This is a full-day training, developed by UNEP CMB, to introduce participants to the basics of GIS, how to import points from Excel to a GIS, and how to make maps with QGIS, MapX and Tableau. It prioritizes the use of free and open software.

Instructions on how to make an ArcGIS map, add georeferenced points, adjust appearances , configure pop up boxes, upload images and sharing a map. Introduces students to ArcGIS mapping. Students learn how to organize and upload designated places onto an ArcGIS map. Students learn how to configure pop-up boxes for each designated place and populate them with information they have uncovered. Students learn how to add images to their designated places on their maps. Once completed, students learn how to import into other media i.e. StoryMaps, Word documents to tell a bigger story about the places on the map.

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.

GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

Smoothed contours were produced at 2 foot intervals from topographic vector data (breaklines) collected by photogrammetrists. Breaklines denote the major terrain shifts as percieved by viewing the aerial photography stereoscopically. Major breaks, such as the top and bottom of hills were marked with the breaklines. Point data (DTM) was used to supplant the breakline data to provide enough information to model the terrain of the area. The data was collected at scale of 1"= 40'.

Survey field crews surveyed 14 photo identifiable points used for photo control. All the ground control points were used in the final analytical triangulation solution. The horizontal positions were reported in feet; NAD1983 (2011) Massachusetts State Plane Coordinate System, Mainland Zone, Epoch 2010.00. Elevations were based on the NorthAmerican Vertical Datum, 1988.

The aerial photographic mission was carried out on April 12, 2017. 459 exposures were taken in 16 flight lines at 3300' AMT resulting in a pixel resolution of 0.22' . The photography was collected with 60% overlap to ensure proper stereo viewing.

The digital photographs were triangulated using KLT software. The interior orientations of each photo were measured, the photos were tied togther within flight lines and lastly each flight line was tied, creating one single unified block. This block was then projected into Massachusetts State Plane NAD 83 coordinates using the14 aerial photo ground control points that were collected by traditional survey. RMS formulas were used to compute error propagation and reduce error.

The breakline and dtm data collected through the stereocompilation process was edited in KLT Atlas software to check for continuity. A TIN was generated from the edited topographic data which was then used to produce smoothed contours at 2' intervals. The contour information was then checked for errors and converted into AutoCAD .dxf format for GIS import.

This layer contains pre-processed address points for Richland County, North Dakota that can be added to OpenStreetMap.Data SourceThe address points were downloaded in June 2020 from the ArcGIS Hub Open Data site.There are 7,608 total address points. Of these, 7,588 addresses are not already represented as points in OSM and can be added. Features where Import_2_OSM = 1 can be added to OpenStreetMap.1 - no OSM conflict, and can be added2 - address point already exists in OSM and should not be added4 - point has no street address number and should not be added to OSMFeatures with an Import_2_OSM value of 1 (i.e. Import features, shown in Green) have no conflict and can be added to OSM. Features with an Import_2_OSM value of 2 (i.e. No Import features, shown in Red) conflict with existing addresses and should not be added. The layer is currently configured to only display the features that have no conflict and can be added to OSM.OSM Editor ToolsThis layer is accessible through new tools in OSM editors (e.g. updated version of RapiD) that enable OSM mappers to display the features on a map, select an individual feature to inspect its geometry and tags, and then use the feature for editing. OSM mappers should review the individual features and tags, and make any edits and additions that are appropriate, before selecting other features to edit and uploading edits to OSM.

This point layer contains monthly summaries of daily temperatures (means, minimums, and maximums) and precipitation levels (sum, lowest, and highest) for the period January 1981 through December 2010 for weather stations in the Global Historical Climate Network Daily (GHCND). Data in this service were obtained from web services hosted by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc. The data were compiled at Esri from publicly available sources hosted and administered by NOAA. Because the ACIS data is updated and corrected on an ongoing basis, the date of collection for this layer was Jan 23, 2019. The following process was used to produce this dataset:Download the most current list of stations from ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt. Import this into Microsoft Excel and save as CSV. In ArcGIS, import the CSV as a geodatabase table and use the XY Event layer tool to locate each point. Using a detailed U.S. boundary extract the points that fall within the 50 U.S. States, the District of Columbia, and Puerto Rico. Using Python with DA.UpdateCursor and urllib2 access the ACIS Web Services API to determine whether each station had at least 50 monthly values of temperature data for each station. Delete the other stations. Using Python add the necessary field names and acquire all monthly values for the remaining stations. Thus, there are stations that have some missing data. Using Python Add fields and convert the standard values to metric values so both would be present. Thus, there are four sets of monthly data in this dataset: Monthly means, mins, and maxes of daily temperatures - degrees Fahrenheit. Monthly mean of monthly sums of precipitation and the level of precipitation that was the minimum and maximum during the period 1981 to 2010 - mm. Temperatures in 3a. in degrees Celcius. Precipitation levels in 3b in Inches. After initially publishing these data in a different service, it was learned that more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer these most precise coordinates are used. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. If your study area includes areas outside of the U.S., use the World Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer. The data in this layer come from the same source archive, however, they are not curated by the ACIS staff and may contain errors. Revision History: Initially Published: 23 Jan 2019 Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 3,222 of 9,636 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR data is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as: Esri, 2019: U.S. Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed

New-ID: NBI18

The Africa Major Infrastructure and Human Settlements Dataset

Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002

Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename

The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent

References:

ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC.

Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC.

DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC.

Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago

Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa

This GIS data set depicts a combination of the Outer EEZ from NOS sources, and the Inner EEZ from BOEM sources, producing the geographic regulatory boundaries in federal waters, or Magnuson Stevens Act area. Outer EEZ: NOAA's Office of Coast Survey (OCS) is responsible for generating the Three Nautical Mile Line, Territorial Sea, Contiguous Zone, and Exclusive Economic Zone (EEZ). Traditionally, these maritime limits have been generated by hand from the low water line depicted on paper, U.S. nautical charts. Upon final approval by the U.S. Baseline Committee, these legally-binding maritime limits are applied to the next edition of nautical charts produced by the Marine Chart Division of OCS. Due to new cartographic production processes and the availability of digital products such as Electronic Navigational Charts (ENCs), the Office of Coast Survey (OCS) is generating more accurate, digital maritime limits. Through the use of Geographic Information Systems (GIS) software such as CARIS' LOTS and ESRI's ArcGIS, the latest vector representations of these limits will be available to NOAA cartographers and the public. To create digital limits, the charted low water line is digitized from the largest-scale raster nautical charts and used as input to CARIS' LOTS: Limits and Boundaries software for the designation of a baseline. Other parts of the EEZ include maritime boundary agreements and/or unilateral claims as noted in Federal Register Notice, Volume 60, No. 163, Wednesday August 23, 1995. Once the limits are created, they are exported to a shapefile using CARIS' "Import SHP File" utility. Digital limits of the Exclusive Economic Zone for the Atlantic coast of the United States are contained within a zipped file. Within the zipped file is a shapefile and a text file detailing the individual coordinates. Inner EEZ (SLA): The Submerged Lands Act (SLA) of 1953 grants individual States rights to the natural resources of submerged lands from the coastline to no more than 3 nautical miles (5.6 km) into the Atlantic, Pacific, the Arctic Oceans, and the Gulf of Mexico. The only exceptions are Texas and the west coast of Florida, where State jurisdiction extends from the coastline to no more than 3 marine leagues (16.2 km) into the Gulf of Mexico. This data set contains the Submerged Lands Act (SLA) boundary line (also known as State Seaward Boundary (SSB), or Fed State Boundary) in ESRI shapefile formats for the BOEM Atlantic Region. The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. In the BOEM Atlantic Region it is projected 3 nautical miles offshore from the baseline. Further information on the SLA and development of this line from baseline points can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf http://www.boemre.gov/itd/pubs/1999/99-0006.pdf. Due to slight differences in process and purpose, NOAA's 3 nautical mile line depicted on its charts may differ in some areas from the SLA boundary depicted on BOEM maps and OPDs and should not be confused with the SLA boundary. Therefore this boundary is the only boundary that should be used to depict state/federal seperation of jurisdiction for submerged lands. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate and are NOT an OFFICIAL record for the exact Submerged Lands Act Boundary. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs) serve as the legal definition for offshore boundary coordinates and area descriptions.

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

This is a point dataset representing liquefied natural gas import/export terminals in the United States. These are defined as terminals capable of liquefaction of natural gas for transport or receipt and regasification of LNG for use as natural gas. Source: EIA based on Federal Energy Regulatory Commission (FERC), Department of Energy (DOE) Office of Fossil Energy (FE), and trade press. For related data published by EIA see U.S. Liquefaction Capacity (https://www.eia.gov/naturalgas/U.S.liquefactioncapacity.xlsx).

QR code interactive map for DAQClick on a quadrangle to display a QR code. Use the QR code download function of Avenza maps to download the map automatically:1. open PDF Maps on your device.2. tap the + icon to import a map3. tap the square QR icon in the top right4. point the device at the screen and center it on the QR code. The map will begin downloading automatically as soon as the camera can focus on the code

QR code interactive map for DMRClick on a quadrangle to display a QR code. Use the QR code download function of Avenza maps to download the map automatically:1. open PDF Maps on your device.2. tap the + icon to import a map3. tap the square QR icon in the top right4. point the device at the screen and center it on the QR code. The map will begin downloading automatically as soon as the camera can focus on the code.

QR code interactive map for AMLClick on a quadrangle to display a QR code. Use the QR code download function of Avenza maps to download the map automatically:1. open PDF Maps on your device.2. tap the + icon to import a map3. tap the square QR icon in the top right4. point the device at the screen and center it on the QR code. The map will begin downloading automatically as soon as the camera can focus on the code

This feature class represents Petroleum Ports. This includes ports in the 50 states and the District of Columbia that handle 200 or more short tons per year in total volume (import and export) of petroleum products (URL: http://www.eia.gov/maps/layer_info-m.cfm). Generally, a Petroleum Port is any maritime port which has facilities used to import or export any type of petroleum product. Typically, there are multiple petroleum facilities present in any port therefore the point identifying the location is an abstracted location representing the port as a whole. Geographical coverage includes the United States and its territories including Puerto Rico, Virgin Islands, Guam, Northern Mariana Islands, American Samoa, as well as Petroleum Ports in Canada and Mexico that are within 100 miles of each countries border with the United States.

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

Participatory PlanningAn interactive 3D web application enabling citizens to engage in urban planning, using the ArcGIS API for JavaScript. This is a non-commercial demo application made by the Esri R&D Center Zurich. It is intended for presentations or as a starting point for new projects.The app uses various API features such as 3D drawing, glTF import and client-side filtering. The example scene used in the app is located in Dumbo, Brooklyn NY. On the technical side the app is built using TypeScript, npm and webpack.