12 datasets found
  1. s

    Historical districts

    • data.sandiego.gov
    Updated Oct 9, 2017
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    (2017). Historical districts [Dataset]. https://data.sandiego.gov/datasets/historic-districts/
    Explore at:
    csv csv is tabular data. excel, google docs, libreoffice calc or any plain text editor will open files with this format. learn moreAvailable download formats
    Dataset updated
    Oct 9, 2017
    Description

    Historical districts contain multiple properties and/or objects that share historical significance. The City’s Historical Resources Board can establish a district if the contributing resources meet one of six criteria. For more information, read the nomination guidelines.

  2. San Diego 2024 Roll Year

    • hub.arcgis.com
    • gis.data.ca.gov
    • +2more
    Updated May 31, 2024
    + more versions
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    California Department of Tax and Fee Administration (2024). San Diego 2024 Roll Year [Dataset]. https://hub.arcgis.com/maps/51c182a3e7ab490a87574235c22eea05
    Explore at:
    Dataset updated
    May 31, 2024
    Dataset authored and provided by
    California Department of Tax and Fee Administrationhttp://cdtfa.ca.gov/
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    Tax rate area boundaries and related data based on changes filed with the Board of Equalization per Government Code 54900 for the specified assessment roll year. The data included in this map is maintained by the California State Board of Equalization and may differ slightly from the data published by other agencies. BOE_TRA layer = tax rate area boundaries and the assigned TRA number for the specified assessment roll year; BOE_Changes layer = boundary changes filed with the Board of Equalization for the specified assessment roll year; Data Table (C##_YYYY) = tax rate area numbers and related districts for the specified assessment roll year

  3. a

    Neighborhoods

    • data-uvalibrary.opendata.arcgis.com
    • hub.arcgis.com
    Updated Oct 15, 2015
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    The City of San Diego (2015). Neighborhoods [Dataset]. https://data-uvalibrary.opendata.arcgis.com/datasets/SanDiego::neighborhoods
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    Dataset updated
    Oct 15, 2015
    Dataset authored and provided by
    The City of San Diego
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Neighborhoods within the City of San Diego.

  4. O

    Historic Districts

    • data.sanantonio.gov
    • hub.arcgis.com
    • +1more
    Updated Apr 7, 2025
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    GIS Data (2025). Historic Districts [Dataset]. https://data.sanantonio.gov/dataset/historic-districts
    Explore at:
    arcgis geoservices rest api, zip, csv, gdbAvailable download formats
    Dataset updated
    Apr 7, 2025
    Dataset provided by
    City of San Antonio
    Authors
    GIS Data
    Description

    This is a geographic database of historical districts within the City of San Antonio

  5. n

    San Diego GIS

    • cmr.earthdata.nasa.gov
    Updated Jan 29, 2019
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    (2019). San Diego GIS [Dataset]. https://cmr.earthdata.nasa.gov/search/concepts/C1214612238-SCIOPS
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    Dataset updated
    Jan 29, 2019
    Time period covered
    Jan 1, 1970 - Present
    Area covered
    Description

    The SanGIS data set includes an extensive collection of GIS maps that are available to the public.

     Application Data Included:
    
     1. Public Safety: Crime Mapping & Analysis, Computer Aided Dispatch,
     Emergency Response Planning
    
     2. Planning & Development: Specific Plans, Vegetation Mapping, Zoning,
     Geologic Hazards, Codes Enforcement
    
     3. Facilities Management: Water and Waste Water Utilities, Street
     Lighting, Storm Drains, Pavement Management
    
     4. Subdivision Mapping: Basemap Maintenance, Parcel Mapping, Survey
     Control, Orthophotography
    
     5. Route Management: Water Meter Readers, Trash & Recycling Routes
    
     6. Decision Support & Analysis: Facility Siting, Airport Noise, Slope
     Analysis, Demographics, Economic Development
    
     SanGIS was created in July, 1997, as a Joint Powers Agreement (JPA)
     between the City and County of San Diego. After 13 years of working
     together on data and application development, the City and County
     decided to formalize their partnership in GIS by creating the SanGIS
     JPA. Finding that access to correct and current geographic data was
     considered more important than application development to County and
     City departments, SanGIS focuses on ensuring geographic data is
     maintained and accessible.
    
     SanGIS Mission:
    
     To maintain and promote the use of a regional geographic data
     warehouse for the San Diego area and to facilitate the development of
     shared geographic data and automated systems which use that data.
    
     SanGIS Goals:
    
     1. To ensure geographic data currency and integrity.
    
     2. To provide cost effective access to geographic data to member
     agencies, subscribers and the public.
    
     3. To generate revenue from the sale of geographic data products to
     reduce the cost of map maintenance to member agencies.
    
     Data Collection:
    
     SanGIS data was created or obtained from several sources. Some of our
     data is licensed; some data was created from tabular digital files;
     some data was digitized from paper maps; and other data was entered
     using coordinate geometry tools.
    
     Updating the Data:
    
     Responsibility for the maintenance of the over 200 geographic data
     layers is distributed to City and County departments based on several
     factors such as who has the source documents, who has the greatest
     need for the data, and who is held accountable for this data as part
     of their city-wide or county-wide duties. Most basemap maintenance is
     completed by SanGIS staff. SanGIS is also responsible for coordinating
     with other data maintainers to ensure currency and accuracy for all
     participants.
    
     Data Coverage:
    
     All of the SanGIS geographic data is within San Diego County
     only. Much of our data covers the entire County of San Diego but some
     is only for the City of San Diego.
    
     [Summary provided by SanGIS]
    
  6. a

    Council District

    • hub.arcgis.com
    Updated Oct 24, 2019
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    City of Poway (2019). Council District [Dataset]. https://hub.arcgis.com/maps/poway::council-district
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    Dataset updated
    Oct 24, 2019
    Dataset authored and provided by
    City of Poway
    Area covered
    Description

    This layer comprises polygons of City Council Districts within the City of Poway. Council districts for all incorporated cities within the County of San Diego can be found on the SanGIS/SANDAG Regional Data Warehouse.

  7. O

    SAPD Districts

    • data.sanantonio.gov
    • hub.arcgis.com
    • +1more
    Updated May 19, 2025
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    GIS Data (2025). SAPD Districts [Dataset]. https://data.sanantonio.gov/dataset/sapd-districts
    Explore at:
    arcgis geoservices rest api, txt, gpkg, gdb, html, zip, kml, geojson, csv, xlsxAvailable download formats
    Dataset updated
    May 19, 2025
    Dataset provided by
    City of San Antonio
    Authors
    GIS Data
    Description

    This is a polygon dataset depicting the polygon boundaries of the patrol districts for the San Antonio Police Department. Districts updated 12/30/2017

  8. a

    Data from: Central Business District (CBD)

    • opendata-cosagis.opendata.arcgis.com
    • data.sanantonio.gov
    • +2more
    Updated Jun 20, 2018
    + more versions
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    City of San Antonio (2018). Central Business District (CBD) [Dataset]. https://opendata-cosagis.opendata.arcgis.com/datasets/central-business-district-cbd
    Explore at:
    Dataset updated
    Jun 20, 2018
    Dataset authored and provided by
    City of San Antonio
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This is a graphical polygon dataset which depicts concentrated downtown retail, service office and mixed uses in the existing downtown business district. Major/regional shopping centers are permitted, but urban design standards are required in order to maintain a neighborhood commercial scale, to promote pedestrian activity and to maintain the unique character of the center.

  9. a

    Redistricted Council Districts 2022

    • opendata-cosagis.opendata.arcgis.com
    • data.sanantonio.gov
    Updated Jun 27, 2022
    + more versions
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    City of San Antonio (2022). Redistricted Council Districts 2022 [Dataset]. https://opendata-cosagis.opendata.arcgis.com/maps/redistricted-council-districts-2022
    Explore at:
    Dataset updated
    Jun 27, 2022
    Dataset authored and provided by
    City of San Antonio
    Area covered
    Description

    City Council adopted new district boundaries that are effective for the May 2023 municipal election for City Council. You can learn more about these district changes at the City’s redistricting website: https://www.sabexarcountmein.org/.

  10. a

    River Improvement Overlay Districts (RIO)

    • opendata-cosagis.opendata.arcgis.com
    • data.sanantonio.gov
    Updated Jun 22, 2018
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    City of San Antonio (2018). River Improvement Overlay Districts (RIO) [Dataset]. https://opendata-cosagis.opendata.arcgis.com/datasets/a7669e5bc8ce488299d65f9c619b5f3a
    Explore at:
    Dataset updated
    Jun 22, 2018
    Dataset authored and provided by
    City of San Antonio
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    This is a graphical polygon dataset depicting the polygon boundaries of the Historic River Overlay Districts of San Antonio, Texas.

  11. Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade

    • cityscapes-projects-gisanddata.hub.arcgis.com
    • gis-for-racialequity.hub.arcgis.com
    Updated Jul 23, 2020
    + more versions
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    Urban Observatory by Esri (2020). Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade [Dataset]. https://cityscapes-projects-gisanddata.hub.arcgis.com/items/063cdb28dd3a449b92bc04f904256f62
    Explore at:
    Dataset updated
    Jul 23, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    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:

  12. Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade

    • hub.arcgis.com
    • hub-lincolninstitute.hub.arcgis.com
    • +2more
    Updated Jun 24, 2020
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    Urban Observatory by Esri (2020). Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade [Dataset]. https://hub.arcgis.com/datasets/ef0f926eb1b146d082c38cc35b53c947
    Explore at:
    Dataset updated
    Jun 24, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.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. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: 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. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia:

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(2017). Historical districts [Dataset]. https://data.sandiego.gov/datasets/historic-districts/

Historical districts

Explore at:
csv csv is tabular data. excel, google docs, libreoffice calc or any plain text editor will open files with this format. learn moreAvailable download formats
Dataset updated
Oct 9, 2017
Description

Historical districts contain multiple properties and/or objects that share historical significance. The City’s Historical Resources Board can establish a district if the contributing resources meet one of six criteria. For more information, read the nomination guidelines.

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