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This dataset represents an ongoing effort to approximate the geographic extents of 5 digit zip codes. The dataset was produced using a combination of methods and is based on several sets of source data. Methods include: 1) using local zip code polygon data obtained from counties and cities contained within these counties; 2 ) examining the 2000 Census TIGER line file's zip code attributes; and 3) using UGRC address grid quadrant boundaries to assign the zip code to segments within a boundary corresponding to a place location (city, town, places) from the postal service website and address information system (AIS). In addition, UGRC has used the locations of mailing addresses known to have valid zip codes to fine tune this dataset.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The Address Points dataset shows Utah address points for all twenty-nine Utah counties. An address point represents a geographic location that has been assigned a US Postal Service (USPS) address by the local address authority (i.e., county or municipality) but does not necessarily receive mail. Address points may include several pieces of information about the structure or location that’s being mapped, such as:the full address (i.e., the USPS mailing address, if the address is for a physical location [rather than a PO box]);the landmark name; whether the location is a building;the type of unit;the city and ZIP code; unique code identifiers of the specific geographic location, including the Federal Information Processing Standard Publication (FIPS) county code and the US National Grid (USNG) spatial address;the address source; andthe date that the address point was loaded into the map layer.This dataset is mapping grade; it is a framework layer that receives regular updates. As with all our datasets, the Utah Geospatial Resource Center (UGRC) works to ensure the quality and accuracy of our data to the best of our abilities. Maintaining the dataset is now an ongoing effort between UGRC, counties, and municipalities. Specifically, UGRC works with each county or municipality’s Master Address List (MAL) authority to continually improve the address point data. Counties have been placed on an update schedule depending on the rate of new development and change within them. Populous counties, such as Weber, Davis, Salt Lake, Utah, and Washington, are more complete and are updated monthly, while rural or less populous counties may be updated quarterly or every six months.The information in the Address Points dataset was originally compiled by Utah counties and municipalities and was aggregated by UGRC for the MAL grant initiative in 2012. The purpose of this initiative was to make sure that all state entities were using the same verified, accurate county and municipal address information. Since 2012, more data has been added to the Address Points GIS data and is used for geocoding, 911 response, and analysis and planning purposes. The Address Point data is also used as reference data for the api.mapserv.utah.gov geocoding endpoint, and you can find the address points in many web mapping applications. This dataset is updated monthly and can also be found at: https://gis.utah.gov/data/location/address-data/.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The "Utah Small Health Statistics Areas" feature layer was developed by the Office of Public Health Assessment, Utah Department of Health using the Utah Local Health District and Small Area Designation Algorithm (see https://ibis.health.utah.gov/pdf/resource/Algorithm.pdf). Small Areas are defined based on ZIP Codes, population size, local health district and county boundaries, similarity of ZIP Code area income levels, community political boundaries, and input from local community representatives.
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TwitterOpen Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
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Last update: October 16, 2025 OverviewThis point data was generated and filtered from OpenStreetMap and is intended to represent places of interest in the state of Utah. These may include businesses, restaurants, places of worship, airports, parks, schools, event centers, apartment complexes, hotels, car dealerships…almost anything that you can find in OpenStreetMap (OSM). There are over 23,000 features in the original dataset (March 2022) and users can directly contribute to it through openstreetmap.org. This data is updated approximately once every month and will likely continue to grow over time with user activity. Data SourcesThe original bulk set of OSM data for the state of Utah is downloaded from Geofabrik: https://download.geofabrik.de/north-america/us/utah-latest-free.shp.zipAdditional attributes for the Utah features are gathered via the Overpass API using the following query: https://overpass-turbo.eu/s/1geRData Creation ProcessThe Open Source Places layer is created by a Python script that pulls statewide OSM data from a nightly archive provided by Geofabrik (https://www.geofabrik.de/data/download.html). The archive data contains nearly 20 shapefiles, some that are relevant to this dataset and some that aren't. The Open Source Places layer is built by filtering the polygon and point data in those shapefiles down to a single point feature class with specific categories and attributes that UGRC determines would be of widest interest. The polygon features (buildings, areas, complexes, etc.) are converted to points using an internal centroid. Spatial filtering is done as the data from multiple shapefiles is combined into a single layer to minimize the occurrence of duplicate features. (For example, a restaurant can be represented in OSM as both a point of interest and as a building polygon. The spatial filtering helps reduce the chances that both of these features are present in the final dataset.) Additional de-duplication is performed by using the 'block_id' field as a spatial index, to ensure that no two features of the same name exist within a census block. Then, additional fields are created and assigned from UGRC's SGID data (county, city, zip, nearby address, etc.) via point-in-polygon and near analyses. A numeric check is done on the 'name' field to remove features where the name is less than 3 characters long or more than 50% numeric characters. This eliminates several features derived from the buildings layer where the 'name' is simply an apartment complex building number (ex: 3A) or house number (ex: 1612). Finally, additional attributes (osm_addr, opening_hours, phone, website, cuisine, etc.) are pulled from the Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API) and joined to the filtered data using the 'osm_id' field as the join key. Field Descriptionsaddr_dist - the distance (m) to the nearest UGRC address point within 25 mosm_id - the feature ID in the OSM databasecategory - the feature's data class based on the 4-digit code and tags in the OSM databasename - the name of the feature in the OSM databasecounty - the county the feature is located in (assigned from UGRC's county boundaries)city - the city the feature is located in (assigned from UGRC's municipal boundaries)zip - the zip code of the feature (assigned from UGRC's approximation of zip code boundaries)block_id - the census block the feature is located in (assigned from UGRC's census block boundaries)ugrc_addr - the nearest address (within 25 m) from the UGRC address point databasedisclaimer - a note from UGRC about the ugrc_near_addr fieldlon - the approximate longitude of the feature, calculated in WGS84 EPSG:4326lat - the approximate latitude of the feature, calculated in WGS84 EPSG:4326amenity - the amenity available at the feature (if applicable), often similar to the categorycuisine - the type of food available (if applicable), multiple types are separated by semicolons (;)tourism - the type of tourist location, if applicable (zoo, viewpoint, hotel, attraction, etc.)shop - the type of shop, if applicablewebsite - the feature's website in the OSM database, if availablephone - the feature's phone number(s) in the OSM database, if availableopen_hours - the feature's operating hours in the OSM database, if availableosm_addr - the feature's address in the OSM database, if availableMore information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/society/open-source-places/
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TwitterThe USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
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TwitterWater Right Areas are administrative boundaries based primarily on surface drainage areas.Different water right areas can have different appropriation policies and can be administered bydifferent regional offices. The first two digits of water right numbers identify the area code of the water right area that the right islocated within.Columns in the table show the Regional Office responsible for administering the water rights for that area. Also included is a link to the policies for the same area.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset represents an ongoing effort to approximate the geographic extents of 5 digit zip codes. The dataset was produced using a combination of methods and is based on several sets of source data. Methods include: 1) using local zip code polygon data obtained from counties and cities contained within these counties; 2 ) examining the 2000 Census TIGER line file's zip code attributes; and 3) using UGRC address grid quadrant boundaries to assign the zip code to segments within a boundary corresponding to a place location (city, town, places) from the postal service website and address information system (AIS). In addition, UGRC has used the locations of mailing addresses known to have valid zip codes to fine tune this dataset.