This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.

"""District boundaries: When the zoning map shows a zoning district boundary as following a particular feature, or reflects a clear intent that the boundary follows the feature, the boundary will be construed as following that feature as it actually exists.Map interpretations: Where any uncertainty exists about a zoning boundary, the actual location of the boundary will be determined by the Zoning Administrator using the following rules of interpretation.(1) A boundary shown on the zoning map as approximately following a river, stream, lake or other watercourse will be construed as following the actual centerline of the watercourse. If, subsequent to the establishment of the boundary, the centerline of the watercourse should move as a result of natural processes (flooding, erosion, sedimentation, etc.), the boundary will be construed as moving with the centerline of the watercourse.(2) A boundary shown on the zoning map as approximately following a ridge line or topographic contour line will be construed as following the actual ridge line or contour line. If, subsequent to the establishment of the boundary, the ridge line or contour line should move as a result of natural processes (erosion, slippage, subsidence, etc.), the boundary will be construed as moving with the ridge line or contour line.(3) A boundary shown on the zoning map as approximately following lot lines or other parcel boundaries assigned by the Will County Supervisor of Assessments will be construed as following such lot lines or parcel boundaries.(4) A boundary shown on the zoning map as approximately following a street or railroad line will be construed as following the centerline of the street or railroad right-of-way.(5) A boundary shown on the zoning map as approximately following the boundary of an adjacent municipality will be construed as following that boundary.(6) A boundary shown on the zoning map as approximately parallel to, or as an apparent extension of, a feature described above will be construed as being actually parallel to, or an extension of, the feature.(7) Zoning boundaries that do not coincide with a property line, parcel boundary, landmark or particular feature will be determined with a scale.(8) It is the intent that the entire unincorporated area of the county, including all land and water areas, rivers, streets, alleys, railroads, and other right-of-ways, be included in the districts established by this zoning ordinance. If any area is not shown on the zoning map as being included in a zoning district, it will be deemed to be classified in the E-1 district until otherwise reclassified by a zoning map amendment in accordance with chapter 155-16.30."""

Parcels with tax assessor data for SSMMA region. See Code Definitions for Cook County parcel classification details.CAVEAT EMPTOR: Cook County data from 2016, Will County data from 2017.We are currently in the process of collecting Cook County data and processing Will County data for 2019.For up-to-date parcel tax records, visit the Cook County GIS Viewer or Will County GIS Viewer, or access the Cook County Tax Assessor's databases [1] [2] or the Will County Tax Assessor's Office.Municipalities in SSMMA's GIS Consortium may contact the GIS staff if more recent data is available to display on municipal viewers.

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. (See entry under the Advisory section concerning additional review being done on NHD waterbody attribute coding and impervious surface estimation.) ADVISORY This data set, edition 20231120, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, however, that there is continuing review being done on this layer to update several additional attributes not presently evaluated in this edition. These attributes include several from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Those NHD specific attributes are still being evaluated and may be added to a future edition of this base data set. Similarly, additional review is being done to assess the feasibility of incorporating data on impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. As the additional attributes are evaluated, they may be added to a future edition of this data set. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.The data for Somerset County data was extracted & processed from the latest dataset by the Somerset County Office of GIS Services (SCOGIS).

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Download In State Plane Projection Here. Boundaries of designated high quality ADID wetlands established as a result of a formal process under the direction of the U.S. Environmental Protection Agency. Part 404(b)(1) of the Clean Water Act authorizes the USEPA and the US Army Corps of Engineers to identify in advance of specific permit requests aquatic sites which will be considered as areas generally unsuitable for disposal of dredged or fill material. This process is called an Advanced Identification or ADID. Under the ADID process identification of an area as generally unsuitable for fill does not prohibit applications for permits to fill in these areas. Therefore the ADID designation of unsuitability is advisory not regulatory. An ADID designation lets a potential applicant know in advance that a proposal to fill such a site is not likely to be consistent with the 404(b)(1) guidelines, and the USEPA will probably request permit denial. ADID wetland information is also useful in watershed planning, land use planning, public land acquisition programs, natural resource studies and other purposes. The wetland selection criteria and methodology are documented in the publication entitled "Advanced Identification (ADID) Study, Lake County, Illinois. Final Report, November 1992" which is included in this download. Boundaries were delineated by the ADID project team on orthophotograph background with an intended usage scale of 1" = 400', a scale ratio of 1:4800.

This dataset contains street centerlines for vehicular and foot traffic in Allegheny County.

Street Centerlines are classified as Primary Road, Secondary Road, Unpaved Road, Limited Access Road, Connecting Road, Jeep Trail, Walkway, Stairway, Alleyway and Unknown. A Primary Road is a street paved with either concrete or asphalt that has two (2) or more lanes in each direction. A Secondary Road is a residential type hard surface road, or any hard surface road with only one (1) lane in each direction. An Unpaved Road is any road covered with packed dirt or gravel. A Limited Access Road is one that can only be accessed from a Connecting Road such as an Interstate Highway. A Connecting Road is a ramp connecting a Limited Access Road to a surface street. A Walkway is a paved or unpaved foot track that connects two (2) roads together. Walkways within College Campuses will also be shown. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. Walkways will not have an Edge of Pavement feature. A Stairway is a paved or wooden structure that connects two (2) roads together. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. An Alleyway is a road, usually narrower than a Secondary Road that runs between, but parallel to, two (2) Secondary Roads. Generally, Outbuildings will be adjacent to Alleyways. A Jeep Trail is a vehicular trail used for recreation. A Jeep Trail will not have an associated edge of pavement feature. A road coded as Unknown is a road, which in the judgment of the photogrammetrist, does not fall into any of the categories listed.

Centerlines will be visually placed between the edges of pavement. One (1) centerline will be placed between each edge of pavement. Roads with medial strips, such as Limited Access Roads, will have two (2) centerlines for those portions of the road where the medial strip is present. For roads that terminate with a cul-de-sac, the centerline shall continue through the center of the cul-de-sac and stop at the edge of pavement.

All attribute data will remain for all Street Centerlines that are not updated. For Street Centerlines that are new, the only attribute field that will be populated is the FeatureCode and UPDATE_YEAR. If a Street Centerline is graphically modified, the existing attribute data will remain and the UPDATE_YEAR will be set to 2004. The attribute values for 2004 Street Centerlines should be considered suspicious until verified.

The ArcInfo Street Centerline coverage that is being updated has 800 segments of Paper Streets, 66 segments of Vacated Streets and 78 segments of Steps. Street Centerlines that are coded as Paper Streets in the OWNER field will remain unchanged in the updated dataset unless the area has been developed. In the event the area has been developed, the Street Centerlines will be modified to reflect the true condition of the visible roads. Street Centerlines that are coded as Vacated in the OWNER field will also remain unchanged in the updated dataset. In the event the area coinciding with the Vacated Streets has been developed, the Vacated Street Centerlines will be removed in order to reflect the true condition of the area. Street Centerlines that are coded as Steps in the OWNER field will be updated to reflect the current condition of the area. The Street Centerlines dataset consists of an external table that links to the supplied coverages and the Geodatabase created for this project using the "-ID" (UserID) field. In order to maintain the link to the external table and not loose valuable data the decision was made to keep all database information currently in the Street Centerline dataset. When a Street Centerline is modified during the update process, the field "UPDATE_YEAR" is set to 2004. All other database attributes will remain unchanged from the original values. All Street Centerline database data with an "UPDATE_YEAR" of 2004 should be verified before used. In some occasions the Street Centerline was divided into two (2) sections to allow for a new road intersection. Both sections of the resulting Street Centerline will have the same database attributes including Address Range. All new Street Centerlines will have zero (0) for "SystemID" and "UserID".

This dataset was previously harvested from Allegheny County’s GIS data portal. The new authoritative source for this data is now the PASDA page (https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=1224), which includes links to historical versions of the shapefile representations of this data.

The 2002 Sierra County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Central District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and Central District. Important Points about Using this Data Set: 1. The land use boundaries were hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Not all land use codes will be represented in the survey.The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.3, dated April 13, 2022. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. See the CADWR Land User Viewer (gis.water.ca.gov/app/CADWRLandUseViewer) for the most current contact information. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.

The Hydrology Feature Dataset contains photogrammetrically compiled water drainage features and structures including rivers, streams, drainage canals, locks, dams, lakes, ponds, reservoirs and mooring cells. Rivers, Lakes, Ponds, Reservoirs, Hidden Lakes, Reservoirs or Ponds: If greater than 25 feet and less than 30 feet wide, is captured as a double line stream. If greater than 30 feet wide it is captured as a river. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Polygons are created from Stream edges and River Edges. The Ohio River, Monongahela River and Allegheny River are coded as Major Rivers. All other River and Stream polygons are coded as River. If a stream is less than 25 feet wide it is placed as a single line and coded as a Stream. Both sides of the stream are digitized and coded as a Stream for Streams whose width is greater than 25 feet. River edges are digitized and coded as River. A Drainage Canal is a manmade or channelized hydrographic feature. Drainage Canals are differentiated from streams in that drainage canals have had the sides and/or bottom stabilized to prevent erosion for the predominant length of the feature. Streams may have had some stabilization done, but are primarily in a natural state. Lakes are large standing bodies of water greater than five acres in size. Ponds are large standing bodies of water greater than one acre in size and less than five acres in size. Reservoirs are manmade embankments of water. Included in this definition are both covered and uncovered water tanks. Reservoirs that are greater than one acre in size are digitized. Hidden Streams, Hidden Rivers and Hidden Drainage Canal or Culverts are those areas of drainage where the water flows through a manmade facility such as a culvert. Hydrology Annotation is not being updated but will be preserved. If a drainage feature has been removed, as apparent on the aerial photography, the associated drainage name annotation will be removed. A Mooring Cell is a structure to which tows can tie off while awaiting lockage. They are normally constructed of concrete and steel and are anchored to the river bottom by means of gravity or sheet piling. Mooring Cells do not currently exist in the Allegheny County dataset but will be added. Locks are devices that are used to control flow or access to a hydrologic feature. The edges of the Lock are captured. Dams are devices that are used to hold or delay the natural flow of water. The edges of the Dam are shown. 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: 2006 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: Original Lakes and Drainage datasets combined to create this layer. Data was updated as a result of a flyover in the spring of 2004. A database field has been defined for all map features named "Update Year". This database field will define which dataset provided each map feature. Map features from the current map will be set to "2004". The earlier dataset map features the earlier dataset map features used to supplement the area near the county boundary will be set to "1993". All new or modified map data will have the value for "Update Year" set to "2004". Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/16BWrRkoPtq2ANRkrbG7CrfQk2dUsWRiaS2Ee1mTn7l0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

Municipalities around Cook County have opened cooling centers for area residents as temperatures start to rise. Residents who need to seek shelter should contact the center first to verify it is open. This dataset will be no longer updated on May 1, 2019. Please refer to this link for future updates: https://datacatalog.cookcountyil.gov/dataset/Cook-County-Cooling-Centers-Map/dnn9-esge

'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.

This dataset is a compilation of tax parcel polygon and point layers from the seven Twin Cities, Minnesota metropolitan area counties of Anoka, Carver, Dakota, Hennepin, Ramsey, Scott and Washington. The seven counties were assembled into a common coordinate system. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. The primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

Polygon and point counts for each county are as follows (based on the January 2012 dataset unless otherwise noted):

polygons / points
Anoka - 129385 / 129385
Carver - 38699 / 38699
Dakota - 136792 / 150666
Hennepin - 424702 / 424702
Ramsey - 148978 / 166846
Scott - 55227 / 55227
Washington - 106841 / 106841

This is a MetroGIS Regionally Endorsed dataset.

Each of the seven Metro Area counties has entered into a multiparty agreement with the Metropolitan Council to assemble and distribute the parcel data for each county as a regional (seven county) parcel dataset.

A standard set of attribute fields is included for each county. The attributes are identical for the point and polygon datasets. Not all attributes fields are populated by each county. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes 2011 document.

Additional information may be available in the individual metadata for each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person listed in the individual county metadata.

Anoka = http://www.anokacounty.us/315/GIS

Caver = http://www.co.carver.mn.us/GIS

Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx

Hennepin: http://www.hennepin.us/gisopendata

Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data

Scott = http://www.scottcountymn.gov/1183/GIS-Data-and-Maps

Washington = http://www.co.washington.mn.us/index.aspx?NID=1606

Parcel Viewer makes searching for King County parcel information easy. You can search by address, search by parcel number, or you can just zoom in on the map and click on a parcel. Once a parcel is selected, you will get direct links to the King County Assessor’s eReal Property report and the Districts and Development Conditions report.

A complete, historic universe of Cook County parcels with attached geographic, governmental, and spatial data.

When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded.

Additional notes:

• Non-taxing district data is attached via spatial join (st_contains) to each parcel's centroid.
• Tax district data (school district, park district, municipality, etc.) are attached by a parcel's assigned tax code.
• Centroids are based on Cook County parcel shapefiles.
• Older properties may be missing coordinates and thus also missing attached spatial data (usually they are missing a parcel boundary in the shapefile).
• Newer properties may be missing a mailing or property address, as they need to be assigned one by the postal service.
• This dataset contains data for the current tax year, which may not yet be complete or final. Assessed values for any given year are subject to change until review and certification of values by the Cook County Board of Review, though there are a few rare circumstances where values may change for the current or past years after that.
• Rowcount for a given year is final once the Assessor has certified the assessment roll all townships.
• Data will be updated monthly.
• Depending on the time of year, some third-party and internal data will be missing for the most recent year. Assessments mailed this year represent values from last year, so this isn't an issue. By the time the Data Department models values for this year, those data will have populated.
• Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time.
• Due to discrepancies between the systems used by the Assessor and Clerk's offices, tax_district_code is not currently up-to-date in this table.

Read about the Assessor's 2025 Open Data Refresh.

This dataset contains photogrammetrically compiled roof outlines of buildings. All near orthogonal corners are square. Buildings that are less than 400 square feet are not captured. Special consideration is given to garages that are less than 400 square feet and will be digitized when greater than 200 square feet. Interim rooflines, such as dormers and party walls, as well as minor structures, such as carports, decks, patios, stairs, etc., and impermanent structures, such as sheds, are not shown. Large buildings which appear to house activities that are commercial or industrial in nature are shown as commercial/industrial. Structures that appear to be primarily residential in nature, including hotels and apartment buildings are shown as residential buildings. Structures which appear to be used or owned primarily by governmental, nonprofit, religious, or charitable organizations, or which serve a public function are shown as public buildings. Structures which are closely associated with a larger building, such as a garage, are shown as an out building. Structures which cannot be clearly defined as Industrial/Commercial; Residential; Public; or Out Buildings are flagged as such for later categorization. The classification of buildings is subject to the interpretation from the aerial photography and may not reflect the building’s actual use. Buildings that have an area less than the minimum required size for data capture will occasionally be present in the Geodatabase. Buildings are not removed after they have been digitized and determined to be less than the minimum required size.

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (https://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (https://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: Housing and Properties

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Information Technology

Temporal Coverage: current

Data Notes:

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

Development Notes: Data meets or exceeds map accuracy standards in effect during the spring of 1992 and updated as a result of a flyover in the spring of 2004 and 2015. Original data was derived from aerial photography flown in the spring of 1992 for the eastern half of the County and the spring of 1993 for the western half of the County. Photography was produced at a scale of 1"=1500'. Mapping was stereo digitized at a scale of 1"=200'.

Other: none

Related Document(s): Data Dictionary (none)

Frequency - Data Change: Daily

Frequency - Publishing: Nightly

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

'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 establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Range Improvements are man-made or man-caused features on the landscape designed and implemented for the purpose of improving the available forage, managing the season of use or use patterns and enhancing the overall rangeland health of areas available for domestic livestock use. Range improvements may occur on private, state, and public lands under the jurisdiction of the Bureau of Land Management (BLM) and/or other federal or state agencies. On public lands managed by the Bureau of Land Management (BLM), permittees or lessees (henceforth, “operators”) may be required to install range improvements to meet the terms and conditions of their permits or leases. Often the BLM, operators, and other interested parties work together and jointly contribute to construction.Range improvements are authorized physical modifications or treatment which are designed to improve production of forage; change vegetation composition; control patterns of use; provide water; stabilize soil and water conditions; restore, protect, and improve the conditions of the rangeland ecosystems to benefit livestock, wild horses and burros, and fish and wildlife. They include, but are not limited to, structures, treatment projects and use of mechanical devices or modifications achieved through mechanical means. Range Improvements. There are two kinds of range improvements: nonstructural and structural. Seeding or prescribed burns are examples of nonstructural range improvements. Fences or facilities such as wells or water pipelines are examples of structural improvements. Many structural improvements are considered permanent, as they are not easily removed from the land. This data standard will only relate to structural range improvements features as GIS and attribute data related to almost all non-structural range improvements is stored in other national data standard datasets (e.g. NISIMS, VTRT, NFPORS). Range improvement data is also available in the Range Improvement System (RIPS), a BLM database used for tracking the establishment and maintenance of range improvments. RIPS is the database of record and contains the data to be used for budgetary and workload planning. This data set shall be comprised of a spatial display of the data in RIPS. The record unique identifier within the RIPS database (RIPS number) will be added to GIS features in this data standard to link between spatial depictions of range improvements features to their corresponding RIPS records. Wherever possible RIPS data shall be used to populate this data set.