Download In State Plane Projection Here ** In addition to the Tax Parcel polygons feature class, the hyperlink download above also contains a parcel point data layer ** Parcel boundaries are developed from deeds, plats of subdivision and other legal documents going back to the mid 1800's, following generally accepted practices used in Public Land Survey System states, and following guidelines established by the Illinois Department of Revenue and the International Association of Assessment Officials. Lake County's parcel coverage is based on resolving the accumulated evidence of all of the legal documents surrounding a particular parcel or subdivision, and not the result of a countywide resurvey. These parcel boundaries are intended to be a visual inventory of property for tax and other administrative purposes; they are not intended to be used in place of an on-site survey or for the precise determination of property corners or PLSS features based on GIS coordinates. In Illinois, only a registered professional land surveyor is authorized to determine boundary locations. Included are the tax parcel boundaries, represented as polygons and centroids, for all changes resulting from legal records submitted to the Recorder of Deeds up to December 31st of the preceding year, as well as any court orders, municipal annexations and other transactions which impact the tax parcel boundaries. NOTE: The ONLY attribute included is the Property Index Number, or PARCEL_NUM. Additional assessment attribute data can be downloaded here This parcel layer is used for tax assessment purposes and for a variety of other local government functions. It changes often, both spatially and in its attribution, based on divisions or consolidations, the sale of property and other transactions. Example: PIN 08-17-304-014 can be interpreted as follows: Township 08, Section 17, Block 304, Parcel 014. Note that the first digit of block, "3" in this example, signifies that the parcel lies in quarter section 3. The quarter sections are labeled from 1 through 4, representing the northwest, northeast, southwest and southeast quarter sections, respectively. Update Frequency: This dataset is updated on a weekly basis.

The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. This layer is a partial view of the Information Sales System (ISS) extract, a report of property characteristics taken from the County’s Megabyte Property Tax System (MPTS). This layer may be missing some attributes (e.g., Owner Name) which may not be published to the Internet due to privacy conditions under the California Public Records Act (CPRA). Please contact the Clerk-Recorder-Assessor (CRA) office at (707) 565-1888 for information on availability, associated fees, and access to other versions of Sonoma County parcels containing additional property characteristics.The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

Hotel chain Marriott had properties in multiple regions across the globe from 2016 to 2024. The company reported ***** properties in North America that year, which was significantly more than any other region.

This file is based on the Geospatial Services Division's Official Protraction Diagram (OPD)and Leasing Maps (LM). Each offshore area is defined by an API Number corresponding to those in the API Bulletin Number D12A. OPDs are numbered using the United Nations International Map of the World Numbering System, and are generally named for land or hydrographic features contained within the limits of the diagram. This data set contains Official Protraction Diagram (OPD) and Leasing Map (LM) outlines in ESRI shape file formats for the BOEM Gulf of Mexico Region. The use of OPDs and LMs makes it easier to refer to individual blocks within a region or planning area. These diagrams were clipped along the Submerged Lands Act (SLA) boundary and along lines contained in the Continental Shelf Boundaries (CSB) GIS data files to show only those blocks or portions thereof within federal jurisdiction. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are NOT an OFFICIAL record for the exact OPD boundaries. Only the paper OPD or a digital image of them serves as OFFICIAL records.Official Protraction Diagrams and other cadastre information the BOEM produces are generated in accordance with 30 Code of Federal Regulations (CFR) Part 556.8 Subpart A, (formerly Part 256.8 Subpart A (2010)) to support Federal land ownership and mineral resource management. Further information on the SLA and development of this line from baseline points can be found in OCS Report BOEM 99-0006: Boundary Development on the Outer Continental Shelf. https://www.boem.gov/BOEM-Newsroom/Library/Publications/1999/99-0006-pdf.aspx Because GIS projection and topology functions can change or generalize coordinates, and because shapefiles cannot represent true arcs, these GIS files are considered to be approximate and are NOT an OFFICIAL record for the exact block coordinates or areas. The Official Protraction Diagrams (OPDs)and Leasing Maps (LMs) and Supplemental Official Block Diagrams (SOBDs) serve as the legal definition for BOEM offshore boundary coordinates and area descriptions and can be found at the following location: https://www.boem.gov/Official-Protraction-Diagrams/. Contains the protraction polygons clipped on the fedstate (SLA-Boundary) as of March 15, 2013. Used ArcCatalog to create shape files.

United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Saudi Arabia data was reported at 1,463.000 Unit in 2016. This records an increase from the previous number of 338.000 Unit for 2015. United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Saudi Arabia data is updated yearly, averaging 137.000 Unit from Dec 1998 (Median) to 2016, with 19 observations. The data reached an all-time high of 1,463.000 Unit in 2016 and a record low of 62.000 Unit in 2009. United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Saudi Arabia data remains active status in CEIC and is reported by Dubai Statistics Center. The data is categorized under Global Database’s United Arab Emirates – Table AE.EB001: Land Parcels Statistics: Dubai.

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled so that the applications can restrict the displayed scenes to a specific date range. This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.For more information on each of the individual layers, see http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ; http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ; http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled so that the applications can restrict the displayed scenes to a specific date range. This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.For more information on each of the individual layers, see http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ; http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ; http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): Land Cover Class ID: is the identification number of each LULC class Land Cover Class Short Name: is the short name of each LULC class Image ID: is the identification number of each image within its corresponding LULC class Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image Latitude: is the latitude of the center point of each image Longitude: is the longitude of the center point of each image Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes Administrative Department Level1: is the administrative level 1 name to which each image belongs Administrative Department Level2: is the administrative level 2 name to which each image belongs Locality: is the name of the locality to which each image belongs Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files: A CSV file that contains all exported images for this class A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images". To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name. © Sentinel2GlobalLULC Dataset by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)

United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Kuwait data was reported at 855.000 Unit in 2016. This records an increase from the previous number of 154.000 Unit for 2015. United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Kuwait data is updated yearly, averaging 244.000 Unit from Dec 1998 (Median) to 2016, with 19 observations. The data reached an all-time high of 855.000 Unit in 2016 and a record low of 32.000 Unit in 2011. United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Kuwait data remains active status in CEIC and is reported by Dubai Statistics Center. The data is categorized under Global Database’s United Arab Emirates – Table AE.EB001: Land Parcels Statistics: Dubai.

HILDA+ version 2.0 is an updated version of the HILDA+ (HIstoric Land Dynamics Assessment+) doi:10.1594/PANGAEA.921846. It is a global dataset on annual land use/cover change between 1960-2020 at 1 km spatial resolution. It is based on a data-driven reconstruction approach and integrates multiple open data streams (from high-resolution remote sensing, long-term land use reconstructions and statistics). Compared to the previous version, this new HILDA+ version 2.0 uses a base map from the year 2020 (based on ESA World Cover), integrates new remote sensing-based land cover datasets (see documentation sheet in the uploaded data), is calibrated on updated national land use statistics from FAO and includes additional cropland-related land use categories: tree crops, agroforestry and annual crops. See the documentation and the paper reference for the method of cropland mapping. Forests are subdivided into different forest types based ESA CCI Land Cover (1992-2020). HILDA+ version 2.0 covers the following land use/cover categories (given with their respective code numbers in the dataset): 11: Urban areas, 22: Annual crops, 23: Tree crops, 24: Agroforestry, 33: Pasture/rangeland, 40: Forest (unknown/other), 41: Forest (evergreen, needle leaf), 42: Forest (evergreen, broad leaf), 43: Forest (deciduous, needle leaf), 44: Forest (deciduous, broad leaf), 45: Forest (mixed), 55: Unmanaged grass/shrubland, 66: Sparse/no vegetation.

United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Oman data was reported at 301.000 Unit in 2016. This records an increase from the previous number of 113.000 Unit for 2015. United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Oman data is updated yearly, averaging 156.000 Unit from Dec 1998 (Median) to 2016, with 19 observations. The data reached an all-time high of 301.000 Unit in 2016 and a record low of 27.000 Unit in 2011. United Arab Emirates Number of Land Parcels: Dubai: Owned by GCC Natives: Oman data remains active status in CEIC and is reported by Dubai Statistics Center. The data is categorized under Global Database’s United Arab Emirates – Table AE.EB001: Land Parcels Statistics: Dubai.

This feature layer displays the current zoning by parcel for the City of Kennesaw, Georgia. For each parcel within the city limits of Kennesaw, the following data (fields) can be found: Zoning_1, CALCAcres, Classification, PIN, Juris, InKDDA, InHistDist, InAHD, InHPV, In SLO, InURA, LCI, and FLU. Zoning_1 is the type of zoning the parcel is classified as (Ex. R-20, HI, & CBD). CALC Acres is the calculated acres for the parcel. Classification is the general class of zoning (Residential, Commercial, Industrial, or Mixed Use). PIN is the parcel number. Juris is the tax district that he parcel is located in. InKDDA is whether the parcel is or is not in the Kennesaw Downtown Development Authority (KDDA). InHistDist is whether the parcel is in a Historical district. InAHD is whether the parcel is or is not in the Airport Hazard District for Cobb County International Airport. InHPV is whether the parcel is or is not in the Historical Preservation Village. InSLO is whether the parcel is or is not in the Senior Living Overlay (senior living areas). InURA is whether the parcel is or is not in the Urban Redevelopment Areas. LCI is livable centers initiative. FLU is the future land use type.