The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

These are the main layers that were used in mapping and analysis for the Santa Monica Mountains North Area Plan, which was adopted by the Board of Supervisors on May 4, 2021. Below are some links to important documents and to actually GIS data.Plan Website - This has links to the actual plan, maps and all project related materials. Click here for website.Online Web Mapping Application - This is the online application that shows all of the layers associated with the plan. These are the same layers that will be available for download below. Click here for the web mapping application.GIS Layers - The main GIS layers used in the application are available below.Below is a list of the GIS layers provided (shapefile format):Environmental (Zipped - 4.4 MB - click here)Habitat Connectivity - Essential Connectivity Area (ECA)Vegetation Sensitivity (includes ArcGIS .lyr file for version 10.0 and higher)Scenic Resources (Zipped - 1.3 MB - click here)State-Designated Scenic Highway 200-foot buffer (Please see 'State-Designated Scenic Highway' on our Open Data site here)Scenic RouteScenic Route 200-foot buffer

The Cropland Data Layer (CDL) is a crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. The CDL is created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section. For detailed FAQ please visit CropScape and Cropland Data Layers - FAQs. To explore details about the classification accuracies and utility of the data, see state-level omission and commission errors by crop type and year. The asset date is aligned with the calendar year of harvest. For most crops the planted and harvest year are the same. Some exceptions: winter wheat is unique, as it is planted in the prior year. A hay crop like alfalfa could have been planted years prior. For winter wheat the data also have a class called "Double Crop Winter Wheat/Soybeans". Some mid-latitude areas of the US have conditions such that a second crop (usually soybeans) can be planted immediately after the harvest of winter wheat and itself still be harvested within the same year. So for mapping winter wheat areas use both classes (use both values 24 and 26). While the CDL date is aligned with year of harvest, the map itself is more representative of what was planted. In other words, a small percentage of fields on a given year will not be harvested. Some non-agricultural categories are duplicate due to two very different epochs in methodology. The non-ag codes 63-65 and 81-88 are holdovers from the older methodology and will only appear in CDLs from 2007 and earlier. The non-ag codes from 111-195 are from the current methodology which uses the USGS NLCD as non-ag training and will only appear in CDLs 2007 and newer. 2007 was a transition year so there may be both sets of categories in the 2007 national product but will not appear within the same state. Note: The 2024 CDL only has the data band. The cultivated and confidence bands are yet to be released by the provider.

This U.S. Geological Survey (USGS) metadata record consists of 17 different spatial layers in GeoTIFF format for the Hawaii. They are: 1) average water capacity (awc.zip), 2) percent sand (sand.zip), 3) percent silt (silt.zip), 4) percent clay (clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (imperv.zip), 14) snow depletion curve numbers (CV_INT.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All data cover the National Hydrologic Model's (NHM) version 1.1 Alaskan domain. The NHM is a modeling infrastructure consisting of three main parts: 1) an underlying geospatial fabric of modeling units (hydrologic response units and stream segments) with an associated parameter database, 2) a model input data archive, and 3) a repository of the physical model simulation code bases (Regan and others, 2014). The NHM has been used for a variety of applications since its initial development.The 250-meter (m) raster data sets for soils are derived from the OpenGeoHub's LandGIS data (Hengl, 2018). The 30-meter raster of land use and land cover data are a simplified re-classification version of the North American Land-Change Monitoring System (NALCMS, Latifovic and others, 2012) data following the guidance and crosswalk table (CrossWalk.xslx) in Viger and Leavesley (2007). This layer was used to derive rasters representing dominant vegetative cover type, snow, summer and winter rain interception values, leaf cover and loss, and rooting depth. The impervious data was compiled from the Global Man-made Impervious Surface (GMIS) Dataset from Landsat, v1 (NASA, 2010). The tree canopy data was compiled from MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, (Carroll and others, 2017). The snow depletion data was compiled from data by Liston and others (2009) and further processed using methods provided in a snow depletion table (SDC.xslx) by Sexstone and others (2020). All file formats are in GeoTIFF (Geograhpic Tagged Imaged Format).

A complete list of live websites using the data-layer-helper technology, compiled through global website indexing conducted by WebTechSurvey.

The forest cover data provided by Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU) are basically a digital version of its 1:12,500 scale forest cover polygon maps. The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP. As a digital archive, however, changes within forest stands can be updated more readily. At the same time, it should be kept in mind that most of these digital forest cover data were acquired in 1993, and the data set has been static since that time.

This U.S. Geological Survey (USGS) metadata release consists of 17 different spatial layers in GeoTIFF format. They are: 1) average water capacity (AWC.zip), 2) percent sand (Sand.zip), 3) percent silt (Silt.zip), 4) percent clay (Clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (Snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (Imperv.zip), 14) snow depletion curve numbers (Snow.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All data cover the National Hydrologic Model's (NHM) version 1.1 domain. The NHM is a modeling infrastructure consisting of three main parts: 1) an underlying geospatial fabric of modeling units (hydrologic response units and stream segments) with an associated parameter ...

Create your own map using layers from the Open Data Site using this mapping application

Transportation Policy Plan (TPP) 2050 All Data Layers contains the data layers developed as part of the 2050 TPP, published 2025.

This data can be seen across the maps in the 2050 TPP, both in static and interactive form. This layer group includes data associated with various chapters from the TPP including: Bicycle Investment Plan, Freight Investment Plan, Highway Investment Plan, Transit Investment Plan, Environmental Justice Analysis, and Evalutation and Performance.

The specific data layers published for the 2050 TPP are as follows, and can be found in Minnesota Geospatial Commons for public access:

RegionalEnvironmentalJusticeByCensusTract
TPP2050ActiveRailroads
TPP2050BusOnlyShoulders
TPP2050CriticalRuralAndUrbanFreightCorridors
TPP2050EZPass
TPP2050FreestandingTownCenters
TPP2050FreightCorridors
TPP2050FreightTerminals
TPP2050HighFrequencyNetwork
TPP2050HighwaySafetyProjects
TPP2050Interchanges
TPP2050NonPrimaryHighwayFreightSystem
TPP2050ParkAndRideLotsTransitCenters
TPP2050PrimaryHighwayFreightSystem
TPP2050ProjectLocationPoints
TPP2050RBTNAlignments
TPP2050RBTNCorridors
TPP2050RBTNDestinations
TPP2050RegionallySignificantProjectLines
TPP2050RegionallySignificantProjectPoints
TPP2050RegularRouteTransit
TPP2050SpotMobility
TPP2050TargetedRegionalCapacity
TPP2050TransitAdvantages
TPP2050TransitCapitalLevyCommunities
TPP2050TransitMarketAreas
TPP2050TransitSignalPriorityCorridors
TPP2050TransitwayAlignments
TPP2050TravelTimeIndex

Detailed information about the attributes can be found in the TPP 2050 All Layer Attributes document

The spatial extent of this data includes the seven-county metro area and portions of Wright and Sherburne county within the Metropolitan Planning Organization (MPO) area.

This data are for planning purposes and should be used at a regional level.

A complete list of live websites using the Gtm Data Layer technology, compiled through global website indexing conducted by WebTechSurvey.

The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers

Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).

Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation.

Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask.

The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography.

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

The geospatial data product called the Cropland Data Layer (CDL) is hosted on CropScape (https://nassgeodata.gmu.edu/CropScape/). The CDL is a raster, geo-referenced, crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth.

Dataset

This dataset was created by Larbi Saidchikh

Contents

🇬🇧 영국

This data set was prepared by BORIS staff by processing the original vector data into raster files. The original data were received as ARC/INFO coverages or as export files from SERM. The data include information on forest parameters for the BOREAS SSA MSA. The data are stored in binary, image format files.

The Geospatial Fabric is a dataset of spatial modeling units for use within the National Hydrologic Model that covers Alaska, and most major river basins that flow in from Canada. This U.S. Geological Survey (USGS) data release consists of the geospatial fabric features and other related datasets created to expand the National Hydrologic Model to Alaska. This U.S. Geological Survey (USGS) child item consists of 17 different spatial layers in GeoTIFF format for Alaska. They are 1) average water capacity (awc.zip), 2) percent sand (sand.zip), 3) percent silt (silt.zip), 4) percent clay (clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (imperv.zip), 14) snow depletion curve numbers (CV_INT.zip), 15) rooting depth ( ...

These spatial layers were created to provide estimates of non-native species invasion risk across the contiguous United States based on proximity to human population centers and transportation corridors, and proximity to known locations of non-native species. To calculate the human transport risk layer we estimated the proximity to human population centers, transportation corridors, and speed of movement across the landscape. To calculate invasion risk based on known locations of non-native species, we gathered over 30 million records of non-native species occurrences across the contiguous United States from online databases to create nationwide maps of non-native species richness by species type: amphibians, fish, invertebrates, mammals, mollusks, plants, and reptiles.

Chicago Heights Municipal Atlas Data Layers - March 25, 2020 uploadData LayersWards (2018)* Alderman's name and contact emailCity-Owned Parcels* Unknown year of data, but differs from 2016 Cook County Assessor dataHeritage Preservation Overlay DistrictVoting Precincts (2018)Municipal Boundary (2016)* Differs from Cook County municipal boundary data, but this boundary will be prioritized.Vacant Properties (2016) - Points [for heat map]Vacant Properties (2016) - ParcelsLand Bank Properties (2017)Lawn Maintenance Parcels (2019)