Layers in this dataset represent Public Land Survey System subdivisions for Canadian County. Included are Townships, Sections, Quarter Sections and Government Lots. This data was created from 2019 to 2021 as part of a project to update county parcel data in partnership with ProWest & Associates (https://www.prowestgis.com/) and CEC Corporation (https://www.connectcec.com/). Corners were located to the quarter section level and additional corners were determined for the South Canadian River meanders based on the original government surveys. Quarter section corners were located using Certified Corner Records ( filed by Oklahoma licensed professional surveyors with the Oklahoma Department of Libraries where those records included coordinates. When a corner record could not be found or did not include coordinates, other interpolation methods were employed. These included connecting known corner record locations to unknown corners using data from filed subdivisions or from highway plans on record with the Oklahoma Department of Transportation. Where no corner records with coordinates were available and no interpolation methods could be used, aerial inspection was used to locate corners as the last option.Corner location accuracy varies as the method of locating the corner varies. For corners located using Certified Corner Records, accuracy is high depending on the age of the corner record and can possibly be less than 1 U.S. Foot. For corners located using interpolation methods, accuracy depends on the additional material used to interpolate the corner. In general, newer subdivisions and highway plans yield higher accuracy. For meander corners located using original government surveys, accuracy will be low due to the age of those surveys which date to the 1870's at the earliest. Additionally, corners that were located with aerials as the last available option cannot be assumed to be accurate.The data was built at the quarter section level first by connecting located corners and larger subdivisions were created from the quarter sections. For townships that extend into Grady County, township lines were only roughly located outside sections not in Canadian County.

Feature layer of locations corresponding to surveys that are produced by Vermont licensed land surveyors and submitted—as .pdf copies—to the Vermont Land Survey Library.Locations are attributed with information such as name of surveyor, date of survey, survey type (e.g., subdivision), and municipality. When the feature layer is opened in ArcGIS Online, the .pdf copies (as feature attachments) can be viewed/downloaded.Effective January 1, 2020 and as stated in27 V.S.A. § 341, surveys are required for property line changes in Vermont. Licensed land surveyors who produce the surveys are to submit a digital copy of them to the library in.pdf format (see27 V.S.A. §1401 and 27 V.S.A. §1403).The copies of surveys are for public reference only, with the originals that most often reside with the Municipality remaining the official documents. The purpose of the land survey library is to improve knowledge of who owns what lands where throughout Vermont.For more information about land surveying in Vermont, see theVermont Society of Land Surveyors (VSLS) and the Vermont Survey Law Manual (PDF).

Often these parcels were surveyed before township surveys in their area. They may also be supplemental to them (as is the case with some Cadastral Islands). Sometimes these were laid out after a township survey was done, so some may be part of a geographic township. Cadastral Location includes the following: GTP Block - Timber block used by the Grand Trunk Pacific Railway for feeding steam engines, building bridges, and for supplying railway ties. Mining Location - A parcel of land whose surveyed boundaries were laid out during the late nineteenth century for the Crown sale of land for mining purposes to groups or individuals. Cadastral Island - Island delineated on survey plans. It may or may not be part of a geographic township. Other Location - A parcel of land whose surveyed boundaries were laid out during the late nineteenth century for the Crown sale of land for various agricultural or farming purposes to groups or individuals. These locations are mainly found in Northern Ontario and also exist in the un-surveyed territories.

Land mobile private locations in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

Polygon geometry with attributes displaying future land use as designated by the City of Baton Rouge and Parish of East Baton Rouge comprehensive plan (FUTUREBR).Metadata

Version 2 minor revision 27 June 2024.\r \r This is the latest compilation of land use mapping information for Australia’s regions as at December 2023. The land use data are supported by a supplementary commodities dataset, containing extra information on the location of select predominantly agricultural commodities. These datasets replace the previous 2020 December updates. \r Version 2 fixes issues caused during the conversion of the state vector datasets to rasters, where single pixel horizontal lines were generated in local areas. This does not affect the date or scale of mapping.\r \r These data were compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources through the Australian Collaborative Land Use and Management Program (ACLUMP). These datasets are not recommended for change analysis or for national land use statistics—instead use the Land use of Australia 2010-11 to 2015-16.\r \r About the Catchment Scale Land Use of Australia – Update December 2023 spatial dataset:\r \r A seamless raster dataset that combines land use vector data for all state and territory jurisdictions, at a spatial resolution of 50 by 50 metres.\r Shows a single dominant land use for each location, based on the management objective of the land manager (as identified by state and territory agencies).\r Updates have been made to New South Wales, Northern Territory, Tasmania, Victoria, the capital city of Adelaide, parts of the Great Barrier Reef NRM regions, and national updates to select horticultural tree crops and protected cropping structures. There are also minor corrections to Western Australia, and more accurate representation of mining areas in South Australia. \r The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary and are provided as supporting datasets. \r Produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. \r Refer to the metadata and ABARES website for additional information.\r \r About the Catchment Scale Land Use of Australia – Commodities – Update December 2023 spatial dataset:\r - Provides location, extent and year verified for 185 commodities, where mapped, as a vector dataset. \r - Commodity data are validated in the field and using other sources.\r - Generally, a single commodity is shown at a location reflecting the most recent date that location was verified.\r - The location of a commodity may change on a seasonal to annual basis, depending on factors such as climate, markets or farming systems.\r - Not nationally complete or comprehensive, and with various dates of capture (1967 to 2023) and input mapping products (2014 to 2023). \r - Refer to the metadata for additional information.\r \r Citation\r - Land use: ABARES 2024, Catchment Scale Land Use of Australia – Update December 2023 version 2, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, June, CC BY 4.0, DOI: 10.25814/2w2p-ph98\r - Commodities: ABARES 2024, Catchment Scale Land Use of Australia – Commodities – Update December 2023, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, February CC BY 4.0. DOI: 10.25814/zfjz-jt75

This line feature represents Federal interests in private land, including easements and reservations (1) in which the Federal Government does not have a fee title interest; and (2) that provide both legal public recreational access and legal administrative access to the Federal land in conformance with the MAPLand Act and the PLAD project. This dataset also also provides BLM managers with information to identify access limitations, vulnerabilities, and areas where access to public lands may be improved. The dataset can also support additional management of resources such as timber harvest, travel and transportation planning, wildfire fuels reduction projects and many other land management related decisions.

Order limiting mineral entry on state land to leasehold locations and prohibiting new mining claims. Unlike a mining claim, a leasehold location must be converted to a lease before mining is allowed.

This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Leasehold Location category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction.

Each feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://dnr.alaska.gov/projects/las/ Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

City & County Lessee Lands on the island of Oahu

Open AccessThis archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support: Crawford C.L., Yin, H., Radeloff, V.C., and Wilcove, D.S. 2022. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. Science Advances doi.org/10.1126/sciadv.abm8999. An archive of the analysis scripts developed for this project can be found at: https://github.com/chriscra/abandonment_trajectories (https://doi.org/10.5281/zenodo.6383127). Note that the label '_2022_02_07' in many file names refers to the date of the primary analysis. 'dts” or “dt” refer to “data.tables,' large .csv files that were manipulated using the data.table package in R (Dowle and Srinivasan 2021, http://r-datatable.com/). “Rasters” refer to “.tif” files that were processed using the raster and terra packages in R (Hijmans, 2022; https://rspatial.org/terra/; https://rspatial.org/raster). Data files fall into one of four categories of data derived during our analysis of abandonment: observed, potential, maximum, or recultivation. Derived datasets also follow the same naming convention, though are aggregated across sites. These four categories are as follows (using “age_dts” for our site in Shaanxi Province, China as an example): observed abandonment identified through our primary analysis, with a threshold of five years. These files do not have a specific label beyond the description of the file and the date of analysis (e.g., shaanxi_age_2022_02_07.csv); potential abandonment for a scenario without any recultivation, in which abandoned croplands are left abandoned from the year of initial abandonment through the end of the time series, with the label “_potential” (e.g., shaanxi_potential_age_2022_02_07.csv); maximum age of abandonment over the course of the time series, with the label “_max” (e.g., shaanxi_max_age_2022_02_07.csv); recultivation periods, corresponding to the lengths of recultivation periods following abandonment, given the label “_recult” (e.g., shaanxi_recult_age_2022_02_07.csv). This archive includes multiple .zip files, the contents of which are described below: age_dts.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for, as of that year, also referred to as length, duration, etc.), for each year between 1987-2017 for all 11 sites. These maps are stored as .csv files, where each row is a pixel, the first two columns refer to the x and y coordinates (in terms of longitude and latitude), and subsequent columns contain the abandonment age values for an individual year (where years are labeled with 'y' followed by the year, e.g., 'y1987'). Maps are given with a latitude and longitude coordinate reference system. Folder contains observed age, potential age (“_potential”), maximum age (“_max”), and recultivation lengths (“_recult”) for all sites. Maximum age .csv files include only three columns: x, y, and the maximum length (i.e., “max age”, in years) for each pixel throughout the entire time series (1987-2017). Files were produced using the custom functions 'cc_filter_abn_dt(),' “cc_calc_max_age(),' “cc_calc_potential_age(),” and “cc_calc_recult_age();” see '_util/_util_functions.R.' age_rasters.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for), for each year between 1987-2017 for all 11 sites. Maps are stored as .tif files, where each band corresponds to one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Folder contains observed age, potential age (“_potential”), and maximum age (“_max”) rasters for all sites. Maximum age rasters include just one band (“layer”). These rasters match the corresponding .csv files contained in 'age_dts.zip.” derived_data.zip - summary datasets created throughout this analysis, listed below. diff.zip - .csv files for each of our eleven sites containing the year-to-year lagged differences in abandonment age (i.e., length of time abandoned) for each pixel. The rows correspond to a single pixel of land, and the columns refer to the year the difference is in reference to. These rows do not have longitude or latitude values associated with them; however, rows correspond to the same rows in the .csv files in 'input_data.tables.zip' and 'age_dts.zip.' These files were produced using the custom function 'cc_diff_dt()' (much like the base R function 'diff()'), contained within the custom function 'cc_filter_abn_dt()' (see '_util/_util_functions.R'). Folder contains diff files for observed abandonment, potential abandonment (“_potential”), and recultivation lengths (“_recult”) for all sites. input_dts.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment (https://doi.org/10.1016/j.rse.2020.111873). Like “age_dts,” these maps are stored as .csv files, where each row is a pixel and the first two columns refer to x and y coordinates (in terms of longitude and latitude). Subsequent columns contain the land cover class for an individual year (e.g., 'y1987'). Note that these maps were recoded from Yin et al. 2020 so that land cover classification was consistent across sites (see below). This contains two files for each site: the raw land cover maps from Yin et al. 2020 (after recoding), and a “clean” version produced by applying 5- and 8-year temporal filters to the raw input (see custom function “cc_temporal_filter_lc(),” in “_util/_util_functions.R” and “1_prep_r_to_dt.R”). These files correspond to those in 'input_rasters.zip,' and serve as the primary inputs for the analysis. input_rasters.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment. Maps are stored as '.tif' files, where each band corresponds one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Maps are given with a latitude and longitude coordinate reference system. Note that these maps were recoded so that land cover classes matched across sites (see below). Contains two files for each site: the raw land cover maps (after recoding), and a “clean” version that has been processed with 5- and 8-year temporal filters (see above). These files match those in 'input_dts.zip.' length.zip - .csv files containing the length (i.e., age or duration, in years) of each distinct individual period of abandonment at each site. This folder contains length files for observed and potential abandonment, as well as recultivation lengths. Produced using the custom function 'cc_filter_abn_dt()' and “cc_extract_length();” see '_util/_util_functions.R.' derived_data.zip contains the following files: 'site_df.csv' - a simple .csv containing descriptive information for each of our eleven sites, along with the original land cover codes used by Yin et al. 2020 (updated so that all eleven sites in how land cover classes were coded; see below). Primary derived datasets for both observed abandonment (“area_dat”) and potential abandonment (“potential_area_dat”). area_dat - Shows the area (in ha) in each land cover class at each site in each year (1987-2017), along with the area of cropland abandoned in each year following a five-year abandonment threshold (abandoned for >=5 years) or no threshold (abandoned for >=1 years). Produced using custom functions 'cc_calc_area_per_lc_abn()' via 'cc_summarize_abn_dts()'. See scripts 'cluster/2_analyze_abn.R' and '_util/_util_functions.R.' persistence_dat - A .csv containing the area of cropland abandoned (ha) for a given 'cohort' of abandoned cropland (i.e., a group of cropland abandoned in the same year, also called 'year_abn') in a specific year. This area is also given as a proportion of the initial area abandoned in each cohort, or the area of each cohort when it was first classified as abandoned at year 5 ('initial_area_abn'). The 'age' is given as the number of years since a given cohort of abandoned cropland was last actively cultivated, and 'time' is marked relative to the 5th year, when our five-year definition first classifies that land as abandoned (and where the proportion of abandoned land remaining abandoned is 1). Produced using custom functions 'cc_calc_persistence()' via 'cc_summarize_abn_dts()'. See scripts 'cluster/2_analyze_abn.R' and '_util/_util_functions.R.' This serves as the main input for our linear models of recultivation (“decay”) trajectories. turnover_dat - A .csv showing the annual gross gain, annual gross loss, and annual net change in the area (in ha) of abandoned cropland at each site in each year of the time series. Produced using custom functions 'cc_calc_abn_diff()' via 'cc_summarize_abn_dts()' (see '_util/_util_functions.R'), implemented in 'cluster/2_analyze_abn.R.' This file is only produced for observed abandonment. Area summary files (for observed abandonment only) area_summary_df - Contains a range of summary values relating to the area of cropland abandonment for each of our eleven sites. All area values are given in hectares (ha) unless stated otherwise. It contains 16 variables as columns, including 1) 'site,' 2) 'total_site_area_ha_2017' - the total site area (ha) in 2017, 3) 'cropland_area_1987' - the area in cropland in 1987 (ha), 4) 'area_abn_ha_2017' -

The Multi-Resolution Land Characteristics (MRLC) project was established to provide multi-resolution land cover data of the conterminous United States from local to regional scales. A major component of MRLC is an objective to develop a national 30-meter land cover characteristics data base using Landsat thematic mapper (TM) data. This is a cooperative effort among six programs within four U.S. Government agencies, including the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program; the U.S. Geological Survey's (USGS) National Water Quality Assessment Program; the National Biological Service's Gap Analysis Program; the USGS' Earth Resources Observation Systems (EROS) Center; the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; and the EPA's North American Landscape Characterization project.

Multitemporal scenes were selected for the eastern deciduous forests, agricultural regions, and selected other regions. Multitemporal pairs were selected to be in consecutive seasons (in 1992 when possible). All scenes were previewed for image quality.

The participating agencies organized the joint purchase of a single national set of Landsat TM scenes. In addition, the cooperators developed a common definition for preprocessing the satellite data. The shared, consistently processed TM data are the foundation for the development of the national 30-meter land cover data base. The jointly acquired data are archived and distributed by EROS. A variety of products are available to MRLC participants, to their affiliated users, and to the general public.

Multi-Resolution Land Characterization 2001 (MRLC 2001) At-Sensor Reflectance Dataset is a second-generation federal consortium to create an updated pool of nation-wide Landsat imagery, and derive a second-generation National Land Cover Database (NLCD 2001).

The MRLC 2001 data cover the United States, including Alaska and Hawaii. Multi-temporal scenes may also be available, depending on the location. Most of the images are of high quality, and cloud cover is generally less than ten percent. The data will also include a 30-meter Digital Elevation Model (DEM) for all scenes that do not include the Canadian or Mexican borders.

Information which constitutes the geography or location of a land unit, farm, ranch or facility. This could include latitudinal/longitudinal points, boundaries, borders, addresses.

A Traveller reception area is generically defined as any land that is permanently or intermittently developed for the passage or stay of Travellers. These lands have the common characteristic of being realized and managed by a community that can be either a commune, a group of communes, or an intercommunality. Some of them benefit from a state subsidy. A reception area for Travellers may be included in the departmental reception plan for Travellers (reception areas and areas of large passages) or not (areas of small passages and rental family grounds). There are municipalities that have set up reception grounds for Travellers outside the departmental schemes. Reception areas, areas of large passages and small passages and rental family land are the four types of land representative of public action for the reception of Travellers. Areas registered in the SDAGV benefit from aid granted by the State by prefectural order. This assistance may be supplemented by subsidies from the region, the department and family allowance funds. The financial contribution of the State concerns the investments necessary for the development and rehabilitation of permanent reception areas up to 70% of the expenses incurred within 2 years following the publication of the scheme. State aid for the management of reception areas may complement investment aid. Similarly, the law allows the department to participate up to a maximum of 25% of the operating costs of the areas. Reception area for Travellers geolocated in the town's centroid - source: DDT/SHC/AH - validity: 01/09/2014

Description

The PARCELS layer depicts mapped land parcels or real estate properties within Chester County. This data contains geometric representations capturing the general size, shape and location for all of the real estate properties, which can be mapped, on the County's land surface.

ContactsTim Cassel, County of Chester

Tcassel@chesco.org, 610-344-5441, 313 W. Market St Suite 5302 West Chester, PA 19380ResourceUpdate frequency of dataset: Weekly Last Update Date: August 2023Accuracy of attribute: Attributes are correct as per County of Chester Assessment DepartmentPositional accuracy of dataset:The PARCELS layer (and derived copies PARCEL_PUBLISH and PRCL_POLY) was originally created through a conversion process of digitizing hand drawn tax maps into digital form and orientating the tax map sheets and parcels to the features depicted in the 1993 orthophotos. These parcel polygon features were adjusted based on aerial photography dated April 7, 1993, and road centerlines, right-of-way lines and other physical features generated from the same photography. While the imagery has a pixel resolution of 1.5 square feet, a positional accuracy of +/- 5 feet and is designed for use at a scale of 1 inch = 200 feet, the resulting PARCELS horizontal accuracy varies from reasonably accurate in the best cases to less accurate in the worst cases. Subsequent and current map update processes include metes and bounds entry from subdivision and deed information, as well as property corner locations based on GPS points and the utilization of our newest imagery datasets. While the update processes include more accurate methods, the resultant overall accuracy is still dependent on the surrounding parcel fabric's varied accuracy. Planimetric coordinates are based on the Pennsylvania State Plane Coordinate System South Zone and North American Datum 1983 Spatial ReferenceType: ProjectedGeographic Coordinate Reference: GCS North America 1983Projected Coordinate System: NAD 1983 State Plane Pennsylvania SouthLinear Unit: US Survey Feet

Note: There is a difference between land use code and zoning. Land management occurs via a land use designation which determines the land use code. This designation specifies how land and resources within it is managed. Zoning designations define how a property can be developed and used and it is controlled by each township/borough. See below for list of codes.Field DescriptionUPI - Uniform Parcel Identification Number, a unique value given to each parcelPIN_MAP - A Tie-back parcel identification number used to link UPI values to the parcel on which they residePIN_COMMON - A variation of UPI which adds and E for Exempt, U for Utility or T for TrailerPIN_ASMNT - A long form of the UPI number used for assessment purposesCODE (see below for list of codes)MULTI_POLY - Number listing for each parcel of a Multi-Poly parcel. (Example 1 of 20, 2 of 20)POLY_PER_PARCEL - Number of polygons that make up a parcel (0 indicates it is just one polygon)SQFT_PLAN_TOT - The square footage of a parcel (taken from the recorded plan or deed)ACRE_PLAN_TOT - The acreage of a parcel (taken from the recorded plan or deed)ACRE_PLAN_POLY - The acreage of the polygon depicting the parcel, derived by mapping softwareXCOORD - X coordinate of the centroid of the polygonYCOORD - Y coordinate of the centroid of the polygonLOC_ADDRESS - The location address for a parcelMUNI - The municipality the parcel resides inST_NUM - Street Number of the Situs AddressNUM_SUF - Street Number Suffix of the Situs AddressDIR - Street directional of the Situs Address (N, E, S, W)ST_NAME - Street Name of the Situs AddressST_TYPE - Street thorough fair type of the Situs AddressUNIT - Unit Type (see codes below)UNIT_N - Unit numberAD_ROLE - Situs Address Functional Role (see below for list of codes)NO_ADDR_CODE - No longer usedTILE_1200 - Outdated reference system, no longer usedTILE_2400 - Outdated reference system, no longer usedSUBDIV_NUMBER - Number assigned by Recorder of Deeds to a recorded documentLOT - Lot number given to parcel on the recorded planLAND_DEV_ID - Number assigned by Planning Commission for their tracking purposesCREATE_DATE - Date the parcel was mapped into the inactive parcel layerACTIVE_DATE - Date the parcel was added to the active parcel layerOWN1 - Owner of recordOWN2 - Owner of record if more than oneADDR1 - Component of the mailing address on recordADDR2 - Component of the mailing address on recordADDR3 - Component of the mailing address on recordZIP1 - Zipcode of mailing address on recordTAXYR - For Assessment purposesJURIS - Municipality Number where parcel is taxedBOOK - The book number of the recorded deedPAGE - The page number of the recorded deedDEED_REC_DATE - Date the latest deed was recordedLEGAL1 - Description of property location according to the deedLEGAL2 - Description of the taxable property and buildings according to the deedLUC - Landuse code assigned by assessment office. (unrelated to municipal zoning of a parcel) ((see below for list of codes))CLASS - General landuse classification assigned by assessment office (see below for list of codes)LOT_ASSESS - Assessed value of the landPROP_ASSESS - Assessed value of the structuresTOT_ASSESS - Assessed value of the combination of land and structuresFMV319 - Fair Market Value if not under 319FMV515 - Fair Market Value if not under 515LAST_SALE_PRICE - Last recorded sale priceASMNT_DATE - For Assessment Department use onlySCHDIST - The school district within which the parcel resides (see below for list of codes)SUBDIV_NAME - Name of the recorded planPLAN_NUM - Number assigned by the Planning Commission for their tracking purposesMODIFY_DATE - Last edit date of the parcel Data Dictionary Code1000= Parcel1010= Condominium (mother or host parcel)1011= Common Law Condo1020= Hydrography1031= Paper ROW1033= Land polygon with a ROW1034= Land dedicated to an existing ROW1035=- Private Road1040= Open Space1042= Common Area1043= Open Space owned by Homeowners Association1710= Parcel Assessed in Lancaster County but located in Chester County1711= Parcel Assessed in Berks County but located in Chester County1712= Parcel Assessed in Montgomery County but located in Chester County1713= Parcel Assessed in Delaware County but located in Chester County1714= Parcel Assessed in New Castle County but located in Chester County1715= Parcel Assessed in Cecil County but located in Chester County2000= Parcel bisected by a ROW (Duplicate Pin)2040= Open Space (Duplicate Pin)2042= Common Area (Duplicate Pin)2043= Open Space owned by Homeowners Association2010= Condominium (Duplicate Pin) LUCC-10= Banks, Savings&LoanC-20= Gas StationC-30= Restaurants, Stores (Retail)C-35= Condominium StoresC-40= Motels, HotelsC-41= Nursing HomesC-50= Shopping CentersC-60= Office Bldgs/Laboratory/LibraryC-65= Office CondoC-67= Office Condo Common LawC-70= Commercial Garage/Shop/Car DealersC-80= WarehouseC-81= Storage TanksC-90= Entertainment, RecreationC-91= Recreation (Private)C-92= Mobile Home Parks (4+)C-93= Burial Grounds/MausoleumC-94= AirportsC-95= Private SchoolsC-96= Commercial OBY onlyE-10= ChurchesE-11= CemetariesE-12= Service ConnectedE-13= Chester County PropertyE-20= SchoolsE-30= Public UtilitiesE-40= RailroadsE-50= Hospitals, etcE-60= StateE-61= State ParksE-62= FederalE-63= FederalE-70= Local Government (Townships & Boroughs)E-71= Local Government ParksE-80= Non-Profit OrganizationsE-90= Fire CompaniesF-10= Farm 10-19.99 AcresF-20= Farm 20-79.99 AcresF-40= Farm 80 Acres and overM-10= Heavy IndustrialM-20= Light IndustrialM-25= Light Industrial CondominiumM-30= Quarry/LandfillN-01= Not assessed in Chester CountyR-10= Single Family/CabinR-20= Two FamilyR-30= Multi Family/Dorms/SingleR-40= Apartments (4-19 Units)R-50= CodominiumR-55= Town House (Common Law Condo)R-60= Dwelling W/Comm Use Primary RR-61= Dwelling W/Comm Use Primary CR-70= Mobile HomeR-80= Barns, Stables, Pools, Misc BldgR-90= Apartment Complex (20 or more units)R-95= Common Elements (Not Open Space)T-10= Trailers and Mobile HomesU-03= Public UtilitiesU-04= RailroadsV-10= Vacant Land ResidentialV-11= Vacant Land CommercialV-12= Open SpaceV-13= Road Beds, Right of Way, Access WayV-14= Basins, Drainage ControlV-35= Condo Store/Vacant Common ElementV-50= Condo/Vacant Common ElementV-55= Condo Common Law/Common AreaV-65= Condo Office/Vacant Common ElementV-67= Condo Common Law Office/Common AreaClassA= ApartmentC= CommercialE= ExemptF= FarmI= IndustrialR= ResidentialU= Utility

SchDist1= Avon Grove2= Coatesville3= Downingtown4= Kennett Consolidated5= Octorara6= Owen J. Roberts7= Oxford8= Phoenixville9= Twin Valley10= Unionville Chaddsford11= West Chester12= Great Valley13= Tredyffrin-Easttown14= Springford

The Register provides information on the availability of surplus land for those government departments and their sponsored bodies which fall under the responsibility of English Ministers. The Register is also used on a voluntary basis by NHS trusts and Welsh Government. The land records are presented as points data. This dataset does not include the land parcel boundaries. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land.

The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other data extracts.

The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas. Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise. The Register helps to ensure that wider Government objectives, including housing needs are factored into land disposal decisions. Through the Register, the disposing body provides details of the site and there is a window of 40 working days during which certain public sector bodies can identify new uses for the land. The dataset available on data.gov.uk covers all sites that are outside the 40 working day ‘window’. Such sites may be included in the disposal strategies that have been published by a number of individual government departments. In addition, these sites may now be ‘on the market’ and being actively marketed. The ‘Register of Public Sector Land’ dataset provides details of any sites that are being disposed of through the Register of Surplus Public Sector Land and are outside the 40 working day ‘window’ Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other datasets.

This table contains the information about the land including land sizes (square feet & acres) and land property type for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.

Future Land Use designation based on zoning dataset. It shows the categories of land uses desired over time, and their intensities. The map reflects the land uses that correspond to the long term vision, goals and policies expressed in the master plan, and it constitutes the most direct link between the Master Plan and the Comprehensive Zoning Ordinance. It is important to note, however, that the Future Land Use Map is not a zoning map and it does not govern design or function.Zoning regulates land use to promote smart growth and preserve the quality of life in communities. Permitted Use are allowed by right, subject to compliance with appropriate standards. Conditional Use require City Planning Commission review with a recommendation forwarded to the City Council for final action.

Locations to access DEEP property including state parks, forests, and wildlife management areas.

This map layer consists of federally owned or administered lands of the United States, Puerto Rico, and the U.S. Virgin Islands. Only areas of 640 acres or more are included. There may be private inholdings within the boundaries of Federal lands in this map layer. This is a revised version of the January 2005 map layer.

The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study areas of this activity are in Chippewa, Eau Claire, Jackson, Monroe, Trempealeau, and Wood counties in west central Wisconsin. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for these study area counties. Locations of industrial sand mines and processing plants (vector features) were obtained from the Wisconsin Department of Natural Resources at http://dnr.wi.gov/topic/Mines/ISMMap.html, and from the Wisconsin Center for Investigative Journalism, October 2012, update at https://fusiontables.google.com/DataSource?docid=17nDFI4iUPOdyDOEWU7Vu1ONMiVofa3aWR_Gs-Zk#rows:id=1. These features were used to spatially validate some of the mining locations that were predefined with Landsat-detected mining locations (polygons). Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1961-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas. Lidar point cloud data and high-resolution (1-3 m) lidar DEMs were acquired for the Wisconsin six-county study area from Chippewa County Land Records Division, Chippewa Falls, WI; Eau Claire County, Eau Claire, WI; Jackson County and Jackson County Land Information Council, Black River Falls, WI; Monroe County, Sparta, WI; Trempealeau County, Whitehall, WI; and Wood County Planning and Zoning, Wisconsin Rapids, WI. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study areas. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions collected for Chippewa County, WI on May 15, 2011 and April 14, 2012; Eau Claire County, WI in 2013; Jackson County, WI in April, 2015; Monroe County, WI April 11-12, 2010; Trempealeau County, WI April 26, 2014 to May 5, 2014; and Wood County, WI March 21-31, 2015. The most recent difference analysis consisting of a raster dataset time span (2008-2015 date range) was analyzed by differencing the Wisconsin lidar-derived DEMs and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the IFSAR/lidar difference dataset. Chippewa County lidar DEM metadata reported the root mean square error (RMSE) of 0.083 m. Eau Claire County lidar DEM metadata described an RMSE of 18.5 cm that supports 2 ft contours. Jackson County lidar DEM metadata reported that a comparison of the ground survey versus lidar model values indicated an RMSE of 0.214 ft (0.065 m). Monroe County lidar DEM metadata was obtained from the U.S. Interagency Elevation Inventory, which indicated an RMSE of 0.106 m. Trempealeau County lidar DEM included metadata describing RMSE values for different land cover types. A comparison of the Trempealeau ground survey versus lidar model values indicated an overall vertical RMSE of 0.344 ft (0.105 m). An RMSE was reported for each of the following land cover types in Trempealeau County: Urban: 0.169 US Survey Feet (0.051 m); Low Grass: 0.150 US Survey Feet (0.046 m); Tall Grass: 0.489 US Survey Feet (0.149 m); Low Trees: 0.432 US Survey Feet (0.132 m); Tall Trees: 0.342 US Survey Feet (0.104 m). This allowed additional refinement of the spatially explicit threshold values. Wood County lidar DEM RMSE was obtained from the US Interagency Elevation Inventory (0.122 m).References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.