Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service. The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3) Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022 ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/ This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster. The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4 What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.

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.

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.

Notice: this is not the latest Heat Island Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

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).

Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The Protected Areas Database of the United States provides a comprehensive map of lands protected by government agencies and private land owners. This database combines federal lands with information on state and local government lands and conservation easements on private lands to create a powerful resource for land-use planning. Dataset SummaryPhenomenon Mapped: Areas mapped in the Protected Areas Data base of the United States (GAP Status 1-4) Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022 ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/ This layer displays lands mapped in Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays all four GAP Status classes: GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protection The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster. The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Protected from Land Cover ConversionUSA Unprotected AreasUSA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4

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

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) Regional Grid Shapefiles and Raster used in Thermal Quality Analysis task of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. Polygon (Fishnet2.shp and associated files), Point (Fishnet2_label.shp and associated files) and Raster grid (GridNAD.tif) are included, made using ArcGIS Create Fishnet tool.

There is an associated file containing the ArcGIS Toolbox with the Regional Grid Models, (ArcGISToolbox_RegionalGridModels.zip) .

The shapefiles, ArcGIS toolbox, and R script contained within these two .zip files were used to convert vector and raster files to the standardized 1 square km grid used in this project. The code is general enough to be used in other studies that may need to work on a standard grid. ArcGIS 10.1 or later is needed to use the models in the toolbox.

Details regarding methods for seismic risk factor conversion (within the toolbox) may be found in the memo contained within the project final report entitled 14_GPFA-AB_SeismicRiskMapCreationMethods.pdf (Smith and Horowitz, 2015).

The R script AddNewSeisFieldsFunctions.R implements some of the methods described in the memo.

Details about all of the ArcGIS toolbox models may be found in the memo entitled 16_GPFA-AB_RiskAnalysisAndRiskFactorDescriptions.pdf (Whealton, et al., 2015). Some models have been given different names since the memo was written. These models have the former names listed next to the current model name in the list above.

surficialDL: A geomorpholgy deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA

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).

surficialDL The digital terrain model associated with these data/project is available here: https://s3.us-east-1.amazonaws.com/download.massgis.digital.mass.gov/lidar/LIDAR_DEM_32BIT_FP.gdb.zip. alluvDL: polygons (vectors folder) and extents (extents folder) for alluvium features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.
tillDL: polygons (vector folder) and extents (extents folder) for thick till features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.

Datasets used in: Young, A.M., Higuera, P.E., Duffy, P.A., and F.S. Hu. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. In Review at Ecography as of 10/2015.

----------------------- Description ----------------------------------

These data are comprised of 60 individual GeoTIFF files that indicate whether fire is estimated to haved occurred in a given pixel for a given year. Each map is representative of one calendar year. These data are gridded versions of the vector geographic data provided by the Alaska Interagency Coordination Center (AICC; http://fire.ak.blm.gov/). Converting the polygon (i.e., vector) datasets to raster was done using ESRI software in ArcMap 10.1. Specifically, we used the function "Feature to Raster" under the Conversion Toolbox. A given pixel was classified as "fire occurrence" if the center of the pixel overlapped

with a fire polygon from the AICC dataset.

----------------------- Resolution -----------------------------------

Spatial Resolution: 2 km Temporal Resolution: Annual

Time Coverage: 1950-2009

------------------------ File Naming ---------------------------------

'fire' - fire occurrence data. 1 = fire occurrence -999 = No Data/No Fire '_xxxx' - year (1950-2009)

'.tif' - file extention

------------------ Geographic Information ----------------------------

Rows: 725 Columns: 687 Spatial Extent (in meters) - - Top: 2390439.786 - Left: -656204.44 - Right: 717795.56 - Bottom: 940439.786 Spatial Reference: Albers Equal Area Datum: North American 1983 False Easting: 0 False Northing: 0 Central Meridian: -154 Standard Parallel 1: 55 Standard Parallel 2: 65 Latitude of Origin: 50

These data are comprised of 60 individual GeoTIFF files that indicate whether fire is estimated to haved occurred in a given pixel for a given year. Each map is representative of one calendar year. These data are gridded versions of the vector geographic data provided by the Alaska Interagency Coordination Center (AICC; https://fire.ak.blm.gov/ ). Converting the polygon (i.e., vector) datasets to raster was done using ESRI software in ArcMap 10.1. Specifically, we used the function "Feature to Raster" under the Conversion Toolbox. A given pixel was classified as "fire occurrence" if the center of the pixel overlapped with a fire polygon from the AICC dataset. File Naming 'fire' - fire occurrence data 1 = fire occurrence -999 = No Data/No Fire '_xxxx' - year (1950-2009) '.tif' - file extention

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.

Land use/land cover is a reflection of the important parameters of surface State, many geographic and ecological studies of basic parameters. This data set to Landsat8 OLI and sunny or partly cloudy (cloud cover less than 10%) remote sensing images for the data source, using the ArcGIS software interpretation based on data availability partition. Land use/land cover to the object-oriented classification method. Multi-scale segmentation with eCognition software support, select spatial, spectral, textures, shapes, features, computers automatically based on rules set classification classification and artificial Visual modifications and similar methods. Using Arc/Info AML language vector-raster conversion , using ArcGIS software for stitching and trimming the data in the study area, the final raster data set. Data overall accuracy and Kappa coefficient of 85.4% and 0.807 respectively.

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.

This vegetation map shows the extant distributions of vegetation formations and classes throughout NSW, and provides users with information about the resolution, currency and uncertainties in the underlying data that were used to assemble the map. Data represents NSW native vegetation extent, compiled from various vegetation maps using methods outlined in Simpson et al. (2011) and in Keith and Simpson (2010). The NSW vegetation map (version 2.2, Keith and Simpson 2006) was revised by interpreting additional candidate maps as vector layers and synthesising these into a single raster-based data set. This involved eight steps: developing a comprehensive ‘standard’ classification of vegetation classes for NSW; collating and standardising the projection and format of candidate source maps; assigning vegetation units of source maps to NSW vegetation classes; assessing the spatial resolution, currency and reliability of candidate source maps; assembling a composite map from candidate source maps to maximise reliability; applying a spatial mask to represent extant native vegetation; adjusting spatial resolution by dissolving small polygons and converting to 200 m raster; attributing the spatial resolution, currency and reliability of the underlying source data sets. The classification of 106 vegetation classes described by Keith (2004) was adopted as the framework for preparation of version 3.03 of the NSW vegetation map. Polygons from the “Estuarine macrophytes CCA” dataset of less than 0.1 ha were eliminated. For all other datasets polygons of less than 2 ha were eliminated. The map incorporates data from a statewide woody vegetation mask from the NSW Woody Vegetation Change Detection Program (Kitchen et al. 2010). The map is presented as a raster within an ESRI ArcGIS (9.3) geodatabase. Supersedes Keith and Simpson (2006), Keith (2004) and Pressey et al. (2000). Pressey et al. (2000) was the native veg extent product used to calculate native veg cover values for the Over-Cleared Landscapes Database prior to July 2006. References: Keith D. A. (2004) Ocean Shores to Desert Dunes: The native vegetation of New South Wales and the ACT. Department of Environment and Conservation, Sydney. Keith, D. A. and Simpson, C. C. (2010) Vegetation Formations of NSW (version 3.0): A seamless map for modelling fire spread and behaviour. Report to the Rural Fire Service. NSW Department of Environment and Climate Change. October 2010. Keith, D. A. and Simpson, C. C. (2006). A protocol for assessment and integration of vegetation maps, with an application to spatial data sets from south-eastern Australia. Austral Ecology 33, 761–774. Pressey, R.L., Hager, T.C., Ryan, K.M., Schwarz, J., Wall, S., Ferrier, S. and Creaser, P.M. (2000). Using abiotic data for conservation assessments over extensive regions: quantitative methods applied across New South Wales, Australia. Biological Conservation 96, 55-82

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.

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

This is a combined feature class from 158 individual line feature classes derived from flow accumulation rasters for each of the analysis area tiles. Stream networks were generated using the ArcGIS Spatial Analyst Derive Continuous Flow tool against each DEM tile, converting to stream rasters with Strahler Order network segment values, then clipping the result to analysis area polygons to derive stream network network raster with a minimum segment watershed size of 2500 sq ft. The stream raster for each tile was exported to a vector Line feature class, combined into a single feature class. Each segment was provided with a Mounded Landform polygon ID, and statistics for segment length and total streamline length were computed and conflated to HydroAnalysis table fields for analyzing stream statistics.

Status: UPDATED occasionally using ArcMap Contact: Matt Berube, City of Tucson Planning and Development Services, 520-837-5323, Matthew.Berube@tucsonaz.gov (Creator: Josh Pope, 2006-2009)Intended Use: For map display Lineage: Created in December 2011 from dist_hist_footprints and dist_hist_SHPO_pointsSupplemental Information: This data was built from access database of National Register structures from Arizona State Historic Preservation Office. The records from the SHPO database were geocoded. This point layer was extensively research in the spring and summer of 2009. Additional districts and geometry added in 2010-2011, and additional data researched and reorganized. Original maps, National Register Historic District documents' property lists, and change of status letters were used to verify the location and status of each record. The geometry of the footprints were originally derived from multiple sources. Manually digitized from 2008 aerial photography, conversion of layered pdf's to dwg's that were rubber sheeted to align with the 2008 aerials, pgdb or dwg's from the consultant, or raster to vector conversion of scanned hard copy maps for Sam Hughes N.R. District. Use [HDReg_Status] field to determine if a structure is contributing to a National Register Historic District ("Contributor"). Use [ILReg_Status] field to determine if a structure is Individually Listed on the National Register of Historic Places ("Listed") or is Eligible to be listed ("Eligible"). Use [Reg_Status] field to determine the National Register status of a structure.(from original dist_hist_footprints): The geometry of the footrpints were derived from multiple sources. Manually digitized from 2008 aerial photography, conversion of layered pdf's to dwg's that were rubber sheeted to align with the 2008 aerials, pgdb or dwg's from the consultant, or raster to vector conversion of scanned hard copy maps for Sam Hughes N.R. District. Status derived from dist_hist_SHPO_points. Use [Contributi] field to determine if a strcuture is a contributing to a National Register Historic District ("Yes"). Use [On_Register] field to determine if a structure is Individually Listed on the Register ("On_Reg") or is Eligible to be listed ("Eligible") Dec 2009 - loaded shapefile into EDITSDE geodatabase (from original dist_hist_SHPO_point layer): This data was built from access database of National Register structures from Arizona State Historic Preservation Office. The records from the SHPO database were geocoded. This point layer was extensively research in the spring and summer of 2009. Original maps and National Register District documents property lists, and change of status letters were used to verify the location and status of each record. There are a number of records that could require additional research, the site survey form, to determine the location and or status. These records are identified in the [Status_QC] field with the attribute of "QQQ". Use [Reg_Status] field to determine the National Register status of a structure. Dec 2009 - loaded shapefile into EDITSDE geodatabase

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.