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. Edge refers to the linear topological primitives that make up MTDB. The All Lines Shapefile contains linear features such as roads, railroads, and hydrography. Additional attribute data associated with the linear features found in the All Lines Shapefile are available in relationship (.dbf) files that users must download separately. The All Lines Shapefile contains the geometry and attributes of each topological primitive edge. Each edge has a unique TIGER/Line identifier (TLID) value.

This layer contains various census levels 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, BLOCK, BLKGRP, and TBLKGRP.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual tract level, since this data has been protected using differential privacy.**To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual tracts will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the tract itself.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

Highlights:» Search for grave by burial name» View photos of headstones if available» Graves marked for veteran status

Observations and subtle shifts of vegetation communities in western Lake Erie have USGS researchers concerned about the potential for Grass Carp to alter these vegetation communities. Broad-scale surveys of vegetation using remote sensing and GIS mapping, coupled with on-the-ground samples in key locations will permit assessment of the effect Grass Carp may have already had on aquatic vegetation communities and establish baseline conditions for assessing future effects. Existing aerial imagery was used with object-based image analysis to detect and map aquatic vegetation in the western basin of Lake Erie.

Download .zipThis coverage was extracted from the 1994 statewide land cover inventory of Ohio produced by Bruce R. Motsch and Gary M. Schaal of the Ohio Department of Natural Resources.

The land cover inventory for the State of Ohio was produced by the digital image processing of Landsat Thematic Mapper Data. The Thematic Mapper is a multi-spectral scanner that collects electromagnetic radiation reflected from the earth's surface in the visible, near infrared and mid-infrared wavelength bands. The resolution of the Thematic Mapper data is a 30 meter by 30 meter cell. The computer analysis of the data isolates unique spectral classes that relate to land cover characteristics.

The land cover inventory was produced from Thematic Mapper data acquired in September and October 1994. The data was classified into the general land cover categories of urban, agriculture/open urban areas, shrub/scrub, wooded, open water, non-forested wetlands and barren.

The land cover information reflects the conditions of the satellite data during the specific year and season the data was acquired. The Thematic Mapper data was processed using ERDAS image processing software. The data was originally created in raster format and georeferenced to Universal Transverse Mercator (UTM) zone 17 coordinates NAD27. The data can be combined with other georeferenced digital data layers.

The data is also available in its original ERDAS image format.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

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Export of the parcel layer after each year's assessment roll in closed in March

Represents the combination of Parcels, Condos, and BSA Data into one layer.

Download .zipThe Ohio Wetlands Inventory is based on analysis of satellite data by Bruce R. Motsch and Gary M. Schaal and is intended solely as an indicator of wetland sites for which field review should be conducted. The satellite data reflect conditions during the specific year and season the data was acquired and all wetlands may not be indicated. Statistics generated from the inventory are intended solely as an approximation.

The Ohio Wetland Inventory for Ottawa county was produced from May 1985 Landsat Thematic mapper data (cell size 30 meters by 30 meters) using ERDAS Image processing software. The raster data has been converted to ARC/INFO format and exported to an interchange file.

The data was originally georeferenced to UTM zone 17 coordinates NAD 27 and is also available in this coordinate system in ERDAS Imagine format.

The class of woods on hydric soils, wet meadow and farmed wetland fall on hydric soils when digital soils data is available for the county.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

This is the final product after PDM editors create, split, combine property features from March 2013.

The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. NOTE: This map has been updated as of 1/16/24 to use a newer version of the data layer which contains more cities than it previously did. As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. Map opens in Sacramento, CA. Use bookmarks or the search bar to get to other cities.Cities included in this mapAlabama: Birmingham, Mobile, MontgomeryArizona: PhoenixArkansas: Arkadelphia, Batesville, Camden, Conway, El Dorado, Fort Smith, Little Rock, Russellville, TexarkanaCalifornia: Fresno, Los Angeles, Oakland, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: Boulder, Colorado Springs, Denver, Fort Collins, Fort Morgan, Grand Junction, Greeley, Longmont, PuebloConnecticut: Bridgeport and Fairfield; Hartford; New Britain; New Haven; Stamford, Darien, and New Canaan; WaterburyFlorida: Crestview, Daytona Beach, DeFuniak Springs, DeLand, Jacksonville, Miami, New Smyrna, Orlando, Pensacola, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Columbus, Macon, SavannahIowa: Boone, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Dubuque, Sioux City, WaterlooIllinois: Aurora, Chicago, Decatur, East St. Louis, Joliet, Peoria, Rockford, SpringfieldIndiana: Evansville, Fort Wayne, Indianapolis, Lake County Gary, Muncie, South Bend, Terre HauteKansas: Atchison, Greater Kansas City, Junction City, Topeka, WichitaKentucky: Covington, Lexington, LouisvilleLouisiana: New Orleans, ShreveportMaine: Augusta, Boothbay, Portland, Sanford, WatervilleMaryland: BaltimoreMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Fall River, Fitchburg, Haverhill, Holyoke Chicopee, Lawrence, Lexington, Lowell, Lynn, Malden, Medford, Melrose, Milton, Needham, New Bedford, Newton, Pittsfield, Quincy, Revere, Salem, Saugus, Somerville, Springfield, Waltham, Watertown, Winchester, Winthrop, WorcesterMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Austin, Duluth, Mankato, Minneapolis, Rochester, Staples, St. Cloud, St. PaulMississippi: JacksonMissouri: Cape Girardeau, Carthage, Greater Kansas City, Joplin, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Elizabeth City, Fayetteville, Goldsboro, Greensboro, Hendersonville, High Point, New Bern, Rocky Mount, Statesville, Winston-SalemNorth Dakota: Fargo, Grand Forks, Minot, WillistonNebraska: Lincoln, OmahaNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen County, Camden, Essex County, Monmouth, Passaic County, Perth Amboy, Trenton, Union CountyNew York: Albany, Binghamton/Johnson City, Bronx, Brooklyn, Buffalo, Elmira, Jamestown, Lower Westchester County, Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Schenectady, Staten Island, Syracuse, Troy, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorain, Portsmouth, Springfield, Toledo, Warren, YoungstownOklahoma: Ada, Alva, Enid, Miami Ottawa County, Muskogee, Norman, Oklahoma City, South McAlester, TulsaOregon: PortlandPennsylvania: Allentown, Altoona, Bethlehem, Chester, Erie, Harrisburg, Johnstown, Lancaster, McKeesport, New Castle, Philadelphia, Pittsburgh, Wilkes-Barre, YorkRhode Island: Pawtucket & Central Falls, Providence, WoonsocketSouth Carolina: Aiken, Charleston, Columbia, Greater Anderson, Greater Greensville, Orangeburg, Rock Hill, Spartanburg, SumterSouth Dakota: Aberdeen, Huron, Milbank, Mitchell, Rapid City, Sioux Falls, Vermillion, WatertownTennessee: Chattanooga, Elizabethton, Erwin, Greenville, Johnson City, Knoxville, Memphis, NashvilleTexas: Amarillo, Austin, Beaumont, Dallas, El Paso, Forth Worth, Galveston, Houston, Port Arthur, San Antonio, Waco, Wichita FallsUtah: Ogden, Salt Lake CityVirginia: Bristol, Danville, Harrisonburg, Lynchburg, Newport News, Norfolk, Petersburg, Phoebus, Richmond, Roanoke, StauntonVermont: Bennington, Brattleboro, Burlington, Montpelier, Newport City, Poultney, Rutland, Springfield, St. Albans, St. Johnsbury, WindsorWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Madison, Milwaukee County, Oshkosh, RacineWest Virginia: Charleston, Huntington, WheelingAn example of a map produced by the HOLC of Philadelphia:

Purpose:To provide a spatially accurate, topologically correct representation of the road network within Wood, Lucas, Ottawa, Seneca, Hancock, Henry, and Fulton Counties, supporting general mapping, transportation planning, geocoding, and roadway inventory.Supplemental Information:This dataset is part of the Ohio Location Based Response System (LBRS) and adheres to its standards for accuracy and topological correctness. It is designed to assist with public safety, transportation planning, and roadway management across multiple counties.Keywords:Road Centerline, LBRS, Transportation, General Mapping, Geocoding, Roadway Inventory, Multi-County, Ohio.Lineage:Source(s): Data collected through the Ohio LBRS with contributions from local authorities across Wood, Lucas, Ottawa, Seneca, Hancock, Henry, and Fulton Counties.Process Steps: Includes GPS field data collection, digitization, topology enforcement, validation, and periodic updates across all represented counties.Accuracy and Consistency:Positional Accuracy: Consistent with LBRS standards, verified through GPS and county-level records.Attribute Accuracy: Street names, classifications, and jurisdiction verified against official records for each county.Logical Consistency: Maintains topological integrity, providing a continuous, non-overlapping road network.Data Type:Vector (Polyline)Composition:Polylines representing road centerlines with attributes like street names, jurisdiction, surface type, and classification.Spatial Reference:Coordinate System: NAD 1983 NSRS2007 StatePlane Ohio North FIPS 3401 (US Feet)Projection: Lambert Conformal ConicDatum: NAD 1983 NSRS2007Time Period of Content:Reflects conditions as of the LBRS and county GIS updates at the time this was produced. This is a preexisting dataset created before the current GIS Manager.Currentness Reference:Updated as needed for new developments, closures, or reclassifications across all included counties, in alignment with LBRS standards.Contacts:Primary Contact:David L. Price, GIS Coordinator, Wood County GIS DepartmentPhone: (419) 373-3984Email: dprice@woodcountyohio.govDistributor:Available upon request through Wood County GIS or the LBRS data distribution network.Distribution Liability:Wood County, LBRS, and ODOT disclaim responsibility for improper or incorrect dataset use, provided "as-is" without warranty.Metadata Date:October 21, 2024Metadata Review Date:October 21, 2025Metadata Contact:David L. Price, (419) 373-3984, dprice@woodcountyohio.gov