These geospatial models were built using property parcel data from county property appraisers. These data include the property parcel boundaries and attributes that indicate the property use, the number of residential units, the year the residences were built, and the lot size. To this we added data from other sources that enabled us to project maximum development potential (or \"build-out\") at the parcel level, including:<\/p>
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-Population, average household size and average occupancy from the 1990, 2000, 2010 and 2020 decennial censuses<\/p>
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-Future land use classification and unit densities from local government future land use maps<\/p>
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-Surface water and wetlands<\/p>
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-Land uses where future development is restricted<\/p>
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-Planned development boundaries and approved residential units<\/p>
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-Political, natural or other boundaries needed for summarizing the results (cities, utility boundaries, etc.)<\/p>
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Using this \"Build-out Submodel\", we also estimated 2024 population. We applied average household size and occupancy metrics from the 2020 Census to the parcel-level residential unit estimates provided by the county property appraisers. We then added population in group quarters (e.g., nursing homes, college dormitories, prisons, etc.) and made minor adjustments to control the current population to 2024 BEBR county population estimates.<\/p>
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To project future growth, we began by calculating historic growth trends. These were based on tract-level data from the 1990, 2000, 2010, and 2020 censuses. These data were used to produce 11tract-level projections using five different demographic extrapolation methods over multiple base periods. The length of the base was adjusted to roughly match the length of the projection horizon, so for a 20-year horizon, 20 years of historical data were used to establish the growth trends. The number of trend calculations varied based on the length of the base period used, and the highest and lowest calculations were discarded to moderate the effects of extreme projections. The remaining projections were then averaged.<\/p>
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In addition to historic growth trends, spatial features can influence future growth. For that reason, we determined the historical relationship between residential development over the past 20 years and its proximity to the following features:<\/p>
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-Roads and interchanges prioritized by level of use (with each road type modeled separately)<\/p>
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-Existing residential development<\/p>
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-Existing commercial development (based on parcels with specific commercial land use codes deemed attractors to residential growth)<\/p>
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-Coastal and inland waters<\/p>
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-Planned developments<\/p>
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The results of multiple logistic regressions were combined into a single \"Growth Drivers Submodel\" extending beyond the area to be projected to eliminate edge effects. The values were reclassified from 1 to100, with 100 indicating the highest likelihood for development.<\/p>
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Projections were made in 5-year increments to the year 2050 (2025, 2030, 2035, 2040, 2045, and 2050). For each tract, the projections derived from the growth trends were distributed to parcels with the highest driver values first and limited to the maximum development potential from the Build-out Submodel. This cap was based on the land available for residential development, the recent historical densities for which each land use in each jurisdiction have developed, and development plans. Once this was done for all tracts in a county, the total of this preliminary county growth was compared against the published BEBR forecasted growth beyond 2024 for that time period. While the projected growth was typically close to the published county growth forecast, the growth was adjusted to match the county growth exactly. For downward adjustments, the growth was typically reduced for all tracts in proportion to their growth over the period. For upward adjustments, we use the Growth Drivers Submodel to identify groups of parcels with the highest relative probability for development. These upward adjustments were more common as we moved further away from the current year, as more areas that are currently growing reached their maximum capacity. Finally, the parcel-level estimates and projections were summarized by water service areas to support water supply planning and permitting for SWFWMD.<\/p>", "summary": "Parcel-level results of the tract-level population projections for counties all or partly within the Southwest Florida Water Management District (SWFWMD).", "title": "District Population Projections", "tags": [ "environment", "boundaries", "Population Projections", "Southwest Florida Water Management District", "Florida", "utilities", "Communication" ], "type": "", "typeKeywords": [], "thumbnail": "", "url": "", "minScale": 0, "maxScale": 0, "spatialReference": "", "accessInformation": "BEBR, GISA Inc., SWFWMD", "licenseInfo": "These data were not collected under the supervision of a licensed Professional Surveyor and Mapper. Use of these data requires a general understanding of GIS. This data was produced using professional standards, but it is provided \"as is\". The Southwest Florida Water Management District and GIS Associates make no warranties, guaranties or representations about the accuracy, completeness, quality or suitability of the data, either expressly or implied, including but not limited to any implied warranties of merchantability or fitness for a particular purpose. Neither the Southwest Florida Water Management District nor GIS Associates shall not be liable for any damages suffered as a result of using, modifying, contributing, displaying or distributing this data." }