You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

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.

SSURGO-QA ArcGIS Pro Toolbox1. SetupDownload SSURGO by Areasymbol - Use Soil Data Access and Web Soil Survey download page to get SSURGO datasets. User can a wildcard to query the database by Areasymbol or by age.Download SSURGO by Region - Downloads SSURGO Soil Survey Areas that are owned by a specific region including an approximiate 2 soil survey area buffer.Generate Regional Transactional Geodatabase - Used to create the Regional Transactional Spatial Database (RTSD) for SSURGO.Generate SSO SSURGO Datasets - Create a SSURGO file geodatabase for a selected MLRA Soil Survey Office.Import SSURGO Datasets in FGDB - This tooll will import SSURGO spatial and tabular datasets within a given location into a File Geodatabase and establish the necessary table and feature class relationships to interact with the dataset.Insert NATSYM and MUNAME Value - This tool adds the National Mapunit Symbol (NATMUSYM) and the Mapunit Name (MUNAME) values to the corresponding MUKEY. An MUKEY field is required to execute. A network connection is required in order to submit a query to SDacess.RTSD - Check SDJR Project Out - Designed to work with the RTSD to manage SDJR projects and export data for those projects to be sent to the MLRA SSO.

Point feature class and related table containing the Precise Surveys measurement time series. Measurements include elevations, Northings and Eastings, distances, and point-to-point measurements. Northing and Easting measurements are in CA State Plane Coordinate systems, Elevations measurements are provided in NAVD88 or NGVD29. This dataset is for data exploration only. These measurements and point locations are not considered survey-grade since there may be nuances such as epochs, adjustments, and measurement methods that are not fully reflected in the GIS data. These values are not considered authoritative values and should not be used in-lieu of actual surveyed values provided by a licensed land surveyor. Related data and time series are stored in a table connected to the point feature class via a relationship class. There may be multiple table entries and time series associated to a single mark. Data was assembled through an import of Excel tables and import of mark locations in ArcGIS Pro. Records were edited by DOE, Geomatics, GDSS to resolve any non-unique mark names. This dataset was last updated 4/2024.

Deoxygenation of the oceans is one of the most important issues in oceanography today. Using dissolved oxygen measurements taken at various depths in Monterey Bay, California, you'll perform a 3D geostatistical interpolation to predict the oxygen levels throughout the entire bay.In this lesson you will build skills in these areas: - Charting data with histograms and scatter plots in ArcGIS Pro- Interpolating 3D measurements- Comparing 2D interpolation- Exporting rasters - Creating a 3D animationLearn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

Want to keep the data in your Hosted Feature Service current? Not interested in writing a lot of code?Leverage this Python Script from the command line, Windows Scheduled Task, or from within your own code to automate the replacement of data in an existing Hosted Feature Service. It can also be leveraged by your Notebook environment and automatically managed by the MNCD Tool!See the Sampler Notebook that features the OverwriteFS tool run from Online to update a Feature Service. It leverages MNCD to cache the OverwriteFS script for import to the Notebook. A great way to jump start your Feature Service update workflow! RequirementsPython v3.xArcGIS Python APIStored Connection Profile, defined by Python API 'GIS' module. Also accepts 'pro', to specify using the active ArcGIS Pro connection. Will require ArcGIS Pro and Arcpy!Pre-Existing Hosted Feature ServiceCapabilitiesOverwrite a Feature Service, refreshing the Service Item and DataBackup and reapply Service, Layer, and Item properties - New at v2.0.0Manage Service to Service or Service to Data relationships - New at v2.0.0Repair Lost Service File Item to Service Relationships, re-enabling Service Overwrite - New at v2.0.0'Swap Layer' capability for Views, allowing two Services to support a View, acting as Active and Idle role during Updates - New at v2.0.0Data Conversion capability, able to invoke following a download and before Service update - New at v2.0.0Includes 'Rss2Json' Conversion routine, able to read a RSS or GeoRSS source and generate GeoJson for Service Update - New at v2.0.0Renamed 'Rss2Json' to 'Xml2GeoJSON' for its enhanced capabilities, 'Rss2Json' remains for compatability - Revised at v2.1.0Added 'Json2GeoJSON' Conversion routine, able to read and manipulate Json or GeoJSON data for Service Updates - New at v2.1.0Can update other File item types like PDF, Word, Excel, and so on - New at v2.1.0Supports ArcGIS Python API v2.0 - New at v2.1.2RevisionsSep 29, 2021: Long awaited update to v2.0.0!Sep 30, 2021: v2.0.1, Patch to correct Outcome Status when download or Coversion resulted in no change. Also updated documentation.Oct 7, 2021: v2.0.2, workflow Patch correcting Extent update of Views when Overwriting Service, discovered following recent ArcGIS Online update. Enhancements to 'datetimeUtil' Support script.Nov 30, 2021: v2.1.0, added new 'Json2GeoJSON' Converter, enhanced 'Xml2GeoJSON' Converter, retired 'Rss2Json' Converter, added new Option Switches 'IgnoreAge' and 'UpdateTarget' for source age control and QA/QC workflows, revised Optimization logic and CRC comparison on downloads.Dec 1, 2021: v2.1.1, Only a patch to Conversion routines: Corrected handling of null Z-values in Geometries (discovered immediately following release 2.1.0), improve error trapping while processing rows, and added deprecation message to retired 'Rss2Json' conversion routine.Feb 22, 2022: v2.1.2, Patch to detect and re-apply case-insensitive field indexes. Update to allow Swapping Layers to Service without an associated file item. Added cache refresh following updates. Patch to support Python API 2.0 service 'table' property. Patches to 'Json2GeoJSON' and 'Xml2GeoJSON' converter routines.Sep 5, 2024: v2.1.4, Patch service manager refresh failure issue. Added trace report to Convert execution on exception. Set 'ignore-DataItemCheck' property to True when 'GetTarget' action initiated. Hardened Async job status check. Update 'overwriteFeatureService' to support GeoPackage type and file item type when item.name includes a period, updated retry loop to try one final overwrite after del, fixed error stop issue on failed overwrite attempts. Removed restriction on uploading files larger than 2GB. Restores missing 'itemInfo' file on service File items. Corrected false swap success when view has no layers. Lifted restriction of Overwrite/Swap Layers for OGC. Added 'serviceDescription' to service detail backup. Added 'thumbnail' to item backup/restore logic. Added 'byLayerOrder' parameter to 'swapFeatureViewLayers'. Added 'SwapByOrder' action switch. Patch added to overwriteFeatureService 'status' check. Patch for June 2024 update made to 'managers.overwrite' API script that blocks uploads > 25MB, API v2.3.0.3. Patch 'overwriteFeatureService' to correctly identify overwrite file if service has multiple Service2Data relationships.Includes documentation updates!

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Reason for SelectionProtected 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 3.0national 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