U.S. Environmental Protection Agency 20130709 2.0 vector digital data Washington, DC U.S. Environmental Protection Agency, Office of Sustainable Communities State of Florida https://edg.epa.gov/data/Public/OP/SLD/SmartLocationDb.zip http://geodata.epa.gov/ArcGIS/rest/services/OA/SmartLocationDatabase/MapServer The Smart Location Database is a consistent nationwide geographic data resource for measuring location efficiency. It includes more than 90 attributes summarizing characteristics such as housing density, diversity of land use, neighborhood design, destination accessibility, distance to transit, employment, and demographics. Most attributes are available for every census block group in the State of Florida. A census block group is a geographical unit used by the US Census that is smaller than a census tract but larger than a census block. Each block groups is a cluster of census blocks having the same first digit of their four-digit identifying numbers within a census tract. Version 2.0 of this data was first released in July 2013. See the user guide for a full description of data sources, data currency, and known limitations: https://edg.epa.gov/data/Public/OP/SLD/SLD_userguide.pdf The Smart Location Database (SLD) enables the consistent comparison of different locations across the U.S. in terms of their land use characteristics, infrastructure, and accessibility to nearby destinations. SLD attributes were selected for their utility in travel demand modeling studies. However they may also be relevant to land use scenario planning, community development, and public health studies – particularly when obtaining local data is not possible or practical. Planned updates to the SLD will summarize these environment and accessibility characteristics in the form of a Smart Location Index that represents a block group’s relative location efficiency when compared to other block groups within the same metropolitan region. http://www.epa.gov/smartgrowth/smartlocationdatabase.htm Sample Variables Included in the Smart Location Database For full descriptions of the variables, see the Smart Location Database User Guide (PDF) (35 pp, 1.22MB, About PDF). Category Density Gross residential density (housing units per acre) on unprotected land Gross population density (people per acre) on unprotected acre Gross employment density (jobs per acre) on unprotected acre Diversity of land use Jobs per housing unit Employment entropy (a measure of employment diversity) Employment and housing entropy Urban design Street intersections per square mile High-speed road network density Transit service Aggregate transit service frequency, afternoon peak period Transit service density, afternoon peak period Distance to nearest transit stop Destination accessibility by transit Jobs within a 45-minute transit commute Working-age population within a 45-minute transit commute Destination accessibility by car Jobs within a 45-minute drive Working-age population within a 45-minute drive Demographics Percentage of households with no car, 1 car, or 2 or more cars Percentage of workers that are low, medium, or high wage (by home and work locations) Employment Employment totals broken down by 5-tier classification scheme Employment totals broken down by 8-tier classification scheme ------------------------------------ Uses of the Smart Location Database Accessing and comparing neighborhood conditions Users can browse a simple interactive map Link to EPA's External Link Disclaimer to assess and compare conditions across different neighborhoods in their communities. Developing indicators of location efficiency EPA is using the Smart Location Database to develop simple indicators of location efficiency. For instance, EPA is working with the U.S. General Services Administration to develop a Smart Location Index that scores census block groups based on their built environment and accessibility characteristics. Block groups that are associated with reduced vehicle miles traveled receive higher scores compared to other block groups within the same metropolitan region. Advanced users could create similar composite indicators to compare walk ability, compact design, or other built environment characteristics. EPA hopes to include additional indicators of location efficiency in forthcoming updates to the Smart Location Database. Scenario planning and travel demand modeling Planners can use the Smart Location Database as baseline data in scenario planning, sketch planning, and travel demand studies when more detailed or consistent local data are unavailable. Analysts can also use elasticities found in the research literature6 to adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment. Conducting nationwide research studies and developing tools Building on previous research, EPA is conducting a nationwide modeling study to predict employee commute travel (e.g., average trip distances, mode share, vehicle miles traveled, etc.) based on workplace neighborhood characteristics. If successful, this study will produce equations for refining travel demand models. This study and others like it also make it possible to create simple online tools to help more communities analyze the potential outcomes of proposed land use development. Comparing urban form among metropolitan regions Researchers can use the Smart Location Database in nationwide studies that compare metropolitan regions based on urban form characteristics. For instance, analysts could determine the percentage of residents that live in walkable or transit-rich neighborhoods. EPA’s 2012 study Residential Construction Trends in America’s Metropolitan Regions used the Smart Location Database in conjunction with data from the National Land Cover Database and the American Community Survey to measure and compare infill housing development. Modeling impervious surface growth EPA analyzed variables in a previous version of the Smart Location Database and the National Land Cover Database to create a model and simple spreadsheet tool for estimating new impervious surface growth associated with land use development scenarios. This model is sensitive not only to density of development but also to its relative centrality within the surrounding metropolitan region. For details, see EPA’s Impervious Surface Growth Model. For questions about the Smart Location Database and associated projects, please contact Kevin Ramsey (202-566-1153, ramsey.kevin@epa.gov). 20100101 publication date Complete As needed -87.639361 -79.812218 31.042647 24.354700 None Destination accessibility by car urban form travel demand Destination accessibility by transit neighborhood conditions Urban design Demographics Transit service Smart Location Database impervious surface growth SLD location efficiency Density Diversity of land use Employment None Land Human Environment Transportation None transportation society ISO 19115 Topic Categories environment location transportation society None Florida Year 2010 2013 None None. Please check sources, scale, accuracy, currentness and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of the EPA would be appreciated. U.S. Environmental Protection Agency, Office of Policy, Office of Sustainable Communities Kevin Ramsey Policy Research Fellow mailing

1200 Pennsylvania Avenue, NW (MC 1807T)

Washington DC 20460 US (202) 566-1153 ramsey.kevin@epa.gov http://www.epa.gov/smartgrowth U.S. EPA Smart Growth Program FIPS Pub 199 Standard Technical Controls Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595 Smart Location Database http://www.epa.gov/dced/smartlocationdatabase.htm SLD Version 2.0 User Guide https://edg.epa.gov/data/Public/OP/SLD/SLD_UserGuide.pdf GeoPlan relied on the integrity of the attribute information within the original data. Attribute value ranges were tested for validity. See report for full discussion of corrective measures taken. See report for full discussion of data completeness. Data were collected using methods that have unknown accuracy (EPA National Geospatial Data Policy [NGDP] Accuracy Tier 10). For more information, please see EPA's NGDP at http://epa.gov/geospatial/policies.html This data is provided 'as is' and its vertical positional accuracy has not been verified by GeoPlan 0 None Spatial and Attribute Information See report for full description of processing steps. Smart Location Database Version 2.0 User Guide https://edg.epa.gov/data/Public/OP/SLD/SLD_UserGuide.pdf USEPA The GeoPlan Center downloaded the Smart Location Database from the following US EPA Website on November 21st, 2013. http://www.epa.gov/dced/smartlocationdatabase.htm Download data for the entire nation: Shapefile (ZIP) (800 MB) https://edg.epa.gov/data/Public/OP/SLD/SLD_shapefile.zip The nationwide file was originally named: SmartLocationDb.shp and it was in the following projection: GCS_WGS_1984 GeoPlan ran a select by attribute on the field SFIPS = '12' Next GeoPlan Reprojected the selected data to the FGDL Albers NAD 83 Harn projection. Next GeoPlan renamed the dataset to EPA_SLD_JUL13.shp Next GeoPlan Added and populated the following fields. DESCRIPT FGDLAQDATE Finally GeoPlan updated the metadata. Online Metadata can be viewed at the following website: https://edg.epa.gov/metadata/rest/document?id={BCE98875-BED3-4911-8BEA-32220B3E15E7}&xsl=metadata_to_html_full GeoPlan 20131121 Vector GT-polygon composed of chains 11442 NAD 1983 HARN Florida GDL Albers 24.0 31.5 -84.0 24.0 400000.0 0.0 coordinate pair 0.0001 0.0001 meter D North American 1983 HARN GRS 1980 6378137.0 298.257222101 EPA_SLD_JUL13 EPA_SLD_JUL13.DBF US Census OBJECTID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Coordinates defining the features. GEOID10 Unique 12-digit block group identification string (FIPS code) US Census 2010 SFIPS State 2-digit FIPS code US Census CFIPS County 3-digit FIPS code US Census TRFIPS Census tract FIPS code in which CBG resides US Census CSA FIPS for Combined Statistical Area in which CBG resides US Census CSA_Name Name of CSA in which CBG resides US Census CBSA FIPS for core based statistical area (CBSA) in which CBG resides US Census CBSA_Name Name of CBSA in which CBG resides US Census CBSA_EMP Total employment in CBSA Census LEHD, 2010 CBSA_POP Total population in CBSA US Census CBSA_WRK Total number of workers that live in CBSA Census LEHD, 2010 COUNTHU10 Housing units, 2010 US Census 2010 TOTPOP10 Population, 2010 US Census 2010 HH Households (occupied housing units), 2010 US Census 2010 P_WrkAge Percent of population that is working-aged, 2010 US Census 2010 AutoOwn0 Numer of households in CBG that own zero automobiles, 2010 Amercian Community Survey, 2010 Pct_AO0 Percent of households with zero automobiles, 2010. Amercian Community Survey, 2010 AutoOwn1 Number of households in CBG that own one automobile, 2010. Amercian Community Survey, 2010 Pct_AO1 Percentage of households with one automobile, 2010 Amercian Community Survey, 2010 AutoOwn2p Number of households that own 2 or more automobiles, 2010 Amercian Community Survey, 2010 Pct_AO2p Percentage of of households with 2 or more automobiles, 2010 Amercian Community Survey, 2010 Workers Number of workers in CBG (home location), 2010 Census LEHD, 2010 R_LOWWAGEW Number of workers earning $1250/month or less (home location), 2010 Census LEHD, 2010 R_MEDWAGEW Number of workers earning more than $1250/month but less than $3333/month (home location), 2010 Census LEHD, 2010 R_HiWageWk Number of workers earning greater than $3333/month (home location), 2010 Census LEHD, 2010 R_PCTLOWWA Percent of LowWageWk of total number workers in a CBG (home location), 2010 Census LEHD, 2010 EMPTOT Total employment, 2010 Census LEHD, 2010 InfoUSA, 2011 (MA only) E5_Ret10 Retail jobs within a 5-tier employment classification scheme (LEHD: CNS07) Census LEHD, 2010 E5_Off10 Office jobs within a 5-tier employment classification scheme (LEHD: CNS09 + CNS10 + CNS11 + CNS13 + CNS20) Census LEHD, 2010 E5_Ind10 Industrial jobs within a 5-tier employment classification scheme (LEHD: CNS01 + CNS02 + CNS03 + CNS04 + CNS05 + CNS06 + CNS08) Census LEHD, 2010 E5_Svc10 Service jobs within a 5-tier employment classification scheme (LEHD: CNS12 + CNS14 + CNS15 + CNS16 + CNS19) Census LEHD, 2010 E5_Ent10 Entertainment jobs within a 5-tier employment classification scheme (LEHD: CNS17 + CNS18) Census LEHD, 2010 E8_Ret10 Retail jobs within a 8-tier employment classification scheme (LEHD: CNS07) Census LEHD, 2010 E8_Off10 Office jobs within a 8-tier employment classification scheme (LEHD: CNS09 + CNS10 + CNS11 + CNS13) Census LEHD, 2010 E8_Ind10 Industrial jobs within a 8-tier employment classification scheme (LEHD: CNS01 + CNS02 + CNS03 + CNS04 + CNS05 + CNS06 + CNS08) Census LEHD, 2010 E8_Svc10 Service jobs within a 8-tier employment classification scheme (LEHD: CNS12 + CNS14 + CNS19) Census LEHD, 2010 E8_Ent10 Entertainment jobs within a 8-tier employment classification scheme (LEHD: CNS17 + CNS18) Census LEHD, 2010 E8_Ed10 Education jobs within a 8-tier employment classification scheme (LEHD: CNS15) Census LEHD, 2010 E8_Hlth10 Health care jobs within a 8-tier employment classification scheme (LEHD: CNS16) Census LEHD, 2010 E8_Pub10 Public administration jobs within a 8-tier employment classification scheme (LEHD: CNS20) Census LEHD, 2010 E_FEDT10 Federal employment (total). Census LEHD, 2010 E_FEDRET10 Federal retail employment Census LEHD, 2010 E_FEDOFF10 Federal office employment Census LEHD, 2010 E_FEDIND10 Federal Industrial Employment Census LEHD, 2010 E_FEDSVC10 Federal Service Employment Census LEHD, 2010 E_FEDENT10 Federal Entertainment Employment Census LEHD, 2010 E_LOWWAGEW Number of workers earning $1250/month or less (work location), 2010 Census LEHD, 2010 E_MEDWAGEW Number of workers earning more than $1250/month but less than $3333/month (work location), 2010 Census LEHD, 2010 E_HiWageWk Number of workers earning greater than $3333/month (work location), 2010 Census LEHD, 2010 E_PCTLOWWA Percent of LowWageWk of total number workers in a CBG (work location), 2010 Census LEHD, 2010 Ac_tot Total geometric area of the CBG (in acres) US Census TIGER/Line Shapefile 2010 AC_WATER Total water area in acres Census, Navteq Water and Oceans Ac_Unpr Acres of land area that is not protected from development (i.e., not a park or conservation area) Navteq 2011; PAD-US 2013 Ac_Land Block group area that is land (in acres) Navteq 2011; PAD-US 2013 D1a Gross residential density (HU/acre) on unprotected land, 2010 Derived from SLD metrics D1b Gross population density (people/acre) on unprocted land, 2010 Derived from SLD metrics D1c Gross employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics D1C5_Ret10 Gross retail (5-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1c5_Off10 Gross office (5-tier) employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics D1C5_Ind10 Gross industrial (5-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1c8_Off10 Gross office (8-tier) employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics D1C5_Ent10 Gross entertainment (5-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1C8_Ret10 Gross retail (8-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1c5_Svc10 Gross service (5-tier) employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics D1C8_Ind10 Gross industrial (8-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1c8_Svc10 Gross service (8-tier) employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics 0 D1C8_Ent10 Gross entertainment (8-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1c8_Ed10 Gross education (8-tier) employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics D1C8_Hlth1 Gross health care (8-tier) employment density (jobs/acre) on unprotected land Derived from other SLD variables D1c8_Pub10 Gross public sector (8-tier) employment density (jobs/acre) on unprotected land, 2010 Derived from SLD metrics D1d Gross activity density (HU + employment / acre) on unprotected land, 2010 Derived from SLD metrics D1_flag Flag to indicate D1 density metrics use land area rather than unprotected land area in denominator. Derived from SLD metrics D2a_JpHH Jobs per housing unit Derived from SLD metrics D2b_E5Mix 5-tier employment entropy Derived from SLD metrics D2B_E5MIXA 5-tier employment entropy (denominator set to the static 5 employment types in the CBG) Derived from other SLD variables D2b_E8Mix 8-tier employment entropy Derived from SLD metrics D2b_E8MixA 8-tier employment entropy, denominator set to the static 8 eployment types in the CBG Derived from SLD metrics D2a_EpHHm 5-tier employment and household entropy Derived from SLD metrics D2c_TrpMx1 5-tier employment and household entropy, based on vehicle trip production and trip attractions Derived from SLD metrics D2c_TrpMx2 Employment and household entropy (excluding industrial jobs), based on trip production and attraction Derived from SLD metrics D2c_TripEq Trip production and trip attractions equilibrium index (closer to 1 = more balance) Derived from SLD metrics D2r_JobPop Deviation of CBG jobs/population ratio from regional average jobs/pop ratio Derived from SLD metrics D2a_WrkEmp Household workers per job Derived from SLD metrics D2r_WrkEmp Deviation of CBG ratio of household workers/job from regional average ratio of household workers/ob Derived from SLD metrics D2c_WrEmix Household worker per job equilibrium index (closer to one = more balanced) Derived from SLD metrics D3a Total road network density (facility-miles per square mile) Navteq NAVSTREETS 2011 D3aao Road network density in terms of facility miles of auto-oriented links per square mile Navteq NAVSTREETS 2011 D3amm Road network density in terms of facility miles of multi-model links per square mile Navteq NAVSTREETS 2011 D3apo Street network density in terms of facility miles of pedestrian oriented links per square mile Navteq NAVSTREETS 2011 D3b Weighted intersection density per square mile (auto-oriented intersections excluded) Navteq NAVSTREETS 2011 D3bao Intersection density in terms of auto-oriented intersections per square mile Navteq NAVSTREETS 2011 D3bmm3 Intersection density in terms of multi-model intersections having three legs per square mile Navteq NAVSTREETS 2011 D3bmm4 Intersection density in terms of multi-model intersections having four or more legs per square mile Navteq NAVSTREETS 2011 D3bpo3 Intersection density in terms of pedestrian-oriented intersections having three legs per square mile Navteq NAVSTREETS 2011 D3bpo4 Intersection density in terms of pedestrian-oriented intersections having four or more legs per square mile Navteq NAVSTREETS 2011 D4a Distance from population weighted centroid to nearest transit stop, meters Publically available GTFS data, 2012 D4b025 Proportion of CBG employment within 1/4 mile of fixed guideway transit stop Census LEHD 2010; Center for Transit Oriented Development, 2011 D4b050 Proportion of CBG employment within 1/2 mile of fixed guideway transit stop Census LEHD 2010; Center for Transit Oriented Development, 2011 D4c Aggregate frequency of transit service within 0.25 miles of block group boundary per hour during evening peak period Publically available GTFS data, 2012 D4d Peak pm transit departure within 0.25 miles of CBG, per square mile Derived from SLD metrics D5ar Jobs within a 45 minute drive (weighted) Census LEHD 2010; Navteq NAVSTREETS 2011 D5ae Working-age population within 45 min. drive (weighted) Census 2010; Navteq NAVSTREETS 2011 D5br Jobs within 45 min. transit commute (weighted) Census LEHD 2010; Publically available GTFS data, 2012 D5br_Flag Transit accessibility score reflects reverse accessibility to account for direction transit service. Census LEHD 2010; Publically available GTFS data, 2012 D5be Working-age population within 45 min. transit commute (weighted) Census 2010; Publically available GTFS data, 2012 D5be_Flag Transit accessibility score reflects reverse accessibility to account for direction transit service Census LEHD 2010; Publically available GTFS data, 2012 D5cr Job accessibility (D5ar) as proportion of total regional job accessibility Derived from SLD metrics D5cri Regional centrality index (auto) - D5cr divided by max D5cr in metro region (CBSA) Derived from SLD metrics D5ce Accessibility to working-age populatin (D5ae) as proportion of total regional accessibility Derived from SLD metrics D5cei Regional centrality index (auto) - D5ce divided by max D5ce in metro region (CBSA) Derived from SLD metrics D5dr Job accessibility by transit (D5br) as proportion of total regional job accessibility by transit Derived from SLD metrics D5dri Regional centrality index (transit) - D5dr divided by max D5dr in metro region (CBSA) Derived from SLD metrics D5de Accessibility to working-age populatin by transit (D5be) as proportion of total regional accessibility Derived from SLD metrics D5dei Regional centrality index (transit) - D5de divided by max D5de in metro region (CBSA) Derived from SLD metrics DESCRIPT Based on field GEOID10. GeoPlan FGDLAQDATE The date FGDL acquired the data from the Source. GeoPlan AUTOID Unique ID added by GeoPlan GeoPlan SHAPE.AREA Area in meters GeoPlan SHAPE.LEN Perimeter in meters GeoPlan Attribute descriptions and data sources available in the SLD Guidebook. https://edg.epa.gov/data/Public/OP/SLD/SLD_UserGuide.pdf See report. https://edg.epa.gov/data/Public/OP/SLD/SLD_UserGuide.pdf U.S. Environmental Protection Agency, Office of Policy, Office of Sustainable Communities Kevin Ramsey mailing

431 Architecture PO Box 115706

Gainesville Florida 32611-5706 US FGDL Frequently Asked Questions: http://www.fgdl.org/fgdlfaq.html For FGDL Software: http://www.fgdl.org/software.html Mailing list for FGDL: http://www.fgdl.org/fgdl-l.html Web site: http://www.fgdl.org Technical Support: http://www.fgdl.org/fgdlfeed.html DOWNLOADABLE DATA The Florida Geographic Data Library is a collection of Geospatial Data compiled by the University of Florida GeoPlan Center with support from the Florida Department of Transportation. GIS data available in FGDL is collected from various state, federal, and other agencies (data sources) who are data stewards, producers, or publishers. The data available in FGDL may not be the most current version of the data offered by the data source. University of Florida GeoPlan Center makes no guarantees about the currentness of the data and suggests that data users check with the data source to see if more recent versions of the data exist. Furthermore, the GIS data available in the FGDL are provided 'as is'. The University of Florida GeoPlan Center makes no warranties, guaranties or representations as to the truth, accuracy or completeness of the data provided by the data sources. The University of Florida GeoPlan Center makes no representations or warranties about the quality or suitability of the materials, either expressly or implied, including but not limited to any implied warranties of merchantability, fitness for a particular purpose, or non-infringement. The University of Florida GeoPlan Center shall not be liable for any damages suffered as a result of using, modifying, contributing or distributing the materials. A note about data scale: Scale is an important factor in data usage. Certain scale datasets are not suitable for some project, analysis, or modeling purposes. Please be sure you are using the best available data. 1:24000 scale datasets are recommended for projects that are at the county level. 1:24000 data should NOT be used for high accuracy base mapping such as property parcel boundaries. 1:100000 scale datasets are recommended for projects that are at the multi-county or regional level. 1:125000 scale datasets are recommended for projects that are at the regional or state level or larger. Vector datasets with no defined scale or accuracy should be considered suspect. Make sure you are familiar with your data before using it for projects or analysis. Every effort has been made to supply the user with data documentation. For additional information, see the References section and the Data Source Contact section of this documentation. For more information regarding scale and accuracy, see our webpage at: http://geoplan.ufl.edu/education.html 20200303 20170723 U.S. Environmental Protection Agency, Office of Policy, Office of Sustainable Communities Kevin Ramsey Policy Research Fellow mailing

1200 Pennsylvania Avenue, NW (MC 1807T)

Washington DC 20460 US (202) 566-1153 ramsey.kevin@epa.gov http://www.epa.gov/smartgrowth FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time