13 datasets found
  1. D

    Bay Area ZIP Codes

    • data.sfgov.org
    application/rdfxml +5
    Updated Oct 18, 2023
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    (2023). Bay Area ZIP Codes [Dataset]. https://data.sfgov.org/widgets/u5j3-svi6
    Explore at:
    csv, application/rdfxml, xml, tsv, application/rssxml, jsonAvailable download formats
    Dataset updated
    Oct 18, 2023
    Area covered
    San Francisco Bay Area
    Description

    As of November 2023, this map has been updated to use a new format. For details, please see here.
    Bay Area ZIP Codes

  2. D

    San Francisco ZIP Codes

    • data.sfgov.org
    application/rdfxml +5
    Updated Oct 18, 2023
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    (2023). San Francisco ZIP Codes [Dataset]. https://data.sfgov.org/w/srq6-hmpi/ikek-yizv?cur=GgATmM0gmYa&from=clEed08Rsrt
    Explore at:
    csv, json, application/rdfxml, application/rssxml, tsv, xmlAvailable download formats
    Dataset updated
    Oct 18, 2023
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    San Francisco
    Description

    As of November 2023, this map has been updated to use a new format. For details, please see here.
    San Francisco ZIP Code Areas. Matched to city streets and lots by Department of Planning. These are generalized areal representations of United States Postal Service (USPS) ZIP Code service areas. Please note that actual USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes.

  3. s

    Zip Code Areas: San Mateo County, California, 2015

    • searchworks.stanford.edu
    zip
    Updated May 1, 2021
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    (2021). Zip Code Areas: San Mateo County, California, 2015 [Dataset]. https://searchworks.stanford.edu/view/gm175wm1954
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    zipAvailable download formats
    Dataset updated
    May 1, 2021
    Area covered
    San Mateo County, California
    Description

    This coverage can be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analyses of geospatial data.

  4. s

    Census Zip Code Tabulation Areas, 2000 - San Francisco Bay Area, California

    • searchworks.stanford.edu
    zip
    Updated Oct 10, 2016
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    (2016). Census Zip Code Tabulation Areas, 2000 - San Francisco Bay Area, California [Dataset]. https://searchworks.stanford.edu/view/df986nv4623
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    zipAvailable download formats
    Dataset updated
    Oct 10, 2016
    Area covered
    San Francisco Bay Area, San Francisco, California
    Description

    This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

  5. d

    Air Pollutant Exposure Zone

    • catalog.data.gov
    • data.sfgov.org
    Updated Mar 29, 2025
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    data.sfgov.org (2025). Air Pollutant Exposure Zone [Dataset]. https://catalog.data.gov/dataset/air-pollutant-exposure-zone
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    Dataset updated
    Mar 29, 2025
    Dataset provided by
    data.sfgov.org
    Description

    SUMMARY The Air Pollutant Exposure Zone (APEZ) map identifies areas in San Francisco where air modeling indicates higher levels of air pollution. This map is required to be updated every 5 years, as established in San Francisco Health Code article 38. The 2025 Air Pollutant Exposure Zone map is an update to the 2020 map. Additional information may be found at Air Quality Review | SF Planning. HOW THE DATASET IS CREATED The 2025 APEZ update modeled areas of the city where: particulate matter (PM2.5) is greater than or equal to 9 µg/m3 or where the risk of cancer from air pollutants is greater than or equal to 100 in a million; in health vulnerable ZIP codes (94102, 94103, 94110, 94124, and 94134), where the risk of cancer from air pollutants is greater than or equal to 90 in a million; locations within 500 feet of freeways; or locations within 1,000 feet of roadways with a daily average of 100,000 vehicles. To learn more, visit San Francisco Citywide Health Risk Assessment: Technical Support Documentation, Air Pollutant Exposure Zone Handout and Air Pollutant Exposure Zone Story Map. UPDATE PROCESS Updated every five years. HOW TO USE THIS DATASET The City uses this dataset as follows. San Francisco Health Code article 38 requires new developments or major renovations within the APEZ with sensitive receptors, like housing or preschools, to include a ventilation system that sufficiently removes fine particulate matter (minimum efficiency reporting volume [MERV] 13 or equivalent filtration). In addition, Environment Code Chapter 25 requires public agencies implementing projects within the APEZ to use the cleanest construction equipment available. The City’s environmental review under the California Environmental Quality Act (CEQA) uses the APEZ in its analysis to mandate the use of clean construction equipment, when applicable. To learn more, visit Air Quality Review | SF Planning.

  6. D

    Dataset Alerts - Open and Monitoring

    • datasf.org
    • data.sfgov.org
    • +1more
    application/rdfxml +5
    Updated Jun 20, 2025
    + more versions
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    (2025). Dataset Alerts - Open and Monitoring [Dataset]. https://datasf.org/opendata/
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    json, application/rssxml, csv, tsv, xml, application/rdfxmlAvailable download formats
    Dataset updated
    Jun 20, 2025
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

  7. w

    Map of Contributions for Candidate Controlled Committees Grouped by Zip Code...

    • data.wu.ac.at
    csv, json, xml
    Updated Aug 3, 2017
    + more versions
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    San Francisco Ethics Commission (2017). Map of Contributions for Candidate Controlled Committees Grouped by Zip Code - Board of Supervisors District 4 - November 5, 2013 [Dataset]. https://data.wu.ac.at/schema/data_sfgov_org/Ynk3Yi1yNzZt
    Explore at:
    xml, csv, jsonAvailable download formats
    Dataset updated
    Aug 3, 2017
    Dataset provided by
    San Francisco Ethics Commission
    Description

    This dataset includes all itemized monetary contributions ($100 or more) e-filed on Fair Political Practices Commission (FPPC) Form 460 Schedule "A" Monetary Contributions from 1998 to the present.The data is current as of the last modified date on this dataset.See the data key for column definitions: https://data.sfgov.org/Ethics/Campaign-Finance-Data-Key/wygs-cc76

  8. SF Bay Area Bike Share

    • kaggle.com
    zip
    Updated Jun 14, 2016
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    Ben Hamner (2016). SF Bay Area Bike Share [Dataset]. https://www.kaggle.com/datasets/benhamner/sf-bay-area-bike-share/versions/1
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    zip(0 bytes)Available download formats
    Dataset updated
    Jun 14, 2016
    Authors
    Ben Hamner
    Area covered
    San Francisco Bay Area, San Francisco
    Description

    Context

    The Bay Area Bike Share enables quick, easy, and affordable bike trips around the San Francisco Bay Area. They make regular open data releases (this dataset is a transformed version of the data from this link), plus maintain a real-time API.

    sf-trips

    Content

    station.csv - Contains data that represents a station where users can pickup or return bikes.

    status.csv - data about the number of bikes and docks available for given station and minute.

    trips.csv - Data about individual bike trips

    weather.csv - Data about the weather on a specific day for certain zip codes

    Acknowledgements

    This dataset is a transformed version of the Bay area bike share data.

    Inspiration

    • How does weather impact bike trips?
    • How do bike trip patterns vary by time of day and the day of the week?
  9. D

    ARCHIVED: COVID-19 Cases and Deaths Summarized by Geography

    • data.sfgov.org
    Updated May 29, 2020
    + more versions
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    Department of Public Health - Population Health Division (2020). ARCHIVED: COVID-19 Cases and Deaths Summarized by Geography [Dataset]. https://data.sfgov.org/COVID-19/ARCHIVED-COVID-19-Cases-and-Deaths-Summarized-by-G/tpyr-dvnc
    Explore at:
    xml, application/rdfxml, csv, tsv, application/geo+json, kml, application/rssxml, kmzAvailable download formats
    Dataset updated
    May 29, 2020
    Dataset authored and provided by
    Department of Public Health - Population Health Division
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

    On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

    Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

    Dataset is cumulative and covers cases going back to 3/2/2020 when testing began.

    Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

    B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

    C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

    D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

    Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000

    Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

    A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

    Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling basis.

    E. CHANGE LOG

    • 9/11/2023 - data on COVID-19 cases and deaths summarized by geography are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
    • 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
    • 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
    • 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
    • 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case and death data by geography.
    • 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
    • 2/23/2022 - the New Cases Map dashboard began pulling from this dataset. To access Cases by Geography Over Time, please refer to this dataset.
    • 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
    • 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
    • 4/16/2021 - dataset updated to refresh with a five-day data lag.

  10. c

    California Public Schools and Districts Map

    • gis.data.ca.gov
    • data.ca.gov
    • +1more
    Updated Oct 24, 2018
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    California Department of Education (2018). California Public Schools and Districts Map [Dataset]. https://gis.data.ca.gov/maps/169b581b560d4150b03ce84502fa5c72
    Explore at:
    Dataset updated
    Oct 24, 2018
    Dataset authored and provided by
    California Department of Education
    License

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

    Area covered
    Description

    This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

  11. Public Housing

    • data.bayareametro.gov
    Updated Dec 9, 2021
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    California Department of Housing and Community Development (2021). Public Housing [Dataset]. https://data.bayareametro.gov/Structures/Public-Housing/3bj7-zyaq
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    application/rdfxml, csv, application/rssxml, xml, tsv, application/geo+json, kml, kmzAvailable download formats
    Dataset updated
    Dec 9, 2021
    Dataset provided by
    California Department of Housing & Community Developmenthttps://hcd.ca.gov/
    Authors
    California Department of Housing and Community Development
    Description

    The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

    According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

    HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

    Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

    ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
    ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
    ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
    ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
    ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
     ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
    ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
    Null - Could not be geocoded (does not appear on the map) 
    

    For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

    HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

    To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

  12. Z

    GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Feb 18, 2025
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    Borrero, Micah (2025). GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_13207715
    Explore at:
    Dataset updated
    Feb 18, 2025
    Dataset provided by
    Bashir, Noman
    MacDonell, Danika
    Borrero, Micah
    MIT Climate & Sustainability Consortium
    License

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

    Description

    Summary

    Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.

    Relevant Links

    Link to the online version of the tool (requires creation of a free user account).

    Link to GitHub repo with source code to produce this dataset and deploy the Geo-TIDE tool locally.

    Funding

    This dataset was produced with support from the MIT Climate & Sustainability Consortium.

    Original Data Sources

    These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:

    Filename(s) Description of Original Data Source(s) Link(s) to Download Original Data License and Attribution for Original Data Source(s)

    faf5_freight_flows/*.geojson

    trucking_energy_demand.geojson

    highway_assignment_links_*.geojson

    infrastructure_pooling_thought_experiment/*.geojson

    Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab.

    Shapefile for FAF5 Regions

    Shapefile for FAF5 Highway Network Links

    FAF5 2022 Origin-Destination Freight Flow database

    FAF5 2022 Highway Assignment Results

    Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset.

    License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

    Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain.

    Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070

    Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link.

    Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644

    grid_emission_intensity/*.geojson

    Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency.

    eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database.

    eGRID database

    Shapefile with eGRID subregion boundaries

    Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.

    Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.

    US_elec.geojson

    US_hy.geojson

    US_lng.geojson

    US_cng.geojson

    US_lpg.geojson

    Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy.

    US_elec.geojson

    US_hy.geojson

    US_lng.geojson

    US_cng.geojson

    US_lpg.geojson

    Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain.

    These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

    daily_grid_emission_profiles/*.geojson

    Hourly emission intensity data obtained from ElectricityMaps.

    Original data can be downloaded as csv files from the ElectricityMaps United States of America database

    Shapefile with region boundaries used by ElectricityMaps

    License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal

    Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.

    Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.

    gen_cap_2022_state_merged.geojson

    trucking_energy_demand.geojson

    Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration.

    U.S. state boundaries obtained from this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog.

    Annual electricity generation by state

    Net summer capacity by state

    Shapefile with U.S. state boundaries

    Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain.

    electricity_rates_by_state_merged.geojson

    Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration.

    Electricity rate by state

    Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain.

    demand_charges_merged.geojson

    demand_charges_by_state.geojson

    Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog.

    Historical demand charge dataset

    The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').

    Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.

    eastcoast.geojson

    midwest.geojson

    la_i710.geojson

    h2la.geojson

    bayarea.geojson

    saltlake.geojson

    northeast.geojson

    Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy.

    The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset.

    The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center.

    Shapefile for Bay Area country boundaries

    Shapefile for counties in Utah

    Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.

    Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain.

    Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/.

    License for Utah boundaries: Creative Commons 4.0 International License.

    incentives_and_regulations/*.geojson

    State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center.

    Data was collected manually from the State Laws and Incentives database.

    Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain.

    These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

    costs_and_emissions/*.geojson

    diesel_price_by_state.geojson

    trucking_energy_demand.geojson

    Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.

    In

  13. w

    Map of Itemized Monetary Contributions to Committees Controlled by...

    • data.wu.ac.at
    csv, json, xml
    Updated May 10, 2012
    + more versions
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    San Francisco Ethics Commission (2012). Map of Itemized Monetary Contributions to Committees Controlled by Candidates for Mayor 2011 Grouped by Zip Code [Dataset]. https://data.wu.ac.at/schema/data_sfgov_org/ZHZyZi1pemV0
    Explore at:
    xml, json, csvAvailable download formats
    Dataset updated
    May 10, 2012
    Dataset provided by
    San Francisco Ethics Commission
    Description

    This dataset includes all itemized monetary contributions ($100 or more) e-filed on Fair Political Practices Commission (FPPC) Form 460 Schedule "A" Monetary Contributions from 1998 to the present.The data is current as of the last modified date on this dataset.See the data key for column definitions: https://data.sfgov.org/Ethics/Campaign-Finance-Data-Key/wygs-cc76

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(2023). Bay Area ZIP Codes [Dataset]. https://data.sfgov.org/widgets/u5j3-svi6

Bay Area ZIP Codes

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22 scholarly articles cite this dataset (View in Google Scholar)
csv, application/rdfxml, xml, tsv, application/rssxml, jsonAvailable download formats
Dataset updated
Oct 18, 2023
Area covered
San Francisco Bay Area
Description

As of November 2023, this map has been updated to use a new format. For details, please see here.
Bay Area ZIP Codes

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