Urban Park Size (Southeast Blueprint Indicator)

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks. Input Data * [Southeast Blueprint 2024 extent] * [FWS National Realty Tracts], accessed 12-13-2023 * [Protected Areas Database of the United States](PAD-US):[PAD-US 3.0] national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-2023 * 2020 Census Urban Areas from [the Census Bureau’s urban-rural classification]; [download the data], [read more about how urban areas were redefined following the 2020 census] * [OpenStreetMap data] “multipolygons” layer, accessed 12-5-2023 A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap[®] is open data, licensed under the[Open Data Commons Open Database License] (ODbL) by the[OpenStreetMap Foundation] (OSMF). Additional credit to OSM contributors. Read more on[the OSM copyright page]. * [2021 National Land Cover Database] (NLCD): Percentdevelopedimperviousness * 2023[NOAA coastal relief model]: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024 Mapping Steps * Create a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator. * Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”. * Save merged raster to .tif and import into ArcPro. * Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0. * Convert the raster produced above to vector using the “RasterToPolygon” tool. * Clip to 2024 subregions using “Pairwise Clip” tool. * Break apart multipart polygons using “Multipart to single parts” tool. * Hand-edit to remove deep marine polygon. * Dissolve the resulting data layer. * This produces a seamless polygon defining land and shallow marine areas. * Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024. * Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024. * Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks: * All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public. * All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use. * All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” ([BOEM]). * All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status. * Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes. * Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset. * The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function. * Clip the resulting data layer to the inland and nearshore extent. * Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”). * Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator. * Dissolve all the park polygons that were selected in the previous step. * Process all multipart polygons to single parts (“explode”) again. * Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers. * Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park. * Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID. * Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible. * Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function. * Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster. * Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995). * Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park. * Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC). * Extract again to the inland and nearshore extent. * Export the final vector file to a shapefile and import to ArcGIS Pro. * Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field. * Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools. * Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator. * Add color and legend to raster attribute table. * As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the[Southeast Blueprint Data Download]under > 6_Code. Final indicator values Indicator values are assigned as follows: 6= 75+ acre urban park 5= 50 to <75 acre urban park 4= 30 to <50 acre urban park 3= 10 to <30 acre urban park 2=5 to <10acreurbanpark 1 = <5 acre urban park 0 = Not identified as an urban park Known Issues * This indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. * This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands. * The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly natural. We tested many Southeast parks for misclassification issues, and the 80% average impervious threshold seemed to best capture edge cases like Old Fourth Ward Park in Atlanta, GA (where NLCD overestimates impervious) and City Plaza in Raleigh, NC (where NLCD underestimates impervious). However, some parks are likely still represented incorrectly in this indicator. * This indicator includes beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future. Other Things to Keep in Mind * This indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email[hilary_morris@fws.gov]). Literature Cited Dewitz, J., 2023, National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release. [[https://doi.org/10.5066/P9JZ7AO3]]. Gies E. (2006). The health benefits of parks: how parks help keep Americans fit & healthy. The Trust for Public Land. Accessed February 16, 2022. [[https://www.tpl.org/wp-content/uploads/2014/03/benefits_HealthBenefitsReport.pdf]]. Greene, C. S., & Millward, A. A. (2017). Getting closure: The role of urban forest canopy density in moderating summer surface temperatures in a large city. Urban ecosystems, 20(1), 141-156. [[https://link.springer.com/article/10.1007/s11252-016-0586-5]]. Hoover, F. A., & Hopton, M. E. (2019). Developing a framework for stormwater management: leveraging ancillary benefits from urban greenspace. Urban ecosystems, 22(6), 1139-1148. [[https://link.springer.com/article/10.1007/s11252-019-00890-6]]. Mertes, James D. and James R. Hall. 1995. Park, recreation, open space, and greenway guidelines. A project of the National Recreation and Park Association and American Academy for Park and Recreation Administration. Alexandria, VA: NPRA. NOAA National Centers for Environmental Information. (2023). Coastal Relief Models (CRMs) [Data set]. NOAA National Centers for Environmental Information. [[doi: 10.25921/5ZN5-KN44]]. Simpson, J. R. (1998). Urban forest impacts on regional cooling and heating energy use: Sacramento County case study. Journal of Arboriculture, 24, 201-214. [[https://www.fs.usda.gov/research/treesearch/61731]]. U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch. June 1, 2017. 2010 Census Urban Area National. [[https://catalog.data.gov/dataset/tiger-line-shapefile-2017-2010-nation-u-s-2010-census-urban-area-national]]. U.S. Department of Health and Human Services. Physical Activity Guidelines for Americans, 2nd edition. Washington, DC: U.S. Department of Health and Human Services; 2018. [[https://health.gov/sites/default/files/2019-09/Physical_Activity_Guidelines_2nd_edition.pdf]]. U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022, Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release. [[https://doi.org/10.5066/P9Q9LQ4B]]. Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case,Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C.,Rigge, Matthew B., Xian, George. 2018. A new generation of the United States National LandCover Database—Requirements, research priorities, design, and implementation strategies:ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123 [[https://doi.org/10.1016/j.isprsjprs.2018.09.006]].