U.S. State Plane Zones (NAD 1983) represents the State Plane Coordinate System (SPCS) Zones for the 1983 North American Datum within United States.

New Parking Citations dataset here: https://data.lacity.org/Transportation/Parking-Citations/4f5p-udkv/about_data ---Archived as of September 2023--- Parking citations with latitude / longitude (XY) in US Feet coordinates according to the California State Plane Coordinate System - Zone 5 (https://www.conservation.ca.gov/cgs/rgm/state-plane-coordinate-system). For more information on Geographic vs Projected coordinate systems, read here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/ For information on how to change map projections, read here: https://learn.arcgis.com/en/projects/make-a-web-map-without-web-mercator/

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.GAP 1 and 2 areas are primarily managed for biodiversity, GAP 3 are managed for multiple uses including conservation and extraction, GAP 4 no known mandate for biodiversity protection. Provides a general overview of protection status including management designations. PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.The USGS Protected Areas Database of the United States (PAD-US) classifies lands into four GAP Status classes:GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protectionIn the United States, areas that are protected from development and managed for biodiversity conservation include Wilderness Areas, National Parks, National Wildlife Refuges, and Wild & Scenic Rivers. Understanding the geographic distribution of these protected areas and their level of protection is an important part of landscape-scale planning. Dataset SummaryPhenomenon Mapped: Areas protected from development and managed to maintain biodiversity Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: USGS Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, or 3GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature 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 and apply filters. 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.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.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 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.

'This file is a digital geospatial Environmental Systems Research Institute (Esri) ArcGIS polygon feature class of the tile index for Cook, DuPage, Grundy, Kane, Kendall, Lake, McHenry, and Will County, Illinois. Each tile is 2,500' on a side, and covers an area of 6,250,000 square feet or 143 acres. There are a total of 18,905 tiles, and each tile represents the boundary or extent of each ortho image. This dataset includes a coordinate based tile identification number, a delivery area number, and a project tile category. The delivery area numbers and project tile attributes are a proprietary classification that are unique to this project. This dataset is stored within an ArcGIS 10.1 geodatabase. This dataset is projected using the Transverse Mercator map projection. The grid coordinate system used is the Illinois State Plane Coordinate System, East Zone (Zone Number Zone 3776, FIPS 1201), NAD 83 NSRS2007 (horizontal datum), with ground coordinates expressed in U.S. Survey Feet.'An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files. Data received at Earth Resources Observation and Science Center (EROS) were reprojected from: Projection: NAD_1983_HARN_StatePlane_Illinois_East_FIPS_1201 Resolution: 6 inch Type: 4 Band to: Standard Product Projection: NAD_1983_UTM_Zone_16N Standard Product Resolution: 0.1500 m Rows: 10000 Columns: 10000 and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.

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.Coordinate System: Web Mercator Auxiliary Sphere 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 Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Number of Features: 3,035,617 flowlines, 473,936 waterbodies, 16,658 sinksSource: EPA and USGSPublication 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 Feature 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.

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.

This layer contains a change analysis from 1973 to 2001 based on analysis of satellite imagery. A NALC image from 1973 with 60-m resolution was classified using unsupervised classification into 100 classes. These classes were subsequently recoded into 5 classes (Woody, Herbaceous, Bare, Marsh and Water) based on comparisions with maps and aerial photos. The same procedure was followed for a 2001 ETM+ image that had been resampled to 15-m resolution. The recoded layers were converted to vector shapefiles and intersected to create this data layer. Subsequently, codes were added to recode the polygons into and to 3 classes (upland, marsh, water) and the area and perimeter of each polygon was calculated. Layer was later renamed (in 2013) from "vbi1970_2001c5_Intersect_N83" to "VBI_LUC_1973_2001_NAD83" to avoid temporal confusion and remove ESRI auto-naming appendage. FGDC Metadata: Identification Information: Citation: Citation information: Originators: John H. Porter Title: Change data layer for the Virginia Coast Reserve, 1973-2001 - VCR05133 *File or table name: vbi1970_2001c5_Intersect_N83 Publication date: 12/22/2005 *Geospatial data presentation form: vector digital data *Online linkage: \MAP1\d\jhp7e\vbi1970_2001c5_Intersect_N83.shp Description: Abstract: This layer contains a change analysis from 1973 to 2001 based on analysis of satellite imagery. A NALC image from 1973 with 60-m resolution was classified using unsupervised classification into 100 classes. These classes were subsequently recoded into 5 classes (Woody, Herbaceous, Bare, Marsh and Water) based on comparisions with maps and aerial photos. The same procedure was followed for a 2001 ETM+ image that had been resampled to 15-m resolution. The recoded layers were converted to vector shapefiles and intersected to create this data layer. Subsequently, codes were added to recode the polygons into and to 3 classes (upland, marsh, water) and the area and perimeter of each polygon was calculated. Purpose: To detect changes on the coast of Virginia. *Language of dataset: en Time period of content: Time period information: Multiple dates/times: Single date/time: Calendar date: 08/12/1973 Single date/time: Calendar date: 08/27/2001 Currentness reference: ground condition Status: Progress: Complete Maintenance and update frequency: None planned Spatial domain: Bounding coordinates: *West bounding coordinate: -76.112114 *East bounding coordinate: -75.135130 *North bounding coordinate: 38.237583 *South bounding coordinate: 37.046598 Local bounding coordinates: *Left bounding coordinate: 402666.874551 *Right bounding coordinate: 487984.802095 *Top bounding coordinate: 4232184.738430 *Bottom bounding coordinate: 4100601.786647 Minimum altitude: -30 Maximum altitude: 30 Altitude units: m Keywords: Theme: Theme keywords: Change analysis Theme keyword thesaurus: None Place: Place keywords: Delmarva Peninsula Place keyword thesaurus: None Access constraints: VCR/LTER Data License required Use constraints: Bona fide scientific research. This is not a legal document Point of contact: Contact information: Contact person primary: Contact person: John Porter Contact organization: Virginia Coast Reserve Long-Term Ecological Research, University of Virginia Contact address: Address type: mailing and physical address Address: 291 McCormick Road Address: PO Box 400123 City: Charlottesville State or province: VA Postal code: 22904-4123 Country: USA Contact voice telephone: 434-924-8999 Contact facsimile telephone: 434-982-2137 Contact electronic mail address: jhp7e@virginia.edu Data set credit: John H. Porter, Virginia Coast Reserve Long-Term Ecological Research, University of Viriginia, Charlottesville, VA 22904 USA *Native dataset format: Shapefile *Native data set environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.0.0.535 Cross reference: Citation information: Title: VCR05113 - Change analysis of the Virginia Coast 1973-2001 Back to Top -------------------------------------------------------------------------------- Data Quality Information: Positional accuracy: Horizontal positional accuracy: Horizontal positional accuracy report: 60-m pixels were used for the 1973 image. Quantitative horizontal positional accuracy assessment: Horizontal positional accuracy value: 60 Horizontal positional accuracy explanation: 60-m pixels were used for the 1973 image. Lineage: Process step: Process description: Dataset copied. Back to Top -------------------------------------------------------------------------------- Spatial Data Organization Information: *Direct spatial reference method: Vector Point and vector object information: SDTS terms description: *Name: vbi1970_2001c5_Intersect_N83 *SDTS point and vector object type: G-polygon *Point and vector object count: 356534 ESRI terms description: *Name: vbi1970_2001c5_Intersect_N83 *ESRI feature type: Simple *ESRI feature geometry: Polygon *ESRI topology: FALSE *ESRI feature count: 356534 *Spatial index: TRUE *Linear referencing: FALSE Back to Top -------------------------------------------------------------------------------- Spatial Reference Information: Horizontal coordinate system definition: Coordinate system name: *Projected coordinate system name: NAD_1983_UTM_Zone_18N *Geographic coordinate system name: GCS_North_American_1983 Planar: Grid coordinate system: *Grid coordinate system name: Universal Transverse Mercator Universal Transverse Mercator: *UTM zone number: 18 Transverse mercator: *Scale factor at central meridian: 0.999600 *Longitude of central meridian: -75.000000 *Latitude of projection origin: 0.000000 *False easting: 500000.000000 *False northing: 0.000000 Planar coordinate information: *Planar coordinate encoding method: coordinate pair Coordinate representation: *Abscissa resolution: 0.000256 *Ordinate resolution: 0.000256 *Planar distance units: meters Geodetic model: *Horizontal datum name: North American Datum of 1983 *Ellipsoid name: Geodetic Reference System 80 *Semi-major axis: 6378137.000000 *Denominator of flattening ratio: 298.257222 Back to Top -------------------------------------------------------------------------------- Entity and Attribute Information: Detailed description: *Name: vbi1970_2001c5_Intersect_N83 Entity type: *Entity type label: vbi1970_2001c5_Intersect_N83 *Entity type type: Feature Class *Entity type count: 356534 Attribute: *Attribute label: FID *Attribute alias: FID *Attribute definition: Internal feature number. *Attribute definition source: ESRI *Attribute type: OID *Attribute width: 4 *Attribute precision: 0 *Attribute scale: 0 Attribute domain values: *Unrepresentable domain: Sequential unique whole numbers that are automatically generated. Attribute: *Attribute label: Shape *Attribute alias: Shape *Attribute definition: Feature geometry. *Attribute definition source: ESRI *Attribute type: Geometry *Attribute width: 0 *Attribute precision: 0 *Attribute scale: 0 Attribute domain values: *Unrepresentable domain: Coordinates defining the features. Attribute: *Attribute label: FID_vbi197 *Attribute alias: FID_vbi197 *Attribute type: Number *Attribute width: 9 Attribute: *Attribute label: ID *Attribute alias: ID *Attribute type: Number *Attribute width: 10 Attribute: *Attribute label: GRIDCODE *Attribute alias: GRIDCODE Attribute definition: Land cover in 1973 *Attribute type: Number *Attribute width: 10 Attribute domain values: Enumerated domain: Enumerated domain value: 1 Enumerated domain value definition: Woody Enumerated domain: Enumerated domain value: 2 Enumerated domain value definition: Bare Enumerated domain: Enumerated domain value: 3 Enumerated domain value definition: Herbaceous Enumerated domain: Enumerated domain value: 4 Enumerated domain value definition: Salt Marsh Enumerated domain: Enumerated domain value: 5 Enumerated domain value definition: Water Enumerated domain: Enumerated domain value: 0 Enumerated domain value definition: not classified Attribute: *Attribute label: FID_vbi010 *Attribute alias: FID_vbi010 *Attribute type: Number *Attribute width: 9 Attribute: *Attribute label: ID_1 *Attribute alias: ID_1 *Attribute type: Number *Attribute width: 10 Attribute: *Attribute label: GRIDCODE_1 *Attribute alias: GRIDCODE_1 Attribute definition: Land Cover in 2001 *Attribute type: Number *Attribute width: 10 Attribute domain values: Enumerated domain: Enumerated domain value: 0 Enumerated domain value definition: not classified Enumerated domain: Enumerated domain value: 1 Enumerated domain value definition: Woody Enumerated domain: Enumerated domain value: 2 Enumerated domain value definition: Bare Enumerated domain: Enumerated domain value: 3 Enumerated domain value definition: Herbaceous Enumerated domain: Enumerated domain value: 4 Enumerated domain value definition: Salt Marsh Enumerated domain: Enumerated domain value: 5 Enumerated domain value definition: Water Attribute: *Attribute label: LandC1973 *Attribute alias: LandC1973 *Attribute type: String *Attribute width: 50 Attribute: *Attribute label: LandC2001 *Attribute alias: LandC2001 *Attribute type: String *Attribute width: 50 Attribute: *Attribute label: LC1973 *Attribute alias: LC1973 Attribute definition: Simple land cover in 1973 *Attribute type: Number *Attribute width: 4 Attribute domain values: Enumerated domain: Enumerated domain value: 0 Enumerated domain value definition: not classified Enumerated domain: Enumerated domain value: 1 Enumerated domain value definition: Upland Enumerated domain value definition source: incorporates grid codes 1-3 Enumerated domain: Enumerated domain value: 2 Enumerated domain value definition: Salt Marsh Enumerated domain: Enumerated domain value: 3 Enumerated domain value definition: Water Attribute: *Attribute label: LC2001 *Attribute alias: LC2001 Attribute definition: Simple land cover in 2001 *Attribute type: Number

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.

Coordinate system Update:Notably, this dataset will be provided in NAD 83 Connecticut State Plane (2011) (EPSG 6434) projection, instead of WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857) which is the coordinate system of the 2023 dataset and will remain in Connecticut State Plane moving forward.Ownership Suppression and Data Access:The updated dataset now includes parcel data for all towns across the state, with some towns featuring fully suppressed ownership information. In these instances, the owner’s name will be replaced with the label "Current Owner," the co-owner’s name will be listed as "Current Co-Owner," and the mailing address will appear as the property address itself. For towns with suppressed ownership data, users should be aware that there was no "Suppression" field in the submission to verify specific details. This measure was implemented this year to help verify compliance with Suppression.New Data Fields:The new dataset introduces the "Land Acres" field, which will display the total acreage for each parcel. This additional field allows for more detailed analysis and better supports planning, zoning, and property valuation tasks. An important new addition is the FIPS code field, which provides the Federal Information Processing Standards (FIPS) code for each parcel’s corresponding block. This allows users to easily identify which block the parcel is in.Updated Service URL:The new parcel service URL includes all the updates mentioned above, such as the improved coordinate system, new data fields, and additional geospatial information. Users are strongly encouraged to transition to the new service as soon as possible to ensure that their workflows remain uninterrupted. The URL for this service will remain persistent moving forward. Once you have transitioned to the new service, the URL will remain constant, ensuring long term stability.For a limited time, the old service will continue to be available, but it will eventually be retired. Users should plan to switch to the new service well before this cutoff to avoid any disruptions in data access.The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2024 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on 10/31/2024 from data collected in 2023-2024. Data was processed using Python scripts and ArcGIS Pro, ensuring standardization and integration of the data.<p style='margin-top:0px; margin-bottom:1.5rem; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helv

Important Note: This item is in mature support as of June 2024 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. Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service.The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available.The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this layer? This layer is suitable for both visualization and analysis across the 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 "Protected from Land Cover Conversion" 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 "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This dataset contains soil type and soil classification, by area. Additional info at: http://mcdc.cas.psu.edu/datawiz.htm; http://co.centre.pa.us/centreco/conservation/Using_SoilMap_website_and_soildatamart.htm

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Environment

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Administrative Services

Temporal Coverage: 2000

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey

Other: none

Related Document(s): Data Dictionary (none)

Frequency - Data Change: As needed

Frequency - Publishing: As needed

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

Summary Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads. Description FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing. Credits Federal Railroad Administration (FRA) Use limitations There are no access and use limitations for this item. Extent West -79.491008 East -75.178954 North 39.733500 South 38.051719 Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000 ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation * Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No Temporal keywords  2013 Theme keywords  Rail Theme keywords  Grade Crossing Theme keywords  Rail Crossings Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00 Presentation formats  * digital map Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor Contact information  ▼►Phone  Voice 202-366-DATA Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format Spatial representation type  * vector * Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348 Credits Federal Railroad Administration (FRA) ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Resource Maintenance ▼►Resource maintenance  Update frequency  annually Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item. Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree"

This is Part 1/2 of the ActiveHuman dataset! Part 2 can be found here. Dataset Description ActiveHuman was generated using Unity's Perception package. It consists of 175428 RGB images and their semantic segmentation counterparts taken at different environments, lighting conditions, camera distances and angles. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals). The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset. Alongside each image, 2D Bounding Box, 3D Bounding Box and Keypoint ground truth annotations are also generated via the use of Labelers and are stored as a JSON-based dataset. These Labelers are scripts that are responsible for capturing ground truth annotations for each captured image or frame. Keypoint annotations follow the COCO format defined by the COCO keypoint annotation template offered in the perception package.

Folder configuration The dataset consists of 3 folders:

JSON Data: Contains all the generated JSON files. RGB Images: Contains the generated RGB images. Semantic Segmentation Images: Contains the generated semantic segmentation images.

Essential Terminology

Annotation: Recorded data describing a single capture. Capture: One completed rendering process of a Unity sensor which stored the rendered result to data files (e.g. PNG, JPG, etc.). Ego: Object or person on which a collection of sensors is attached to (e.g., if a drone has a camera attached to it, the drone would be the ego and the camera would be the sensor). Ego coordinate system: Coordinates with respect to the ego. Global coordinate system: Coordinates with respect to the global origin in Unity. Sensor: Device that captures the dataset (in this instance the sensor is a camera). Sensor coordinate system: Coordinates with respect to the sensor. Sequence: Time-ordered series of captures. This is very useful for video capture where the time-order relationship of two captures is vital. UIID: Universal Unique Identifier. It is a unique hexadecimal identifier that can represent an individual instance of a capture, ego, sensor, annotation, labeled object or keypoint, or keypoint template.

Dataset Data The dataset includes 4 types of JSON annotation files files:

annotation_definitions.json: Contains annotation definitions for all of the active Labelers of the simulation stored in an array. Each entry consists of a collection of key-value pairs which describe a particular type of annotation and contain information about that specific annotation describing how its data should be mapped back to labels or objects in the scene. Each entry contains the following key-value pairs:

id: Integer identifier of the annotation's definition. name: Annotation name (e.g., keypoints, bounding box, bounding box 3D, semantic segmentation). description: Description of the annotation's specifications. format: Format of the file containing the annotation specifications (e.g., json, PNG). spec: Format-specific specifications for the annotation values generated by each Labeler.

Most Labelers generate different annotation specifications in the spec key-value pair:

BoundingBox2DLabeler/BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. KeypointLabeler:

template_id: Keypoint template UUID. template_name: Name of the keypoint template. key_points: Array containing all the joints defined by the keypoint template. This array includes the key-value pairs:

label: Joint label. index: Joint index. color: RGBA values of the keypoint. color_code: Hex color code of the keypoint skeleton: Array containing all the skeleton connections defined by the keypoint template. Each skeleton connection defines a connection between two different joints. This array includes the key-value pairs:

label1: Label of the first joint. label2: Label of the second joint. joint1: Index of the first joint. joint2: Index of the second joint. color: RGBA values of the connection. color_code: Hex color code of the connection. SemanticSegmentationLabeler:

label_name: String identifier of a label. pixel_value: RGBA values of the label. color_code: Hex color code of the label.

captures_xyz.json: Each of these files contains an array of ground truth annotations generated by each active Labeler for each capture separately, as well as extra metadata that describe the state of each active sensor that is present in the scene. Each array entry in the contains the following key-value pairs:

id: UUID of the capture. sequence_id: UUID of the sequence. step: Index of the capture within a sequence. timestamp: Timestamp (in ms) since the beginning of a sequence. sensor: Properties of the sensor. This entry contains a collection with the following key-value pairs:

sensor_id: Sensor UUID. ego_id: Ego UUID. modality: Modality of the sensor (e.g., camera, radar). translation: 3D vector that describes the sensor's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable that describes the sensor's orientation with respect to the ego coordinate system. camera_intrinsic: matrix containing (if it exists) the camera's intrinsic calibration. projection: Projection type used by the camera (e.g., orthographic, perspective). ego: Attributes of the ego. This entry contains a collection with the following key-value pairs:

ego_id: Ego UUID. translation: 3D vector that describes the ego's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable containing the ego's orientation. velocity: 3D vector containing the ego's velocity (in meters per second). acceleration: 3D vector containing the ego's acceleration (in ). format: Format of the file captured by the sensor (e.g., PNG, JPG). annotations: Key-value pair collections, one for each active Labeler. These key-value pairs are as follows:

id: Annotation UUID . annotation_definition: Integer identifier of the annotation's definition. filename: Name of the file generated by the Labeler. This entry is only present for Labelers that generate an image. values: List of key-value pairs containing annotation data for the current Labeler.

Each Labeler generates different annotation specifications in the values key-value pair:

BoundingBox2DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. x: Position of the 2D bounding box on the X axis. y: Position of the 2D bounding box position on the Y axis. width: Width of the 2D bounding box. height: Height of the 2D bounding box. BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. translation: 3D vector containing the location of the center of the 3D bounding box with respect to the sensor coordinate system (in meters). size: 3D vector containing the size of the 3D bounding box (in meters) rotation: Quaternion variable containing the orientation of the 3D bounding box. velocity: 3D vector containing the velocity of the 3D bounding box (in meters per second). acceleration: 3D vector containing the acceleration of the 3D bounding box acceleration (in ). KeypointLabeler:

label_id: Integer identifier of a label. instance_id: UUID of one instance of a joint. Keypoints with the same joint label that are visible on the same capture have different instance_id values. template_id: UUID of the keypoint template. pose: Pose label for that particular capture. keypoints: Array containing the properties of each keypoint. Each keypoint that exists in the keypoint template file is one element of the array. Each entry's contents have as follows:

index: Index of the keypoint in the keypoint template file. x: Pixel coordinates of the keypoint on the X axis. y: Pixel coordinates of the keypoint on the Y axis. state: State of the keypoint.

The SemanticSegmentationLabeler does not contain a values list.

egos.json: Contains collections of key-value pairs for each ego. These include:

id: UUID of the ego. description: Description of the ego. sensors.json: Contains collections of key-value pairs for all sensors of the simulation. These include:

id: UUID of the sensor. ego_id: UUID of the ego on which the sensor is attached. modality: Modality of the sensor (e.g., camera, radar, sonar). description: Description of the sensor (e.g., camera, radar).

Image names The RGB and semantic segmentation images share the same image naming convention. However, the semantic segmentation images also contain the string Semantic_ at the beginning of their filenames. Each RGB image is named "e_h_l_d_r.jpg", where:

e denotes the id of the environment. h denotes the id of the person. l denotes the id of the lighting condition. d denotes the camera distance at which the image was captured. r denotes the camera angle at which the image was captured.

Analysis of ‘Loudoun Parcels’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1a449593-76ac-4922-97ba-40adf45c17d9 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Last Rev. 01/24/08 - E.Foster, P.E. - FSU/BSRCThe Historic Shoreline Database on the Web contains many directories of related types of information about beach changes in Florida over the past 150 or so years. The historic shoreline map images (see the Drawings directory) show precision-digitized approximate mean high water (mhw) shorelines, from the US government coastal topographic maps listed in the associated map bibliography files (see the Sourcebibs directory). These generally show data extending from the mid to late 1800’s to the mid to late 1970’s. The mhw positions have been extracted and tabulated (see the MWHfiles directory) relative to fixed reference “R” points along the beach, spaced approximately 1000 feet (300 meters) apart. Reference points not actually corresponding to actual “in the ground” survey markers are virtual “V” points. Mean high water positions have been and continue to be extracted from FDEP beach profile surveys from the 1970’s through the present and added to the tables. The beach profile data files from which mhw data have been extracted and added into the mhw tables can be found in the ProfileData directory and visually (for many areas) in the ClickOnProfiles directory. The beach profile files include elevation information along the entire length of the profiles. This profile data set has undergone up to fifteen additional quality control checks to ensure accuracy, reliability, and consistency with the historic database coordinate and bearing set. Note that any data deeper than wading depth have not yet undergone any extra quality control checks. Note also that there are *.cod text files of notes associated with the review of the profile data files.The digital historic shoreline map image files are given in a DWG autocad-based format, which should be usable on most versions, as well as many GIS systems. The Florida State Plane 1927/79-adjusted and 1983/90 horizontal coordinate systems are used. These are not metric systems, but with the proper software can be converted to whatever systems you may need. Each map image DWG file contains many layers, documented in an ASCII layer list archived with the DWG file.The database has been maintained and greatly expanded by E. Foster since approximately 1987 and by N. Nguyen since 1995. The initial map digitizing effort was done for FDEP at Florida State University, primarily by S. Demirpolat. Final processing and editing of the original map files to make them user-friendly was performed by N. Nguyen and E. Foster in 1995-7. Extensive quality control and update work has been performed by E. Foster since 1987, and by N. Nguyen since 1995. Field profile surveys have been performed by the FDEP Coastal Data Acquisition section since the early 1970’s, and by a number of commercial surveyors in recent years.The formats of the mhw tables and profile files are explained in text files included in the respective directories.Note that the digitized map image files were originally created in the UTM coordinate system on Intergraph equipment. The translation from UTM to the State Plane coordinate systems has resulted in some minor textual and other visual shifts in the northwest Florida area map image files.The dates in the map legends in the map images are generally composite dates. It is necessary to use the mhw data tables and map bibliographies for accurate dates for any specific location. The date ranges in the data tables relate to specific information given in the map bibliography files.2Generally it may be assumed that the historic shorelines have been digitized as carefully as possible from the source maps. If a historic shoreline does not contain a systematic position error and is feasible in a physical sense, the accuracy of the mhw position is estimated at plus or minus 15 to 50 feet (5 to 15 m), depending on the source and scale. This is as a position in time, NOT as an average mhw position. Data added from field surveys are estimated at plus or minus 10 feet (3 m) or better.It is to be noted that from the 1920’s onward, aerial photographs have usually been the basis of the US government’s coastal topographic maps. Prior to that, the method was plane table surveying. Along higher wave energy coasts, especially the Florida east coast, if there was significant wave activity in the source photography, it is very possible that the mhw was mapped in a more landward location than was probably correct. Alternatively, the use of photography sets with excessive sun glare may have caused the mhw to be mapped in a more seaward location than was probably correct. These effects have been frequently observed in comparisons of close-in-time FDEP controlled aerial photography with FDEP profile surveys. The use of some photography sets containing high wave uprush or sun glare is probable within the historic data. For example, on the east coast the 1940’s series maps tend to show the mhw more seaward than expected, possibly due to sun glare, and the 1960’s series tend to show the mhw more landward than expected. In the latter case, the effect may be due to the 1960’s being a decade of frequent storms. It is recommended that the analyst be aware that some of these effects may exist in the historic data. A questionable historic shoreline is NOT necessarily one to be discarded, just considered with allowance for its’ potential limitations.Using this database, it can readily be observed that the historic trends in shoreline evolution are very consistent with behavior expected from the longshore transport equation, well known to coastal engineers. This is a non-linear equation. Shoreline change can be expected to be linear or constant only in certain situations. It is NOT recommended that any analyst arbitrarily assume constant or linear shoreline change rates over long periods of time, which is often done but not supported by the evidence. The three primary factors controlling shoreline change are sand supply, wave climate, and local geographic features. In some parts of Florida, major storms since 1995 have also become important factors.

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. 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.

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. Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD).Coordinate System: NAD83Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American Samoa.Visible 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: July 27, 2023

Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name.
The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format.

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.

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. 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.

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. Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD).Coordinate System: NAD83Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American Samoa.Visible 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: July 27, 2023

Analysis of ‘United States National Grid for New Mexico, UTM 13, (1000m X 1000m polygons )’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7836d781-a94a-4667-a72a-34d80688d92d on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.

--- Original source retains full ownership of the source dataset ---