Link to Wisconsin Statewide Parcel Map Initiative data download page on the Wisconsin State Cartographer's website.

This dataset contains DNR Managed Lands as parcels with local property name, and GIS and deed acreages. Parcels are symbolized as fee simple (DNR Owned), DNR easement on private land (open/restricted public access) and DNR lease on federal- and county-owned land. This dataset does not contain closed fee or easement. See metadata/data dictionary for interest (transaction type) classification.The parcels are digitized from deed legal description and based on the DNR Landnet System (Public Land Survey System), Wisconsin Transverse Mercator. This data is updated on a weekly basis.This layer represents the geometry of the real estate holdings of the Wisconsin Department of Natural Resources and is not to be interpreted as representing legal property boundaries. Link to the Metadata and Data Dictionary.See also the Public Access Lands interactive mapping application.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Real estate property tax by parcel.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The Generalized Future Land Use (GFLU) Map makes recommendations for future land uses and development intensities to guide the physical development of Madison. The future land use categories guide what types of zoning can be applied, and ultimately what can be built in different parts of the city. For example, a parcel of land specified for future “Medium Residential” land use could be rezoned to allow for a multifamily apartment building but could not be rezoned to allow for industrial uses.

This layer is derived from the DNR Managed Lands dataset, dissolved on PROP_CODE. This layer contains fee simple (DNR owned) land, DNR easement on private land and DNR lease on federal- and county-owned land. DNR easements on privately held property are not included. See metadata/data dictionary for interest (transaction type) classification.This dataset contains land managed by DNR represented as property. Use DNR Managed Land Parcels for the individual parcel polygons. This dataset is updated on a weekly basis.

This layer represents the geometry of the real estate holdings of DNR Managed Lands and is not to be interpreted as representing legal property boundaries.Link to the Metadata and Data Dictionary.See also the Public Access Lands interactive mapping application

Depth-to-bedrock mapping of Grant County provides baseline information on unconsolidated surficial sediment thickness across the county. Bedrock aquifers in Grant County are susceptible to contamination from various land use activities, and depth to bedrock represents one of several factors affecting aquifer susceptibility. The results of this study produced a depth-to-bedrock raster that has a cell size of 32.8 ft (10 m). When used alongside water-table maps, bedrock and surficial geologic maps, as well as land use maps, depth to bedrock can help assess aquifer susceptibility.

State Tax Collections: T01 Property Taxes for Wisconsin was 48.00000 Mil. of U.S. $ in April of 2025, according to the United States Federal Reserve. Historically, State Tax Collections: T01 Property Taxes for Wisconsin reached a record high of 66.00000 in April of 2016 and a record low of -13.00000 in October of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for State Tax Collections: T01 Property Taxes for Wisconsin - last updated from the United States Federal Reserve on November of 2025.

This Coastal Barrier Resources System (CBRS) data set, produced by the U.S. Fish and Wildlife Service (Service), contains areas designated as undeveloped coastal barriers in accordance with the Coastal Barrier Resources Act (CBRA), 16 U.S.C. 3501 et seq., as amended. The boundaries used to create the polygons herein were compiled from the official John H. Chafee Coastal Barrier Resources System CBRS maps, which are accessible at the Service’s Headquarters office or https://www.fws.gov/program/coastal-barrier-resources-act/maps-and-data. These digital polygons are only representations of the CBRS boundaries shown on the official CBRS maps and are not to be considered authoritative. The Service is not responsible for any misuse or misinterpretation of this digital data set, including use of the data to determine eligibility for federal financial assistance such as federal flood insurance. As maps are revised, this data set will be updated with the new boundaries. CBRS boundaries viewed using the CBRS Mapper or the shapefile are subject to misrepresentations beyond the Service’s control, including misalignments of the boundaries with third party base layers and mis-projections of spatial data. The official CBRS map is the controlling document and should be consulted for all official determinations. Official determinations are recommended for all properties that are in close proximity (within 20 feet) of a CBRS boundary. For an official determination of whether or not an area or specific property is located within the CBRS, please follow the procedures found at https://www.fws.gov/service/coastal-barrier-resources-system-property-documentation. For any questions regarding the CBRS, please contact your local Service field office or email CBRA@fws.gov. Contact information for Service field offices can be found at https://www.fws.gov/our-facilities.Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Team Lead, richard_easterbrook@fws.gov

State Government Tax Collections, Property Taxes in Wisconsin was 104756.00000 Thous. of $ in January of 2024, according to the United States Federal Reserve. Historically, State Government Tax Collections, Property Taxes in Wisconsin reached a record high of 170537.00000 in January of 2016 and a record low of 13918.00000 in January of 1946. Trading Economics provides the current actual value, an historical data chart and related indicators for State Government Tax Collections, Property Taxes in Wisconsin - last updated from the United States Federal Reserve on November of 2025.

Tax parcel map of Wood County, Wisconsin. Parcels reference the public land survey system and are derived from a variety of sources including recorded deeds, subdivision plats, certified survey maps, right-of-way plats, and plats of surveys. These source materials were of several different scales and were from dates ranging from the early 1850's to the present. The maps are suitable for planning purposes and will be useful for assisting with land title, assessing and survey work.Parcel maps are neither a replacement for recorded documents that form the legal basis for parcel ownership nor should they replace a field survey. Wood County is providing the information on this web site as a public service. Use of this web site is at your own risk, and the County will not be held liable for any errors or omissions contained in this web site. The information contained on this map is based upon recorded deeds, plans, and other public sources. These primary sources should be consulted to verify the information contained on this map. Due to conflicts, errors, and omissions in the primary sources, the map should be considered as a representation of the editor's judgment, based upon the available evidence.NO INFORMATION ON THIS SITE IS INTENDED TO SERVE AS LEGAL EVIDENCE OF SIZE, SHAPE, LOCATION,OR OWNERSHIP OF REAL ESTATE. THIS MAP IS NOT A SURVEY.

The Soil Data Warehouse and Data Mart provide the authoritative pathway for distributing current official soil survey data to service centers and to the public. Interactive maps and dynamic reports available include Dynamic Maps

Query by Location Query by Location and Connect to Web Soil Survey Prototype Soil (Beta Version) MLRA Explorer

Lists of Hydric Soils

Query by State Query by Soil Survey Area Query by State Map Unit Rating Query by Soil Survey Area Map Unit Rating

Map Unit Description (Brief, Generated)

Query by Soil Survey Area and Map Unit (All Components) Query by Soil Survey Area (Major Components)

Ecological Site Extent

Query by MLRA and Ecological Site

Legend & Prime Farmland

Query by Soil Survey Area

Wisconsin Soil Moisture Regime Site Assessment Guide

Query by Soil Survey Area

Wisconsin Forage Suitability Group Guide

Query by Soil Survey Area

RUSLE2 Related Attributes

Query by Soil Survey Area Resources in this dataset:Resource Title: Soil Data Mart. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/wi/soils/?cid=nrcseprd1326315 Links pull information and hits from the official soils database live. Interactive maps and dynamic reports are available.

Market Hotness: Listing Views per Property in Kenosha County, WI was -15.23290 % Chg. in September of 2025, according to the United States Federal Reserve. Historically, Market Hotness: Listing Views per Property in Kenosha County, WI reached a record high of 67.73389 in January of 2022 and a record low of -25.09114 in May of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Market Hotness: Listing Views per Property in Kenosha County, WI - last updated from the United States Federal Reserve on November of 2025.

This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 ½ minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. These are areas of water that are defined from meander lines of the PLSS and GLO surveys. These are not the official representations of coast or water lines and are representations of the lines marked by the survey along the boundaries of meandered water at the time of survey.

These data are part of a larger USGS project to develop an updated geospatial database of mines, mineral deposits and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, are currently being digitized on a state-by-state basis from the 7.5-minute (1:24,000-scale) and the 15-minute (1:48,000 and 1:62,500-scale) archive of the USGS Historical Topographic Maps Collection, or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. To date, the compilation of 500,000-plus point and polygon mine symbols from approximately 67,000 maps of 22 western states has been completed: Arizona (AZ), Arkansas (AR), California (CA), Colorado (CO), Idaho (ID), Iowa (IA), Kansas (KS), Louisiana (LA), Minnesota (MN), Missouri (MO), Montana (MT), North Dakota (ND), Nebraska (NE), New Mexico (NM), Nevada (NV), Oklahoma (OK), Oregon (OR), South Dakota (SD), Texas (TX), Utah (UT), Washington (WA), and Wyoming (WY). The process renders not only a more complete picture of exploration and mining in the western U.S., but an approximate time line of when these activities occurred. The data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. The data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.

In the late 1880's and early 1900's the Mississippi River Commission (MRC) conducted an extensive high-resolution survey of the Mississippi River from Cairo, Illinois to Minneapolis, Minnesota. These data were published as a series of 89 survey maps and index. In the 1990's, the Upper Midwest Environmental Sciences Center (UMESC) in conjunction with the US Army Corps of Engineers Upper Mississippi River Restoration- Environmental Management Program -- Long Term Resource Monitoring Program element (LTRMP) for the Upper Mississippi River automated the maps' land cover/use symbology to create a turn of the century/pre-impoundment land cover/use data set. Other data on the maps that were not automated include; elevation contours, water depth soundings, proposed water control structures (e.g., wing dams), levees, benchmarks, railroads, and city streets.

Tax parcel map of Waupaca County, Wisconsin. The layer was derived from a variety of source maps including: mylar county parcel maps, plats of surveys, deed descriptions, subdivision maps, certified survey maps, highway right-of-way plats, and township highway right-of-way maps. These source materials were of several different scales and were from dates ranging from the early 1850's to the present. This map provides a useful representation of the geometry and topology of tax parcels and is suitable for its intended purpose. It is not, however, meant to be used for the determination of land ownership or to be in any way a substitute for the land ownership and interest descriptions contained in individual deeds. The tax parcel layer is available in a county-wide or individual layer for each township, city, or village.

City of Madison Tax Parcels.

This data layer is used by the Property Lookup, Neighborhood Development Plans, Neighborhood Development Plan Resource Viewer, Generalized Future Land Use, What is Density, City of Madison Historic Districts and Landmarks, Zoning Districts, Urban Design Districts and Development Information applications.NOTE: the City’s Open Data content is updated daily and is current to the best of our ability.Any “Last Updated Date” dates on the site reflect when the specific web page was updated – NOT the data.

Tax parcel boundary lines including parcel, lot, right-of way meander and water.