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 layer provides labels for selected cities, towns, and neighborhoods around the world in support of the World Dark Gray Base map. Together they draw attention to your thematic content by providing a neutral background with minimal colors, labels, and features. Only key information is represented to provide geographic context, allowing your data to come to the foreground.See the Esri Canvas Maps Part I: Author Beautiful Web Maps With Our New Artisan Basemap Sandwich blog post for more information on how to use this map.This map was compiled by Esri using HERE data, Garmin basemap layers, OpenStreetMap data, GIS community data, and Esri basemap data. The map includes worldwide coverage from 1:591M scale to 1:577k scale. More detailed coverage is included in North America, Europe, Africa, South America and Central America, the Middle East, Pacific Island nations, India, Australia, and New Zealand down to the 1:9k scale. Data for Africa and Pacific Island nations from ~1:288k to ~1:9k was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

This page contains the help documentation for the GIS Open Data Portal. Refer to https://gisdata-csj.opendata.arcgis.com/pages/help.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

These boundaries define the regions based on terrestrial and marine areas. These are intended to be used in by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.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.

Services:2015_Parking (FeatureServer)AGO_MAP_2019 (FeatureServer)Bike_Racks_2020 (FeatureServer)BikeParking2017 (FeatureServer)Chicago_Central_Business_District (FeatureServer)Chicago_Wards_hosted (FeatureServer)ChicagoMayHwys (FeatureServer)Control_Points_Interlockings (FeatureServer)ControlPoints_Interlockings (FeatureServer)Cook_County_Districts_hosted (FeatureServer)CTA_Bus_Routes (FeatureServer)CTA_Bus_Routes_2019 (FeatureServer)cta_rail_lines (FeatureServer)CTABusRoutes2019 (FeatureServer)FRA_Crossings (FeatureServer)FreightRailroads (FeatureServer)Grade_Crossings (FeatureServer)Illinois_House_Districts (FeatureServer)Illinois_Senate_Districts (FeatureServer)Lines_COVID19 (FeatureServer)Metra_Bridges (FeatureServer)Metra_facilities (FeatureServer)metra_lines_2018 (FeatureServer)Metra_Routes_Test (FeatureServer)metra_stations_2018 (FeatureServer)MetraLines_2016 (FeatureServer)MetraLines2017 (FeatureServer)MetraLines2019_CreateRoutes (FeatureServer)MetraPoliceBeats (FeatureServer)MetraStations2017new (FeatureServer)Municipalities (FeatureServer)NICTD_South_Shore_Line (FeatureServer)NICTD_Stations (FeatureServer)Pace_ParkNRide_Facilities (FeatureServer)Pace_Routes_03_25_2019 (FeatureServer)PaceRoutes2020 (FeatureServer)Parking_Lots_2016 (FeatureServer)parking_lots_2017 (FeatureServer)Parking_Survey_2018_AGO_Published (FeatureServer)Parking_Survey_2018_Final (FeatureServer)Parking_Survey_2019_Final (FeatureServer)ParkingLots2017 (FeatureServer)Police_Beats_2020_Draft (FeatureServer)Police_tows (FeatureServer)Six_County_Service_Area (FeatureServer)Stations_COVID19 (FeatureServer)Tie_Substations (FeatureServer)TrainsPerDay (FeatureServer)US_Congressional_Districts (FeatureServer)Yards_Points (FeatureServer)yards_points_2019 (FeatureServer)Yards_Polygons (FeatureServer)yards_polygons_2018 (FeatureServer)

Retirement Notice: This item is in mature support as of June 2025 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This tile layer is designed to provide a a hydrologically oriented set of features to use with the World Terrain Base Layer or other simple base maps. The map features a hydro-centric design based on the amount of water flowing within the drainage network such that symbols of the same size and color represent roughly the same amount of water. This map shows surface water flow as a linear phenomenon even over and through bodies of water. Using the best available data we show relative flow accurately, so that if one river carries more water downstream than another river, the result will be that the river will have a thicker symbol on the map. This map is used as an overlay for content such as elevation from the World Terrain Base service or thematic services such as soil units, vegetation, or ecoregions. Combined with a basemap and your map services, this map provides a frame of reference for showing regional, national, and continental hydrologic phenomena such as drought, runoff, river level monitoring and flood forecasting. River names are collected in the UTF8 character set so river names are collected in their original language but are written in the Roman alphabet. Sources for all river names are from the open source geonames.org project so they are international by nature. The map is compiled from several sources. The global scales (very small scales through 1:2,300,000) include content from: HydroSHEDS, GTOPO30 Global Topographic Data, SRTM, GLWD, WorldClim, GRDC, and WWF Global 200 Terrestrial Eco Regions, with the latter three providing the inputs and basis for calculating flow. At medium scales (1:36,000 to 1:2,000,000) this service currently contains only U.S. data from the NHDPlusV2 that was jointly produced by the USGS and EPA.

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.The Ocean Base map currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas.The World Ocean Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the World Ocean Base with the Reference service drawn on top. Article in the Fall 2011 ArcUser about this basemap: "A Foundation for Ocean GIS".The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, and Esri for topographic content. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details on the users who contributed bathymetric data for this map via the Community Maps Program, view the list of Contributors for the Ocean Basemap. The basemap was designed and developed by Esri. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO's website: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/.The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system.Tip: Here are some famous oceanic locations as they appear this map. Each URL launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This data set contains geolocation information of the infrastructure locations for the SnowEx20 Intensive Observation Period (IOP) and Time Series (TS) campaigns. Available scientific infrastructure locations in this data set are tower and sensor locations, aircraft flight lines, planned and actual snow pit locations, and time-lapse camera locations. Additionally, this data set contains areal snow depth and tree density classification matrix over the Grand Mesa, CO study area.

Through a system of 11 service regions, CCSESA provides the organizational mechanism for the 58 County Superintendents of Schools to design and implement statewide programs to identify and promote quality cost-effective educational practices and services, and provide support to school districts.

Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change data.Please refer to the new service here: https://usfs.maps.arcgis.com/home/item.html?id=085626ec50324e5e9ad6323c050ac84dThis product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

Police citations for 2025. Data from the electronic citations issued by the Sacramento Police Department. This data is updated monthly. One citation could include multiple charges, and will therefore result in showing on multiple lines in the data, extract. Date/Time fields are string data types and will be viewed and downloaded in US/Pacific time. Contact SacGIS@cityofsacramento.org.

The footprint of a building designed for a function or to afford a particular convenience or service. These are a subset of the buildings found in the BuildingFootprint feature class and only those actively managed by a local government, or other land holder. The information typically comes from design drawings of the facilities and is a framework for the management of interior spaces and work activities associated with such.

The Human Geography Dark Map (World Edition) web map provides a detailed world basemap with a dark monochromatic style and content adjusted to support human geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Dark Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Dark Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Dark Base, a simple basemap consisting of land areas in a very dark gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in A Dark Version of the Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

The DC Basemap provides a reference map for the District of Columbia projected in Web Mercator. Access the ArcGIS Rest endpoint. The basemap utilizes the most current planimetric and reference data available and represents the real world with foundation map layers derived from base data collection done in 2023.The service is provided by the Office of the Chief Technology Officer.

The National Parks dataset is frequently updated by the National Park Service (NPS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset depicts National Park Service boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. For the full data description, please go to https://irma.nps.gov/DataStore/Reference/Profile/2224545?lnv=True. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529046

An Exit point layer (within NYC) suitable for use in a GIS. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses. This map service is available to the public. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.