Join the us for a University of California Mapathon as a part of UC GIS Week. This mapping event will support the Missing Maps project. Missing Maps asks for volunteers worldwide to map vulnerable areas before disaster or crisis strikes to reduce risk and speed recovery efforts. We’ll trace buildings and roads from satellite images, for example; then, humanitarian groups on the ground will add the details and create maps with our data.Each year, disasters around the world affect or displace 200 million people. Many of the places where these disasters occur are literally ‘missing’ from open and accessible maps and first responders lack the information to make valuable decisions regarding relief efforts.

New Zealand Esri User Conference 2024 Terms and Conditions

All Metrobus routes and schedules are changing as a result of the Better Bus Initiative. Better Bus is Metro’s initiative to improve regional bus service. On June 29, 2025, Metro will launch its new bus network. DC's Chief Technology Office (OCTO) is working with partners at DDOT and WMATA to post changes to Open Data DC. Please read WMATA notice here.

When rain falls over land, a portion of it runs off into stream channels and storm water systems while the remainder infiltrates into the soil or returns to the atmosphere directly through evaporation. Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow. For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55). Dataset SummaryPhenomenon Mapped: Soil hydrologic groupGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic group is derived from the gSSURGO map unit aggregated attribute table field Hydrologic Group - Dominant Conditions(hydgrpdcd). The seven classes of hydrologic soil group followed by definitions:Group A - Group A soils consist of deep, well drained sands or gravelly sands with high infiltration and low runoff rates.Group B - Group B soils consist of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of infiltration and runoff.Group C - Group C consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of infiltration.Group D - Group D consists of soils with a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a clay pan or clay layer at or near the surface, and soils that are shallow over nearly impervious material.Group A/D - Group A/D soils naturally have a very slow infiltration rate due to a high water table but will have high infiltration and low runoff rates if drained.Group B/D - Group B/D soils naturally have a very slow infiltration rate due to a high water table but will have a moderate rate of infiltration and runoff if drained.Group C/D - Group C/D soils naturally have a very slow infiltration rate due to a high water table but will have a slow rate of infiltration if drained. What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe 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 selectingAddthenBrowse Living Atlas Layers. A window will open. Type "soil hydrologic group" in the search box and browse to the layer. Select the layer then clickAdd to Map. In ArcGIS Pro, open a map and selectAdd Datafrom the Map Tab. SelectDataat the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expandPortalif necessary, then selectLiving Atlas. Type "soil hydrologic group" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Safe, secure, and abundant bicycle parking is necessary to support the District’s growing number of people using bikes for transportation, commuting, and recreation. Providing sufficient bicycle parking is part of DDOT's strategy to promote bicycling in the District of Columbia and reduces the number of bikes locked to trees, benches, and railings.

The NOAA Climate Prediction Center generates monthly to seasonal forecasts of the probability of temperature and precipitation being greater or less than "normal" (the historical average). These layers show the probability of whether temperatures in the United States will be above or below normal over the next 6-10 days, 8-14 days, 30 days, and 90 days ("season"). TemperatureThe color palette uses orange to blue to represent higher or lower than average temperature probability. Dark orange colors imply a greater probability that temperatures will be higher than normal - but the color implies nothing about the actual amount. In other words, dark orange does not mean that these areas will receive higher temperatures than light orange areas, just a greater chance that the areas will be warmer than normal.The National Weather Service maintains a variety of ArcGIS services via their CloudGIS system. The REST endpoints for all of these services can be found here: https://www.weather.gov/gis/cloudgiswebservices

The segregation of public schools was a common practice throughout many decades of U.S. history. During the Reconstruction period after the Civil War, Congress passed three constitutional amendments to protect newly-freed Black Americans. While racial segregation was forbidden in some regions, in other areas, namely the southern states, racial segregation was enforced by law. The transition to a more diverse and inclusive public education system was fueled by families, activists, and the NAACP who used litigation to make sure that all students were afforded access to an equitable education. On May 17, 1954, the Supreme Court of the United States ruled segregation in public schools was unconstitutional in Brown v. Board of Education. The ruling was a pivotal point students, educators, and the ongoing fight for civil rights.

This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data, buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Wetland mapping conducted in WA, OR, CA, NV and ID after 2012 and most other projects mapped after 2015 were mapped to include all surface water features and are not derived data. The linear hydrography dataset used to derive Version 2 was the U.S. Geological Survey's National Hydrography Dataset (NHD). Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class. Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. This dataset should be used in conjunction with the Wetlands_Project_Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.

This data set is a Status Map that identifies the location of wetland data and no data areas. The wetland data itself represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Trust Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). Certain wetland habitats may be excluded because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition.The U.S. Fish and Wildlife Service (Service) is the principal Federal agency that provides information to the public on the extent and status of the Nation's wetlands. The Service's strategic plan for our vast national wetland data holdings is focused on the development, updating, and dissemination of wetlands data and information to Service resource managers and the public. The development of the Wetlands Master Geodatabase is in direct response to the need to integrate digital map data with other resource information to produce timely and relevant management and decision support tools.For more information visit: https://www.fws.gov/wetlands/index.htmlView Wetlands Data on the Wetlands Mapper at: https://www.fws.gov/wetlands/Data/Mapper.htmlWetlands Web Services are available at: https://www.fws.gov/wetlands/Data/Web-Map-Services.htmlWetlands Data available as a KML at: https://www.fws.gov/wetlands/Data/Google-Earth.htmlWetlands Data Downloads available at: https://www.fws.gov/wetlands/Data/Data-Download.htmlWetland Data Standards available at: https://www.fws.gov/wetlands/Data/Data-Standards.html

This Dashboard presents results from Hoffman et al. (2020) which showed that formerly "redlined" neighborhoods are predominantly warmer today than their non-redlined neighbors in 94% of the cities studied. This relationship is accompanied by a similar, although opposite, trend in tree canopy, whereby redlined neighborhoods have systematically less tree canopy today - and more impervious, hard surfaces - than their non-redlined neighbors. Finally, we have included estimates of the neighborhood demographics - indicated by its % non-white population and median house value - to show that, as many studies have shown previously, that these formerly redlined areas remain relatively lower-resourced and primarily communities of color, underscoring the need to address climate change equitably in these cities which were redlined in the 1930s and 1940s.The web map for this Dashboard can be accessed here.

Summary:The Fire Event Data Suite, or FEDS, algorithm uses high resolution VIIRS observations to map fire perimeters, identify the active portion of fire fronts, and track the progression and attributes of individual fires every 12 hours. For individual fire events, FEDS contains information on the latest active fire detections, as well as the total fire event history in 12 hour increments.Suggested Usage:Perimeter data are helpful for understanding the time series progression of a fire event, as observed via the VIIRS sensor. Given the source data, the same considerations that must be taken for FIRMS active fire data are applicable here. All data are experimental and should always be verified with supplementary sources of information when available.Date of Next Image:Updates available at approximate 12-hour intervals.Satellite/Sensor:Suomi NPP and NOAA-20 satellites carrying the VIIRS sensor. The FEDS algorithm uses the locations and sizes of each pixel to derive perimeter information and track individual fire events.Resolution:375m at nadirCredits:NASA Earth Information System (EIS)Doug Morton, Melanie Follette-Cook, Elijah Orland, Tempest McCabe (all GSFC), Yang Chen (UC Irvine)Scientific PaperEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/chile_wildfires_202402/EIS_FEDS/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/chile_wildfires_202402/ChileNRT/