This feature service is derived from the Esri "United States Zip Code Boundaries" layer, queried to only CA data.For the original data see: https://esri.maps.arcgis.com/home/item.html?id=5f31109b46d541da86119bd4cf213848Published by the California Department of Technology Geographic Information Services Team.The GIS Team can be reached at ODSdataservices@state.ca.gov.U.S. ZIP Code Boundaries represents five-digit ZIP Code areas used by the U.S. Postal Service to deliver mail more effectively. The first digit of a five-digit ZIP Code divides the United States into 10 large groups of states (or equivalent areas) numbered from 0 in the Northeast to 9 in the far West. Within these areas, each state is divided into an average of 10 smaller geographical areas, identified by the second and third digits. These digits, in conjunction with the first digit, represent a Sectional Center Facility (SCF) or a mail processing facility area. The fourth and fifth digits identify a post office, station, branch or local delivery area.As of the time this layer was published, in January 2025, Esri's boundaries are sourced from TomTom (June 2024) and the 2023 population estimates are from Esri Demographics. Esri updates its layer annually and those changes will immediately be reflected in this layer. Note that, because this layer passes through Esri's data, if you want to know the true date of the underlying data, click through to Esri's original source data and look at their metadata for more information on updates.Cautions about using Zip Code boundary dataZip code boundaries have three characteristics you should be aware of before using them:Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.As of early 2025, USPS appears to be in the process of releasing boundaries, which will at least provide an authoritative source, but because of the other factors above, we do not recommend these boundaries for many use cases. If you are using these for anything other than mailing purposes, we recommend reconsideration. We provide the boundaries as a convenience, knowing people are looking for them, in order to ensure that up-to-date boundaries are available.

Mature Support Notice: This item is in mature support as of July 2021. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This web map contains the same layers as the 'Imagery with Labels' basemap that is available in the basemap gallery in the ArcGIS applications but also adds the World Transportation map serviceThe World Transportation map service shows streets, roads and highways and their names. When you zoom in to the highest level of detail the lines disappear and you just see the street names and road numbers. The 'Imagery with Labels' basemap contains the World Imagery map service and the World Boundaries and Places map service, so when you use that basemap you get boundaries and places, but you don't get streets and roads at small scales or street and road labels at large scale. So by adding the World Transportation map service into your map as well you get those too.Want to use this map as the basemap for your own web map? If you have not created your web map yet, simply open this map and then do Save As to save a copy of it as your own map, and then make changes to it like zooming in and adding more data. If you have already created your web map, open it and choose the Imagery With Labels basemap from the Basemap dropdown. Then add the World Transportation service into your map by searching for it. This 'Imagery with Labels and Transportation' web map shows you what this looks like. The World Transportation map service is designed to be drawn underneath the World Boundaries and Places map service, as you can see in this web map.In this web map, we have set the Transportation layer with partial transparency to make the transportation network less prominent relative to the imagery. You can manipulate the level of transparency that you use for the basemap and reference layers in the web maps that you create. You can do this in the layer properties of the layers in the map table of contents.Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates. Tip: This web map is a useful general purpose map that you can link to from web pages, emails, social media, etc, and embed in your own web page. Just open the map and then choose the Share option. Like with any public map in ArcGIS Online, you don't need to have an ArcGIS Online account in order to share this map by linking or embedding. In addition, by adding extent parameters in the URL you use to link or embed the map, you can take users directly to particular locations. So anyone can immediately take advantage of this map on the web to show any location in the world without even being signed in to ArcGIS Online. See this help topic for more information. For example, here are some links that use extent parameters to open this map at some famous locations. Some of these specify a rectangular extent on the map to zoom to. Others specify a center point and a zoom level to zoom to:Grand Canyon, Arizona, USAGolden Gate, California, USATaj Mahal, Agra, IndiaVatican CityBronze age white horse, Uffington, UKUluru (Ayres Rock), AustraliaMachu Picchu, Cusco, PeruOkavango Delta, Botswana

Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What 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 selecting Add then Browse Living Atlas Layers. A window will open. Type "Watershed Boundary Dataset" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.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.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What 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 selecting Add then Browse Living Atlas Layers. A window will open. Type "subsidence" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "subsidence" in the search box, browse to the layer then click OK.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.

Here is a direct link to a pristine ArcGIS Online web map with this basemap all loaded and ready to go; a blank canvas!Blank?Did you ever make a sweet map with loads of layers and data and the whole of it was so information-dense that the basemap behind it all was just unnecessary noise? Me too. Nothing against basemaps—they serve a noble purpose of providing spatial context to whatever phenomenon you are mapping. But sometimes your data is sufficient to make a basemap superfluous. Here is a little hack that you can use. A basemap to end all basemaps, as it were. Tile after tile of lightning-fast lightweight nothingness. It's like the Seinfeld of basemaps. A basemap about nothing.HELLLLLLLLLOOOOOOOOOOO! LA LA LAAAAA!Here is a picture of this basemap:Pretty nice, right? Ok, if you want a different color no problem. Here is a link to the vector tile style editor, which will let you paint this big blank canvas whatever color you like: https://developers.arcgis.com/vector-tile-style-editor/e3ac9818c0c344538840e51e9f33f6cc/layersHappy Minimalist Mapping! John Nelson

Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What 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 selecting Add then Browse Living Atlas Layers. A window will open. Type "Watershed Boundary Dataset" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.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.

Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What 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 selecting Add then Browse Living Atlas Layers. A window will open. Type "Watershed Boundary Dataset" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.

Source: NASA LANCE - VNP14IMG_NRT active fire detection - World
Scale/Resolution: 375-meter
Update Frequency: Hourly using the aggregated live feed methodology
Area Covered: World

What can I do with this layer?
This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.

The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.

Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.

Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.

VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.

The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.

Attribute information

• Latitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.
• Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.
• Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: D = Daytime fire, N = Nighttime fire
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest 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 efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map 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!

Source data found here: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServerEach drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological SurveyPublication Date: January 7, 2025What 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 selecting Add then Browse Living Atlas Layers. A window will open. Type "Watershed Boundary Dataset" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

The Watershed Boundary Dataset (WBD), found in the A-16 National Geospatial Data Asset Portfolio, defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area will be composed of multiple smaller drainage areas. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. It is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds.This Web Map Service (WMS) displays line features of 2-digit, 4-digit, 6-digit, 8-digit, 10-digit, 12-digit and 14-digit Hydrologic Units (HU). The Open Geospatial Consortium, WMS specification is an international specification for serving and consuming dynamic maps on the web. These services are useful if you want to make your maps available online in an open, recognized way across different platforms and clients. Any client built to support the WMS specification can view and work with your service.Thumbnail image courtesy of: Eugene Kim

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the August 2025 release

• Nationally, the August 2025 update of G-NAF shows an overall increase of 40,716 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,794,643 of which 14,950,491 or 94.66% are principal.

• In the ACT, there have been minor updates to the address parsing of flat-numbered addresses aimed at: improving the address representation of flat-numbered addresses; improving address coverage; and improving address alignment between contributors. This change affects approximately 4,000 addresses.

• A small number of additional address sites have implemented the use of the BUILDING_NAME attribute as part of the merge criteria to improve address coverage for flat-numbered addresses in NSW and QLD. These changes have resulted in the creation of approximately 400 addresses in NSW and 120 in QLD.

• A focus has been applied to Tasmanian street-locality addresses to reduce the number of these addresses. For the August 2025 release, there is a reduction of some 900 street-locality addresses in Tasmania.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

Use this regional model layer when performing analysis within a single continent. This layer displays a single global land cover map that is modeled by region for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

This feature layer provides access to OpenStreetMap (OSM) shops data for Asia, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes shop features defined as a query against the hosted feature layer (i.e. shop is not blank).In OSM, a shop is a place selling retail products or services, such as a supermarket, bakery, or florist. These features are identified with a shop tag. There are thousands of different tag values for shop used in the OSM database. In this feature layer, unique symbols are used for several of the most popular shop types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Neighborhood level or 1:80k scale) to see the shop features display. You can click on the feature to get the name of the shop. The name of the shop will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this shop layer displaying just one or two shop types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. shop is jewelry), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. supermarket or convenience shop) that are ready to use, but not for every type of shop.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

Blank?Did you ever make a sweet map with loads of layers and data and the whole of it was so information-dense that the basemap behind it all was just unnecessary noise? Me too. Nothing against basemaps—they serve a noble purpose of providing spatial context to whatever phenomenon you are mapping. But sometimes your data is sufficient to make a basemap superfluous. Here is a little hack that you can use. A basemap to end all basemaps, as it were. Tile after tile of lightning-fast lightweight nothingness. It's like the Seinfeld of basemaps. A basemap about nothing.HELLLLLLLLLOOOOOOOOOOO! LA LA LAAAAA!Here is a picture of this map:Pretty nice, right? Ok, if you want a different color no problem. Here is a link to the vector tile style editor, which will let you paint this big blank canvas whatever color you like: https://developers.arcgis.com/vector-tile-style-editor/e9cacfd187904614884c16aa52a21cac/layersHappy Minimalist Mapping! John Nelson

This feature layer provides access to OpenStreetMap (OSM) shops data for Africa, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes shop features defined as a query against the hosted feature layer (i.e. shop is not blank).In OSM, a shop is a place selling retail products or services, such as a supermarket, bakery, or florist. These features are identified with a shop tag. There are thousands of different tag values for shop used in the OSM database. In this feature layer, unique symbols are used for several of the most popular shop types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Neighborhood level or 1:80k scale) to see the shop features display. You can click on the feature to get the name of the shop. The name of the shop will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this shop layer displaying just one or two shop types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. shop is jewelry), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. supermarket or convenience shop) that are ready to use, but not for every type of shop.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

4.0.1 is a minor release to correct a deployment problem from Github to Zenodo.org. Content is the same as the 4.0 release:

Pleiades gazetteer datasets

Please report problems and make feature requests via the main Pleiades Gazetteer Issue Tracker.

Content is governed by the copyrights of the individual contributors responsible for its creation. Some rights are reserved. All content is distributed under the terms of a Creative Commons Attribution license (cc-by).

In order to facilitate reproducibility and to comply with license terms, we encourage use and citation of numbered releases for scholarly work that will be published in static form.

Please share notices of data reuse with the Pleiades community via email to pleiades.admin@nyu.edu. These reports help us to justify continued funding and operation of the gazetteer and to prioritize updates and improvements.

Version 4.0 - 6 February 2025

41,200 place resources

Since release 3.2 of pleiades.datasets on 3 November 2023, the Pleiades gazetteer published 876 new and 9,555 updated place resources, reflecting the work of Johan Åhlfeldt, Ella Arnold, Jeffrey Becker, Gabriel Bodard, Sarah Bond, Catherine Bouras, Lucas Butler, Iulian Bîrzescu, Anne Chen, Birgit Christiansen, Niels Christofferson, James Cowey, Francis Deblauwe, Dan Diffendale, Anthony Durham, Denitsa Dzhigova, Tom Elliott, Jordy Didier Orellana Figueroa, Martina Filosa, Jonathan Fu, Ryosuke Furui, Maija Gierhart, Sean Gillies, Matthias Grawehr, Amelia Grissom, Maxime Guénette, Andrew Harris, Greta Hawes, Ryan M. Horne, Carolin Johansson, Daniel C. Browning Jr., Noah Kaye, Philip Kenrick, Brady Kiesling, Yaniv Korman, Mark Krier, Divya Kumar-Dumas, Thomas Landvatter, Chris de Lisle, Yuyao Liu, Stanisław Ludwiński, Sean Manning, Gabriel McKee, John Muccigrosso, Jamie Novotny, Philipp Pilhofer, Jonathan Prag, Adam Rabinowitz, Rune Rattenborg, María Jesús Redondo, Charlotte Roueché, Karen Rubinson, Thomas Seidler, Rosemary Selth, Jason M. Silverman, R. Scott Smith, Néhémie Strupler, Richard Talbert, Francis Tassaux, Clifflena Tiah, Georgios Tsolakis, Scott Vanderbilt, Athanasia Varveri and Valeria Vitale.

Highlights

• Updated gazetteer data in this release: see "Contents" below.
• Removed deprecated "legacy CSV" serialization. JSON or "CSV for GIS" are the recommended packages for most third-party reuse.
• Added new "indexes" dataset: Pleiades places that reference certain external resources.
• Improved serialization of vocabulary terms in "CSV for GIS" serialization and added the previously omitted "Time Periods" vocabulary.
• Added new "sidebar" dataset: assertions by external datasets of relationships to Pleiades places.

Overview

This is a package of data derived from the Pleiades gazetteer of ancient places. It is used for archival and redistribution purposes and is likely to be less up-to-date than the live data at https://pleiades.stoa.org.

Pleiades is a community-built gazetteer and graph of ancient places. It publishes authoritative information about ancient places and spaces, providing unique services for finding, displaying, and reusing that information under open license. It publishes not just for individual human users, but also for search engines and for the widening array of computational research and visualization tools that support humanities teaching and research.

Pleiades is a continuously published scholarly reference work for the 21st century. We embrace the new paradigm of citizen humanities, encouraging contributions from any knowledgeable person and doing so in a context of pervasive peer review. Pleiades welcomes your contribution, no matter how small, and we have a number of useful tasks suitable for volunteers of every interest.

Access and Archiving

The latest versions of this package can be had by fork or download from the main branch at https://github.com/isawnyu/pleiades-datasets. Numbered releases are created periodically at GitHub. These are archived at:

• zenodo.org using the DOI 10.5281/zenodo.1193921
• archive.nyu.edu using the Handle 2451/34305
• archive.org using the URI https://archive.org/details/pleiades.datasets-{version_number}

Credits

Pleiades is brought to you by:

• Our volunteer content contributors (see data/rdf/authors.ttl for complete list and associated identifiers or data).
• Pleiades has received significant, periodic support from the National Endowment for the Humanities since 2006. Grant numbers: HK-230973-15, PA-51873-06, PX-50003-08, and PW-50557-10. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.
• Web hosting and additional support has been provided since 2008 by the Institute for the Study of the Ancient World at New York University.
• Additional support has been provided since 2000 by the Ancient World Mapping Center at the University of North Carolina at Chapel Hill.
• Development hosting and other project incubation support was provided between 2000 and 2008 by Ross Scaife and the Stoa Consortium.