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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 contains all DOMI Street Closure Permit data in the Computronix (CX) system from the date of its adoption (in May 2020) until the present. The data in each record can be used to determine when street closures are occurring, who is requesting these closures, why the closure is being requested, and for mapping the closures themselves. It is updated hourly (as of March 2024).
It is important to distinguish between a permit, a permit's street closure(s), and the roadway segments that are referenced to that closure(s).
• The CX system identifies a street in segments of roadway. (As an example, the CX system could divide Maple Street into multiple segments.)
• A single street closure may span multiple segments of a street.
• The street closure permit refers to all the component line segments.
• A permit may have multiple streets which are closed. Street closure permits often reference many segments of roadway.
The roadway_id field is a unique GIS line segment representing the aforementioned
segments of road. The roadway_id values are assigned internally by the CX system and are unlikely to be known by the permit applicant. A section of roadway may have multiple permits issued over its lifespan. Therefore, a given roadway_id value may appear in multiple permits.
The field closure_id represents a unique ID for each closure, and permit_id uniquely identifies each permit. This is in contrast to the aforementioned roadway_id field which, again, is a unique ID only for the roadway segments.
City teams that use this data requested that each segment of each street closure permit
be represented as a unique row in the dataset. Thus, a street closure permit that refers to three segments of roadway would be represented as three rows in the table. Aside from the roadway_id field, most other data from that permit pertains equally to those three rows.
Thus, the values in most fields of the three records are identical.
Each row has the fields segment_num and total_segments which detail the relationship
of each record, and its corresponding permit, according to street segment. The above example
produced three records for a single permit. In this case, total_segments would equal 3 for each record. Each of those records would have a unique value between 1 and 3.
The geometry field consists of string values of lat/long coordinates, which can be used
to map the street segments.
All string text (most fields) were converted to UPPERCASE data. Most of the data are manually entered and often contain non-uniform formatting. While several solutions for cleaning the data exist, text were transformed to UPPERCASE to provide some degree of regularization. Beyond that, it is recommended that the user carefully think through cleaning any unstructured data, as there are many nuances to consider. Future improvements to this ETL pipeline may approach this problem with a more sophisticated technique.
These data are used by DOMI to track the status of street closures (and associated permits).
An archived dataset containing historical street closure records (from before May of 2020) for the City of Pittsburgh may be found here: https://data.wprdc.org/dataset/right-of-way-permits
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TwitterThis dataset represents the centerline of all streets, roads and highways in Fulton County. Each record represents a street segment between at-grade intersections. Attributes include street name elements, odd and even address ranges, feature type, zip code (left and right) and highway number.The municipalities of Chattahoochee Hills, College Park, East Point, Fairburn, Hapeville, Palmetto, South Fulton, Union City, as well as the Fulton Industrial District (FID) are actively maintained by the Fulton County GIS Division. The data for Johns Creek, Milton, Alpharetta, Sandy Springs, and Roswell are obtained from the respective cities data portal or REST endpoint and incorporated into this countywide data. For questions or issues concerning these cities, please contact the owner of the respective data directly.
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TwitterA shapefile of the extent of irrigated agricultural fields which includes an attribute table of the irrigated acreage for the period between November 2022 and June 2023 was compiled for the full county extents of Brevard, Clay, Duval, Flagler, Indian River, Nassau, Osceola, Putnam, Seminole, St. Johns, and Volusia Counties, Florida. These eleven counties are fully or partially within the St. Johns River Water Management District (SJRWMD). Attributes for each polygon that represents a field include a general or specific crop type, irrigation system, and primary water source for irrigation.
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TwitterLake St. Clair ShorelineMore Metadata
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TwitterKC streets derived from the cadastral database where lines represent the mid-point of the parcel right-of-way. Because it's parcel based, lines will not always align with the ortho photos. A feature in ST_ADDRESS should exist if there is a property with a valid address. A property such as an apartment or condo complex may have many streets within the property but only one street will be shown in ST_ADDRESS, containing the site address. Use TRANS_NETWORK when the lines should more closely match the ortho photos.
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TwitterThe Digital Bedrock Geologic-GIS Map of the Saint-Gaudens National Historical Park and Vicinity, New Hampshire is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (saga_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (saga_bedrock_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (saga_bedrock_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (saga_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (saga_bedrock_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (saga_bedrock_geology_metadata_faq.pdf). Please read the saga_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (saga_bedrock_geology_metadata.txt or saga_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).
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TwitterThis dataset depicts the watershed boundary of the Great Lakes, based on the watershed boundaries of the Great Lakes Basin and the freshwater segment of the St. Lawrence River.View Dataset on the Gateway
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TwitterThis specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.
How Do We Create Polygons?
-All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery, satellite data, and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct highly detailed polygons. This meticulous process ensures maximum accuracy and consistency. -We verify our polygons through multiple quality assurance checks, focusing on accuracy, relevance, and completeness.
What's More?
-Custom Polygon Creation: Our team can build polygons for any location or category based on your requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard GIS formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.
Unlock the Power of POI and Geospatial Data
With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market and location analyses to identify growth opportunities. -Pinpoint the ideal locations for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute location-based marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.
Why Choose LocationsXYZ?
LocationsXYZ is trusted by leading brands to unlock actionable business insights with our accurate and comprehensive spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI datasets. Request your free sample today and explore how we can help accelerate your business growth.
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TwitterThe Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).
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TwitterThis data set consists of a detailed digital map of individual irrigated fields and a summary of the irrigated acreage for the 2017 growing season developed for St Lucie County, Florida. Selected attribute data that include crop type, irrigation system, and primary water source were collected for each irrigated field.
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TwitterDownload In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.
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TwitterHydrology (Creeks Stream, Shoreline) features exported from the CIty's GIS. See summary description (txt) file for information about intended use, projection, currency, attributes, etc.
This map data layer represents the hydrology for the City of Bloomington, Indiana. This includes creeks, streams, lake shorelines, open channels, and detention pond boundary line features.
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TwitterCDFW BIOS GIS Dataset, Contact: Bob Coey, Description: Results of in-stream habitat surveys (Downie et al 1998 method), summarized by stream reach, for DFG surveys conducted between 1994 and 2001 (inclusive) in the Russin River Basin (CalWater 2.2.1 hydrologic area), Central Coast Region. Sampled habitat parameters, including pool type, frequency and depth; substrate class; bank vegetation composition and canopy closure; and in-stream cover, were measured at the unit scale and summarized to stream reach.
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TwitterA listing of web services published from the authoritative East Baton Rouge Parish Geographic Information System (EBRGIS) data repository. Services are offered in Esri REST, and the Open Geospatial Consortium (OGC) Web Mapping Service (WMS) or Web Feature Service (WFS) formats.
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TwitterThese data provide an accurate high-resolution shoreline compiled from imagery of ST LAWRENCE RIVER, WELLESLEY ISLAND TO MORRISTOWN, NY-ONT . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme '...
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TwitterThis data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.
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TwitterCDFW BIOS GIS Dataset, Contact: U.S. Fish & Wildlife Service USFWS, Description: These data identify the areas (in general) where final critical habitat for the La Graciosa thistle (Circium loncholepis) occurs. Critical habitat for the species occurs in two units. The Pismo-Orcutt Unit extends from the coastal strand in the Pismo Beach area in southwestern San Luis Obispo County through the Orcutt area in the Santa Maria Valley in northern Santa Barbara County. The Canada de Las Flores Unit lies in Canada de Las Flores, in the Solomon Hills in northern Santa Barbara County.
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TwitterThis is an ESRI (ArcGIS) shapefile of a 50 km buffer surrounding the Upper San Francisco Estuary hydrodynamic subregions (see GIS Shapefile of Hydrodynamic Subregions of the Upper San Francisco Estuary data package). Contaminants can also be imported to the Estuary from upstream sources. As a rule-of-thumb, water travels approximately 50 km (30 miles) in a day. Therefore I delineated a drainage area that extends 50 km upstream from the boundary of the subregions (or until reaching the watershed divide where streams drain the western slope of the Coast Ranges directly into San Francisco Bay. This buffer area does not overlap with or include the hydrodynamic subregions.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Line shapefile with the Street segments (SSgs) of the Municipality of Kalamaria with vulnerability and risk estimates.from the publication "Urban areas risk estimation based on earthquake evacuation route risk".
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TwitterThe U.S. Army Corps of Engineers Geospatial Open Data provides shared and trusted USACE geospatial data, services and applications for use by our partner agencies and the public.
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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 contains all DOMI Street Closure Permit data in the Computronix (CX) system from the date of its adoption (in May 2020) until the present. The data in each record can be used to determine when street closures are occurring, who is requesting these closures, why the closure is being requested, and for mapping the closures themselves. It is updated hourly (as of March 2024).
It is important to distinguish between a permit, a permit's street closure(s), and the roadway segments that are referenced to that closure(s).
• The CX system identifies a street in segments of roadway. (As an example, the CX system could divide Maple Street into multiple segments.)
• A single street closure may span multiple segments of a street.
• The street closure permit refers to all the component line segments.
• A permit may have multiple streets which are closed. Street closure permits often reference many segments of roadway.
The roadway_id field is a unique GIS line segment representing the aforementioned
segments of road. The roadway_id values are assigned internally by the CX system and are unlikely to be known by the permit applicant. A section of roadway may have multiple permits issued over its lifespan. Therefore, a given roadway_id value may appear in multiple permits.
The field closure_id represents a unique ID for each closure, and permit_id uniquely identifies each permit. This is in contrast to the aforementioned roadway_id field which, again, is a unique ID only for the roadway segments.
City teams that use this data requested that each segment of each street closure permit
be represented as a unique row in the dataset. Thus, a street closure permit that refers to three segments of roadway would be represented as three rows in the table. Aside from the roadway_id field, most other data from that permit pertains equally to those three rows.
Thus, the values in most fields of the three records are identical.
Each row has the fields segment_num and total_segments which detail the relationship
of each record, and its corresponding permit, according to street segment. The above example
produced three records for a single permit. In this case, total_segments would equal 3 for each record. Each of those records would have a unique value between 1 and 3.
The geometry field consists of string values of lat/long coordinates, which can be used
to map the street segments.
All string text (most fields) were converted to UPPERCASE data. Most of the data are manually entered and often contain non-uniform formatting. While several solutions for cleaning the data exist, text were transformed to UPPERCASE to provide some degree of regularization. Beyond that, it is recommended that the user carefully think through cleaning any unstructured data, as there are many nuances to consider. Future improvements to this ETL pipeline may approach this problem with a more sophisticated technique.
These data are used by DOMI to track the status of street closures (and associated permits).
An archived dataset containing historical street closure records (from before May of 2020) for the City of Pittsburgh may be found here: https://data.wprdc.org/dataset/right-of-way-permits