This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

The Till Areas Overlaying Fluvial and Lacustrine Sediment in New Jersey geodatabase layer is a Geographic Information Systems (GIS) geodatabase layer compiled from NJGS Open-File Map No. 3, Hydrogeologic Character and Thickness of the Glacial Sediment of New Jersey, 1990 (revised 2002). The layer details the map distribution, thickness, and types of Quaternary glacial sediment in northern New Jersey at the 1:100,000 scale. The GIS data are compiled in NAD83 State-Plane-Coordinate feet using an optical scanner, raster-to-vector conversion software, and a digitizer. This layer contains polygons of till deposits overlying fluvial and lacustrine sediment.

This land cover data set was derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor operating on board the United States National Oceanic and Atmospheric Administration (NOAA) satellites. Information on the NOAA series of satellites can be found at www.noaa.gov/satellites.html The vegetation and land cover information set has been classified into twelve categories. Information on the classification of the vegetation and land cover, raster to vector conversion, generalization for cartographic presentations is included in the paper "The Canada Vegetation and Land Cover: A Raster and Vector Data Set for GIS Applications - Uses in Agriculture" (https://geogratis.cgdi.gc.ca/download/landcover/scale/gis95ppr.pdf). A soil quality evaluation was obtained by cross-referencing the AVHRR information with Census of Agriculture records and biophysical (Soil Landscapes of Canada) data and is also included in the above paper. AVHRR Land Cover Data approximates a 1:2M scale and was done originally for Agriculture Canada. The projection used is Lambert Conformal Conic (LCC) 49/77 with origin at 49N 95W.

The Glacial Sediments in New Jersey layer is a Geographic Information Systems (GIS) geodatabase layer compiled from NJGS Open-File Map No. 3, Hydrogeologic Character and Thickness of the Glacial Sediment of New Jersey, 1990 (revised 2002). The layer details the map distribution, thickness, and types of Quaternary glacial sediment in northern New Jersey at the 1:100,000 scale. The GIS data are compiled in NAD83 State-Plane-Coordinate feet using an optical scanner, raster-to-vector conversion software, and a digitizer. This "sediment" layer contains polygons of stratigraphic units.

The primary purpose of this dataset is to provide VCRLTER researchers and students with a convenient and comprehensive set of historical NOS t-sheet shorelines spanning the full Virginia Eastern Shore in a single GIS data layer. From NOAA-NOS-NGS source metadata: "These shoreline data represent a vector conversion of a set of NOS raster shoreline manuscripts identified by t-sheet or tp-sheet numbers. These vector data were created by contractors for NOS who vectorized georeferenced raster shoreline manuscripts using Environmental Systems Research Institute, Inc. (ESRI)(r), ArcInfo's(r) ArcScan(r) software to create individual ArcInfo coverages. The individual coverages were ultimately edgematched within a surveyed project area and appended together. The NOAA NESDIS Environmental Data Rescue Program (EDRP) funded this project. The NOAA National Ocean Service, Coastal Services Center, developed the procedures used in this project and was responsible for project oversight. The project intent was to rescue valuable historical data and make it accessible and useful to the coastal mapping community. This process involved the conversion of original analog products to digital mapping products. This file is a further conversion of that product from a raster to a vector product that may be useful for Electronic Charting and Display Information Systems (ECDIS) and geographic information systems (GIS)." Original NOAA-NOS-NGS data were organized by project, with each project containing a single shapefile containing the historical shoreline features from multiple T-sheets based on surveys from roughly the same time period. There were 43 projects containing information from 208 T-sheets and TP-sheets that were found to cover the Eastern Shore of VA and southern MD and ranging in time from 1847 to 1978 (plus one set of shorelines from 2009 for the new Chincoteague bridge and the immediate surrounding area). VCRLTER staff combined these 43 shapefiles into a single shapefile with an added "PROJID" attribute to identify the source project. This shoreline dataset compliments and overlaps other VCRLTER shoreline datasets for the Virginia barrier islands that contain historical shorelines derived from a combination of sources, including: a subset of the included NOS t-sheets (digitized by VCRLTER researchers prior to availability in digital format from NOAA-NOS-NGS); NOAA coastal change maps; photointerpretation of aerial photos (from USGS, USACE, VITA-VGIN-VBMP, and others), and satellite imagery (from ETM+ Landsat 7 and IKONOS); and GPS surveys.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

vfillDL.zip

dems: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . extents: extents of the training, testing, and validation areas. These extents were defined by the researchers. vectors: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

Ce jeu de données sur la couverture des terres a été obtenu à l’aide d’un capteur AVHRR (radiomètre perfectionné à très haute résolution) à bord de satellites de la National Oceanic and Atmospheric Administration (NOAA) des États-Unis. Le site Web suivant offre de l’information sur les séries de satellites de la NOAA : www.noaa.gov/satellites.html L’ensemble des données sur la couverture végétale et la couverture des terres a été classé en douze catégories. Pour obtenir de l’information sur la classification de la couverture végétale et de la couverture des terres, sur la conversion du format matriciel au format vectoriel ainsi que sur la généralisation pour des présentations cartographiques, on peut consulter le document « The Canada Vegetation and Land Cover: A Raster and Vector Data Set for GIS Applications - Uses in Agriculture » disponible à l’adresse : http://geogratis.cgdi.gc.ca/download/landcover/scale/gis95ppr.pdf. Une évaluation de la qualité du sol, incluse dans le document cité plus haut, a été réalisée en mettant en correspondance les données de l’AVHRR et celles du Recensement de l’agriculture ainsi que les données biophysiques des Pédo-paysages du Canada. La représentation des données de l’AVHRR sur la couverture des terres a été réalisée initialement pour Agriculture Canada, à une échelle approximative de 1/2 000 000. La projection utilisée est la projection conique conforme de Lambert (CCL) 49/77, dont l’origine se trouve par 49 °N. 95 °O.

Ce jeu de données n’est plus maintenu par Agriculture et agroalimentaire Canada et devrait être considéré comme produit archivé. Pour les estimations actuelles de l’étendu agricole au Canada veuillez référer à l’écoumène agricole produit par Statistique Canada. https://www150.statcan.gc.ca/n1/fr/catalogue/92-639-X L'étendue agricole canadienne générée par le radiomètre perfectionné à très haute résolution AVHRR (Advanced Very High Resolution Radiometer) provient du site web GéoGratis (www.geogratis.ca). Lorsque nous avons reçu les données, GéoGratis en avait déjà supprimé tous les polygones ayant une superficie inférieure à 50 km carrés. Ce produit permet à l'usager de voir les régions significatives de terres cultivées et de pâturages sur l'ensemble du Canada. Le présent jeu de données est une de quatre interprétations de l'étendue agricole sur l'ensemble du Canada. Ce produit a été créé aux fins d'analyse, pour permettre à l'usager de voir les régions significatives où les terres cultivées et les pâturages sont situés au Canada, d'après le radiomètre perfectionné à très haute résolution AVHRR. L’ensemble des données sur la couverture végétale et la couverture des terres a été classé en douze catégories. Pour obtenir de l’information sur la classification de la couverture végétale et de la couverture des terres, sur la conversion du format matriciel au format vectoriel ainsi que sur la généralisation pour des présentations cartographiques, on peut consulter le document « The Canada Vegetation and Land Cover: A Raster and Vector Data Set for GIS Applications - Uses in Agriculture » disponible à l’adresse : http://geogratis.cgdi.gc.ca/download/landcover/scale/gis95ppr.pdf. Une évaluation de la qualité du sol, incluse dans le document cité plus haut, a été réalisée en mettant en correspondance les données de l’AVHRR et celles du Recensement de l’agriculture ainsi que les données biophysiques des Pédo-paysages du Canada. La représentation des données de l’AVHRR sur la couverture des terres a été réalisée initialement pour Agriculture Canada, à une échelle approximative de 1/2 000 000. La projection utilisée est la projection conique conforme de Lambert (CCL) 49/77, dont l’origine se trouve par 49 °N. 95 °O."

The Ottawa 1:1,200 Topographic Maps is a collection of scanned and vectorized hardcopy maps (paper format) of the Ottawa-Gatineau region. The range of years of the maps are from 1934 to 1979. The hardcopy maps were sent out to be scanned in the summer of 2007 and are currently archived at the Carleton University Library storage facility.Some of the maps are available for direct download through this interactive index, while others can only be obtained by visiting the Research Support Services desk for assistance.The maps were scanned at 300 dpi by Kovatec Inc., an Ottawa-based company. They were saved as uncompressed TIF files along with the following processing operations: minor crop, deskew, global despeckling and rotation to proper aspect. In addition, Kovatec Inc. carried out an automatic raster to vector conversion, from TIF files to AutoCAD 14 (.dwg) files.The digital maps do not have a spatial reference but can be georeferenced using a GIS software.

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 DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0 national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 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 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A 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 theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA 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 3-27-2024Mapping StepsCreate 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 theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis 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

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf Data Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on the Marine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 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 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster. Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among

The Digital 1:2,500 Topographic Maps is a collection of scanned and vectorized hardcopy maps (Mylar format) of the Ottawa/Gatineau region. The range of years of the maps are from 1966 to 1980. The hardcopy maps were sent out to be scanned in the summer of 2007 and are currently archived at the Carleton University Library storage facility.The maps were scanned at 300 dpi by Kovatec Inc., an Ottawa based company. They were saved as uncompressed TIF files along with the following processing operations: minor crop, deskew, global despeckling and rotation to proper aspect. In addition, Kovatec Inc. carried out an automatic raster to vector conversion, from TIF files to AutoCAD 14 (.dwg) files.The digital maps do not have a spatial reference but can be georeferenced using a GIS software.

Upstream watershed condition is very important in determining the nutrient, sediment, and contaminant loads entering a lake. All developed and agricultural land cover categories (disturbance) were added to quantify the amount of disturbed landcover that may negatively affect water quality entering the lake. 1. Assign the amount of disturbance in the contributing watershed: a. Join the attribute table “Archetypes_rawdata_2023-10-12.csv” (LaPierre et al. 2023) to the LAGOS Lakes dataset.b. Eliminate any lakes that do not have a valid watershed value (removes 3253 or 0.7 % of lakes) c. Add the upstream watershed amounts of agricultural and developed landcover “ws_nlcd2016_totalag_pct” and “ws_nlcd2016_totaldev_pct” to get “totdev_totag”d. Classify lake water quality based on the Midwest Glacial Lakes Partnership thresholds for watershed landcover composition and convert to raster: z 3 = if percent developed + percent ag in the watershed is <= 25 2 = if percent developed + percent ag in the watershed is >25 and <=60 1 = if percent developed + percent ag in the watershed is > 60 4. Using results from above, convert vector to raster to get:a. the whole lake class of contributing watershed condition.7. Clean up errant pixels caused by non-overlapping LAGOS and NLCD open water using the condition: If the final score is NULL but there is an open water class value, then assign that value to the pixel, otherwise, keep the existing value.

Reason for SelectionThe Southeast United States is a global biodiversity hotspot that supports many rare and endemic reptile and amphibian species (Barrett et al. 2014, EPA 2014). These species are experiencing dramatic population declines driven by habitat loss, pollution, invasive species, and disease (Sutherland and deMaynadier 2012, EPA 2014, CI et al. 2004). Amphibians provide an early signal of environmental change because they rely on both terrestrial and aquatic habitats, are sensitive to pollutants, and are often narrowly adapted to specific geographic areas and climatic conditions. As a result, they serve as effective indicators of ecosystem health (CI et al. 2004, EPA 2014). Their association with particular microhabitats and microclimates makes amphibians vulnerable to climate change, and Southeast amphibians are predicted to lose significant amounts of climatically suitable habitat in the future (Barrett et al. 2014). PARCAs also represent the condition and arrangement of embedded isolated wetlands. Many amphibians breed in temporary (i.e., ephemeral) wetlands surrounded by upland habitat, which are not well-captured by existing indicators in the Blueprint (Erwin et al. 2016).Input DataSoutheast Blueprint 2024 extent2023 U.S. Census TIGER/Line state boundaries, accessed 4-5-2024: download the data Southeast Priority Amphibian and Reptile Conservation Areas (PARCAs) PARCAs for all Southeast states except for Mississippi, Virginia, and Kentucky, shared by José Garrido with the Amphibian and Reptile Conservancy (ARC) on 3-5-2024PARCAs for Mississippi, shared by Luis Tirado with ARC on 4-26-2024 (these PARCAs were identified more recently and were not yet captured in ARC’s Southeast PARCAs dataset)South Atlantic PARCAs: Neuse Tar River PARCA (this PARCA was identified through a project funded by the South Atlantic Landscape Conservation Cooperative and is not yet captured in ARC’s Southeast PARCAs dataset; we added this PARCA after consultation with ARC staff) To view a map depicting some of the PARCAs provided, scroll to the bottom of the work page of the ARC website under the heading “PARCAs Nationwide”; to access the data, email info@ARCProtects.org. PARCA is a nonregulatory designation established to raise public awareness and spark voluntary action by landowners and conservation partners to benefit amphibians and/or reptiles. Areas are nominated using scientific criteria and expert review, drawing on the concepts of species rarity, richness, regional responsibility, and landscape integrity. Modeled in part after the Important Bird Areas program developed by BirdLife International, PARCAs are intended to be nationally coordinated but locally implemented at state or regional scales. Importantly, PARCAs are not designed to compete with existing landscape biodiversity initiatives, but to complement them, providing an additional spatially explicit layer for conservation consideration.
PARCAs are intended to be established in areas: capable of supporting viable amphibian and reptile populations, occupied by rare, imperiled, or at-risk species, and rich in species diversity or endemism. For example, species used in identifying the PARCAs in the Southeast include: alligator snapping turtle, Barbour’s map turtle, one-toed amphiuma, Savannah slimy salamander, Mabee’s salamander, dwarf waterdog, Neuse river waterdog, chicken turtle, spotted turtle, tiger salamander, rainbow snake, lesser siren, gopher frog, Eastern diamondback rattlesnake, Southern hognose snake, pine snake, flatwoods salamander, gopher tortoise, striped newt, pine barrens tree frog, indigo snake, and others. There are four major implementation steps: Regional PARC task teams or state experts can use the criteria and modify them when appropriate to designate potential PARCAs in their area of interest. Following the identification of all potential PARCAs, the group then reduces these to a final set of exceptional sites that best represent the area of interest. Experts and stakeholders in the area of interest collaborate to produce a map that identifies these peer-reviewed PARCAs. Final PARCAs are shared with the community to encourage the implementation of voluntary habitat management and conservation efforts. PARCA boundaries can be updated as needed. Mapping Steps Merge the three PARCA polygon datasets and convert from vector to a 30 m pixel raster using the ArcPy Feature to Raster function. Give all PARCAs a value of 1.Add zero values to represent the extent of the source data and to make it perform better in online tools. Convert to raster the TIGER/Line state boundaries for all SEAFWA states except for Virginia and Kentucky and assign them a value of 0. We excluded Virginia and Kentucky because PARCAs have not yet been identified for these states. Use the Cell Statistics “MAX” function to combine the two above rasters.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 valuesIndicator values are assigned as follows:1 = Priority Amphibian and Reptile Conservation Area (PARCA) 0 = Not a PARCA (excluding Kentucky and Virginia)Known IssuesThe mapping of this indicator is relatively coarse and doesn’t always capture differences in pixel-level quality in the outer edge of PARCAs. For example, some PARCAs include developed areas.This indicator is binary and doesn’t capture the full continuum of value across the Southeast.The methods of combining expert knowledge and data in this indicator may have caused some poorly known and/or under-surveyed areas to be scored too low.This indicator underprioritizes important reptile and amphibian habitat in Kentucky and Virginia because PARCAs have not yet been identified for these areas. ARC is working to expand PARCAs to more states in the future.Because of the state-by-state PARCA development and review process, sometimes PARCA boundaries stop at the state line, though suitable habitat for reptiles and amphibians does not always follow jurisdictional boundaries.This indicator excludes “protected” PARCAs maintained by ARC that are too small and spatially explicit to share publicly due to concerns about poaching. As a result, it underprioritizes some important reptile and amphibian habitat. However, these areas are, with a few exceptions in northwest Arkansas and Tennessee, generally well-represented in the Blueprint due to their value for other indicators.This indicator contains small gaps 1-2 pixels wide between some adjoining PARCAs that likely should be continuous, often on either side of a state line. These are represented in the source data as separate polygons with tiny gaps between them, and these translate into gaps in the resulting indicator raster. This results from the PARCA digitizing process and does not reflect meaningful differences in priority.Disclaimer: Comparing with Older Indicator VersionsThere 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 CitedAmphibian and Reptile Conservancy. Priority Amphibian and Reptile Conservation Areas (PARCAs). Revised February 7, 2024. Apodaca, Joseph. 2013. Determining Priority Amphibian and Reptile Conservation Areas (PARCAs) in the South Atlantic landscape, and assessing their efficacy for cross-taxa conservation: Geographic Dataset. [https://www.sciencebase.gov/catalog/item/59e105a1e4b05fe04cd000df]. Barrett, Kyle, Nathan P. Nibbelink, John C. Maerz; Identifying Priority Species and Conservation Opportunities Under Future Climate Scenarios: Amphibians in a Biodiversity Hotspot. Journal of Fish and Wildlife Management 1 December 2014; 5 (2): 282–297. [https://doi.org/10.3996/022014-JFWM-015]. Conservation International, International Union for the Conservation of Nature, NatureServe. 2004. Global Amphibian Assessment Factsheet. [https://www.natureserve.org/sites/default/files/amphibian_fact_sheet.pdf]. Environmental Protection Agency. 2014. Mean Amphibian Species Richness: Southeast. EnviroAtlas Factsheet. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/MeanAmphibianSpeciesRichness.pdf]. Erwin, K. J., Chandler, H. C., Palis, J. G., Gorman, T. A., & Haas, C. A. (2016). Herpetofaunal Communities in Ephemeral Wetlands Embedded within Longleaf Pine Flatwoods of the Gulf Coastal Plain. Southeastern Naturalist, 15(3), 431–447. [https://www.jstor.org/stable/26454722]. Sutherland and deMaynadier. 2012. Model Criteria and Implementation Guidance for a Priority Amphibian and Reptile Conservation Area (PARCA) System in the USA. Partners in Amphibian and Reptile Conservation, Technical Publication PARCA-1. 28 pp. [https://parcplace.org/wp-content/uploads/2017/08/PARCA_System_Criteria_and_Implementation_Guidance_FINAL.pdf]. U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch. TIGER/Line Shapefile, 2023, U.S. Current State and Equivalent National. 2023. [https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html].

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator.Input DataSoutheast Blueprint 2023 subregions: CaribbeanSoutheast Blueprint 2023 extentNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee EasementPuerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 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 censusOpenStreetMap data “multipolygons” layer, accessed 3-14-2023A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton 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. TNC Lands - Public Layer, accessed 3-8-2023U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)Mapping StepsMost mapping steps were completed using QGIS (v 3.22) Graphical Modeler.Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.Merge the terrestrial PR and VI PAD-US layers.Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 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.Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.Fix geometry errors in the resulting merged layer using Fix Geometry.Intersect the resulting fixed file with the Caribbean Blueprint subregion.Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.Clip the Census urban area to the Caribbean Blueprint subregion.Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.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 to join the parks to their buffers.Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 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. This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤2% 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: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.Reclassify the polygons into 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).Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.Clip to the Caribbean Blueprint 2023 subregion.As a final step, clip to the spatial extent of Southeast Blueprint 2023. 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 valuesIndicator values are assigned as follows:6 = 75+ acre urban park5 = >50 to <75 acre urban park4 = 30 to <50 acre urban park3 = 10 to <30 acre urban park2 = 5 to <10 acre urban park1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis 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.This indicator includes parks and 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 MindThis 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. The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast because the landcover data available in the Caribbean does not assess percent impervious in a comparable way.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint

The Florida Wiildlife Corridor (layer name Florida_Wildlife_Corridor_2021.shp): This vector layer was created from the original raster grid 2021 version of the Florida Ecological Greenways Network (FEGN) by combining the Priority 1, Priority 2, and Priority 3 values in the raster layer and converting to a shapefile using the Raster to Polygon command with the simplify option to remove the jagged edges of the original raster layer, reduce file size, and the make conversion to a kml file feasible. The Florida Wildlife Corridor is now part of a new state law intended to protect the corridor through enhanced land protection planning and funding. The Florida Wildlife Corridor is defined in the state law as The Florida Wildlife Corridor represents the most important opportunities to protect a functionally connected statewide system of public and private conservation lands essential for protecting Florida's native biodiversity, water resources, and other ecosystem services while providing a sustainable natural resource economy including a variety of resource-based recreational activities.The FEGN guides OGT ecological greenway conservation efforts and promotes public awareness of the need for and benefits of a statewide ecological greenways network. It is also used as the primary data layer to inform the Florida Forever and other state and regional land acquisition programs regarding the location of the most important wildlife and ecological corridors and large, intact landscapes in the state. The FEGN identifies areas of opportunity for protecting a statewide network of ecological hubs (large areas of ecological significance) and linkages designed to maintain large landscape-scale ecological functions including priority species habitat and ecosystem services throughout the state. Inclusion in the FEGN means the area is either part of a large landscape-scale “hub”, or an ecological corridor connecting two or more hubs. Hubs indicate core landscapes that are large enough to maintain populations of wide-ranging or fragmentation-sensitive species including black bear or panther and areas that are more likely to support functional ecosystem services. Highest priorities indicate the most significant hubs and corridors in relation to completing a functionally connected statewide ecological network, but all priority levels have conservation value. FEGN Priorities 1, 2, and 3 are the most important for protecting a ecologically functional connected statewide network of public and private conservation lands, and these three priority levels (P1, P2, and P3) are now called the Florida Wildlife Corridor as per the Florida Wildlife Corridor legislation passed and signed into law by the Florida Legislature and Governor and 2021, which makes protection of these wildlife and ecological hubs and corridors a high priority as part of a strategic plan for Florida’s future. To accomplish this goal, we need robust state, federal, and local conservation land protection program funding for Florida Forever, Rural and Family Lands Protection Program, Natural Resources Conservation Service easements and incentives, federal Land and Waters Conservation Fund, payments for ecosystem services, etc.