Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

CHAT-DC (Data Community) is a PILOT system of state/federal participants, website resources, CHAT data, and software that works on I.T./data management solutions for the greater CHAT group. The state data included in this dataset represent the states in the CHAT-DC and their CHAT submission in a pilot data flow and pilot data access tools.CHAT (Crucial Habitat Assessment Tool) is a spatial dataset and web tool. The data extent is hexagonized (1 sq mi). Each hex has a CRUCIAL habitat rank, among other rankings (Terrestrial Crucial Habitat, Aquatic Crucial Habitat, etc.) “crucial habitat data layer is derived from important habitat and connectivity input layers. The Western Association of Fish and Wildlife Agencies (WAFWA) CHAT is intended to provide coarse-scale, non-regulatory wildlife information to support project pre-planning. CHAT was created by state wildlife agencies across the West, using a common framework to define "Crucial Habitat" using a suite of priority datasets”... ”States given guidelines for habitat prioritization”... “West-wide definitions support compatibility and consistency across state boundaries and address certain discrepancies ”http://www.wafwachat.org".

The Digital Shoals and Underwater Hazard Areas-GIS Map of Chattahoochee River National Recreation Area, Georgia is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (chsh_shoals_and_underwater_hazards.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (chsh_shoals_and_underwater_hazards.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (chsh_shoals_and_underwater_hazards.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (chat_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chat_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (chsh_shoals_and_underwater_hazards_metadata_faq.pdf). Please read the chat_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: GeoCorps of America. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (chsh_shoals_and_underwater_hazards_metadata.txt or chsh_shoals_and_underwater_hazards_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

This data availability layer shows the approximate areas that data has been processed by WAFWA CHAT data providers. The layer is derived from a spatially modifying a copy of the States layer from the old NationalAtlas website, No attribution has been changed from the source layer. The layer will be updated as needed when new data areas are available.

Wauconda Special Service Area Number 1

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Ecological Land Units (ELUs) are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The ELU layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). In 2015 these four components resulted in 3,639 different combinations or ELUs, which is 284 fewer than 2014 which used older land cover and a different landform methodology. Note: This layer is designed for use as a geoprocessing input layer and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service, the legend cannot be used in a meaningful way. Use the World Ecological Land Units Map 2015 tiled map layer for mapping and visualization. These four component datasets represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. Values for each of the four input layers are listed in the table below. BioclimateLandformsLithologyLand CoverArcticPlainsUndefinedBare AreaCold DryHillsUnconsolidated SedimentSparse VegetationCold Semi-DryMountainsCarbonate Sedimentary RockGrassland, Shrub, or ScrubCold Moist Mixed Sedimentary RockMostly CroplandCold Wet Non-Carbonate Sedimentary RockMostly Needleleaf/Evergreen ForestCool Dry EvaporiteMostly Deciduous ForestCool Semi-Dry PyroclasticsSwampy or Often FloodedCool Moist Metamorphic RockArtificial or Urban AreaCool Wet Acidic VolcanicsSurface WaterHot Dry Acidic PlutonicsUndefinedHot Semi-Dry Non-Acidic Volcanics Hot Moist Non-Acidic Plutonics Hot Wet Warm Dry Warm Semi-Dry Warm Moist Warm Wet This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black layer. To use in pop-ups set the transparency to 100% and configure the pop-up. The pop-up from this layer can be combined with the World Ecological Land Units Map. Layers providing access to the four input layers used to create this map see the following links: World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010 The ecophysiographic facets layer is available here and a layer summarizing the local diversity of the ecophysiographic facets is available here. A service is available to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables. The layer was created by the USGS and Esri in 2015. For more information see the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it can not be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This service is a tiled map service version of CHAT CH Rank hosted feature layer. CHAT-DC (Data Community) is a PILOT system of state/federal participants, website resources, CHAT data, and software that works on I.T./data management solutions for the greater CHAT group. The state data included in this dataset represent the states in the CHAT-DC and their CHAT submission in a pilot data flow and pilot data access tools.CHAT (Crucial Habitat Assessment Tool) is a spatial dataset and web tool. The data extent is hexagonized (1 sq mi). Each hex has a CRUCIAL habitat rank, among other rankings (Terrestrial Crucial Habitat, Aquatic Crucial Habitat, etc.) “crucial habitat data layer is derived from important habitat and connectivity input layers. The Western Association of Fish and Wildlife Agencies (WAFWA) CHAT is intended to provide coarse-scale, non-regulatory wildlife information to support project pre-planning. CHAT was created by state wildlife agencies across the West, using a common framework to define "Crucial Habitat" using a suite of priority datasets”... ”States given guidelines for habitat prioritization”... “West-wide definitions support compatibility and consistency across state boundaries and address certain discrepancies ”http://www.wafwachat.org".

Ecophysiographic facets are unique combinations of climate, lithology, landcover, and landform. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online.This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black or white layer. Include this layer in web maps by making it draw 100% transparent.This 2015 map contains updates to the 2014 Ecophysiographic Facets layer in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation). The result is more than twice as many unique facet combinations.Ecophysiographic Facets are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The Ecophysiographic Facets layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). The first three of these inputs (climate, landforms, lithology) represent the primary environmental factors that determine the distribution of living organisms while the fourth (land cover) is vegetation's response to the physical environment.Dataset SummaryThis layer provides access to a 250-m cell-sized raster of unique combinations of climate, lithology, land cover, and landform known as ecophysiographic facets. The layer was created in 2015 by Esri and the USGS. The following layers were used to create this map:World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010A simplified classification of the ecological facets is available in the World Ecophysiographic Land Units layer. A layer summarizing the local diversity of ecophysiographic facets is available here. A service is available providing access to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.The process used to produce this layer is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Ecophysiographic facets are unique combinations of climate, lithology, landcover, and landform. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online.This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black or white layer. Include this layer in web maps by making it draw 100% transparent.This 2015 map contains updates to the 2014 Ecophysiographic Facets layer in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation). The result is more than twice as many unique facet combinations.Ecophysiographic Facets are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The Ecophysiographic Facets layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). The first three of these inputs (climate, landforms, lithology) represent the primary environmental factors that determine the distribution of living organisms while the fourth (land cover) is vegetation's response to the physical environment.Dataset SummaryThis layer provides access to a 250-m cell-sized raster of unique combinations of climate, lithology, land cover, and landform known as ecophysiographic facets. The layer was created in 2015 by Esri and the USGS. The following layers were used to create this map:World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010A simplified classification of the ecological facets is available in the World Ecophysiographic Land Units layer. A layer summarizing the local diversity of ecophysiographic facets is available here. A service is available providing access to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.The process used to produce this layer is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Zones of common emergency response assignments for fire, rescue, police, and medical personnel. Used for E911 response purposes. Data was originally entered using ArcView. Data was entered using heads-up digitizing methods. Shapefile polygons were constructed using the RDS shapefile as the primary basis of reference. In densely developed areas, the tax map drawing files were also used as a supplemental basis of reference. Data was then converted to ArcInfo coverage format and the CLEAN command (using a tolerance of 10 feet) was used to reconstruct polygon topology. Several gaps and sliver polygons resulted from the CLEAN process. These were remedied using the MERGE subcommand found within ArcEdit. Feature attributes were then verified to insure that data had not been lost during the conversion and editing process. After successfully insuring that all data was still resident, the data was converted to Shapefile format for public distribution. In 2013 we shifted our core area parcel base therefore this dataset was shifted and manually corrected to follow updated parcel and road lines. In August 2015 we shifted our parcel base for areas along the Parks Highway from Houston to Talkeetna/Trapper Creek. Therefore this dataset was shifted and manually corrected to follow updated parcel and road lines.

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident ComplexArea Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Trails Layer is designed to provide information about National Forest System trail locations and characteristics to the public. When fully realized, it will describe trail locations, basic characteristics of the trail, and where and when various trail uses are prohibited, allowed and encouraged. Because the data readiness varies between Forests, each Forest will approve which level of attribute subset are published for that forest. Forests can provide no information or one of three attribute subsets describing trails. The attribute subsets include TrailNFS_Centerline which includes the location and trail name and number; TrailNFS_Basic which adds information about basic trail characteristics; and TrailNFS_Mgmt which adds information about where and when users are prohibited, allowed, and encouraged. When a Forest chooses to provide the highest attribute subset, TrailNFS_Mgmt, these attributes must be consistent with the Forest's published Motorized Vehicle Use Map (MVUM). Metadata for the individual Forest feature classes used to compile this feature class are available at data.fs.usda.gov/geodata/edw/dir_trails.php. Metadata

Do not download this parcel map service as a shapefile - you will get an error. To download a zipped file geodatabase, go to this Hub item: https://egis-lacounty.hub.arcgis.com/datasets/parcelsThis map service provides information about properties and parcel boundaries in the County of Los Angeles. The Office of the Assessor (click here for their website) maintains assessment records of real and personal property in the County of Los Angeles, as well as a GIS Tax Parcel Base Map. The Assessor has recently changed its policies and will be releasing a number of datasets publicly over time. They will be available here, as well as on the County’s Open Data Portal (click here to learn more). To access the Property Assessment Information System, where you can search for properties and see maps and imagery, go to the PAIS website.All inquiries should be directed to the Mapping & GIS Services Section, LA County Office of the Assessor at gisinfo@assessor.lacounty.gov

This data represents calls for police services, which are separated by year due to file size, and updated annually. Historical data is available back to 2006. Available fields include:Event Number – Unique numerical identifier of the call for service.Case Number – If an incident report was written, this is the file number, which can be joined to the incidents and arrests datasets.Call Date – Date the call for service was generated.Call Time – Time the call for service was generated.Source – How the call was generated from the public, or self-initiated by an officer.Priority – The priority level of the call response, with ‘P’ being the highest, followed in order numerically.Nature – The description of the call for service.Address – The block number and street or intersection of the call for service.X – Mapping coordinate of the call for service, projected as NC State Plane (feet).Y – Mapping coordinate of the call for service, projected as NC State Plane (feet).District – The patrol district where the call for service was located.Beat – The patrol beat where the call for service was located, which is a sub-division of the district.Disposition – How the call for service was closed.Cancelled – Whether the call for service was cancelled prior to officer arrival.

311 is a City of Cleveland program for reporting complaints and submitting service requests for non-emergency issues, such as potholes or problems with trash pickups. For emergencies, dial 911. This dataset includes 311 requests filed since 2/29/24. All records filed before 2/29/24 are archived. To view this list of archived 311 requests, click here. More information on Cleveland 311 can be found here: https://www.clevelandohio.gov/311. Please note: Prior to the new system launching on 9/4/2024, some dates in the close_date field are after the true date of closure. Update FrequencyDaily around 7:00 AM EST Contacts:311 or Open Data Team (opendata@clevelandohio.gov) Data GlossaryThis dataset is based on the Open311 data standard. Column | Descriptionservice_request_id | The unique ID (reference number) of the service request created in the 311 system. service_name | The human-readable name of the service request type.division_responsible | The Division or operation unit responsible for fulfilling or otherwise addressing the service request. agency_responsible | The Department responsible for fulfilling or otherwise addressing the service request.status_description | A single-word indicator of the current state of the service request.address | Human-readable address or description of location.requested_date | The date and time when the service request was made.updated_datetime | The date and time when the service request was last modified.closed_date | Date and time the request record was closed or cancelled.target_date | The date by which the request is targeted to be addressed by the responsible Department. source | Mechanism or path by which the service request was received.parcelpin | The ID number for the parcel where the request is located.neighborhood | The neighborhood where the request is located.ward_name | The ward where the request is located (full name). Will be updated from 2014 to 2026 wards at the start of the 2026 council term.ward | The ward where the request is located (as a number). Will be updated from 2014 to 2026 wards at the start of the 2026 council term.ward_name_2014 | The 2014 ward where the request is located (full name).ward_2014 | The 2014 ward where the request is located (as a number).ward_name_2026 | The 2026 ward where the request is located (full name).ward_2026 | The 2026 ward where the request is located (as a number).lat | Latitude of the location in decimal degrees.long | Longitude of the location in decimal degrees. Related Data Products:Cleveland 311 Request Explorer Cleveland 311 Service Request Report

Crowd counting from an image is a highly challenging task due to occlusion, low quality, and scale variation of objects. With the development of deep learning techniques, various crowd counting methods have been proposed in response to this challenge. This model uses state-of-the-art method to solve the crowd counting problem.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.Input8 bit, 3-band (RGB) oriented imagery (preferably JPEG, JPG format with resolution less than 2000x2000 pixels).OutputFeature class with the number of classes as count of people.Applicable geographiesThis model is expected to work well in all regions globally. However, results can vary for imagery that are statistically dissimilar to training data.Model architectureThis model is based on the DM-Count model which uses the Distribution Matching for Crowd Counting architecture by Boyu Wang, Huidong Liu, Dimitris Samaras and Minh Hoai.Accuracy metricsThe average PSNR and SSIM over the QNRF test set are 40.65 and 0.55 respectively.Training dataThe model has been trained on the UCF-QNRF dataset.Sample resultsHere are a few results from the model.CitationsH. Idrees, M. Tayyab, K. Athrey, D. Zhang, S. Al-Maddeed, N. Rajpoot, M. Shah, Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds, in Proceedings of IEEE European Conference on Computer Vision (ECCV 2018), Munich, Germany, September 8-14, 2018.