected in the Historical Trend alternative. Appendix B shows the Bay County TPO resolution adopting the historical trend alternative for the development of the LRTP. There were a number of data issues that were identified in the study related to the 2006 base year data that were carried over and impacted the future year zonal data. In the development of the population controls totals it was observed that the 2006 base year population was higher than the projected 2010 population from BEBR 2006 estimate. The updating of the base year population information should be more closely evaluated and compared with other data sources for the next update of the base year data. In the next update of the LRTP this data should reviewed more closely to eliminate these types of issues. For the next update a number of traffic zones should be split into smaller zones. In particular the TAZs within the sector plan area should be split. Also, the TAZs associated with the Huckelberry and Bear Creek developments should also be split into smaller zones. These splits should also conform to the new 2010 Census Blocks. 39

45 Appendix Appendix A School Enrollment Projections 40

46 A-1 School Enrollment Projections YR YR YR YR YR YR YR TAZ TYPE NAME Bay Haven Charter Academy Breakfast Point Academy Callaway Elementary School Cedar Grove Elementary School Deane Bozeman School Deer Point Elementary Hiland Park Elementary School Hutchison Beach Elementary Lucille Moore Elementary School Lynn Haven Elementary School M. Cherry Street Elementary School M.K. Lewis School Millville Elementary School Northside Elementary School Oakland Terrace Elementary School Oscar Patterson Elementary School Parker Elementary School Patronis Elementary School Southport Elementary School Springfield Elementary School St. Andrews Elementary School Tommy Smith Elementary School Tyndall Elementary School Waller Elementary School West Bay Elementary School Everitt Middle School Jinks Middle Schhol Merritt Brown Middle School Mowat Middle School Newpoint Bay Charter School Rosenwald Middle School Surfside Middle School A. Crawford Mosley High School A.D.Harris High School Bay High School Haney Technical High School J.R. Arnold High School New Horizons Alternative Learning Center Rutherford High School Shaw Educational Center Chautaugua Learn & Serve Charter School Covenant Christian School Eagle Nest Christian Academy Emerald Bay Academy Fellowship Christian Academy FIRST PRESBYTERIAN CHRISTIAN SCHOOL 41

47 GOOD SHEPHERD LUTHERAN SCHOOL Heritage Boys Military Academy Holy Nativity Episcopal School Holy Nativity Episcopal School Kaleidoscope School of Discovery Panama City Advanced School Panama City Christian School Panama City Marine Institute Panama City Renaissance School Panama City Seventh-Day Adventist School PRIMARY PREP SCHOOL St. Bernadette Catholic School St. John The Evangelist Catholic School THE ROCK SCHOOL White House Academy GULF COAST COMMUNITY COLLEGE FLORIDA STATE UNIVERSITY Troy University 42

48 Appendix B Resolutions B-1 TPO Resolution Approving Land Use Projections 43