The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida 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 (guis_geomorphology.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 (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.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.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.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 (guis_geomorphology_metadata_faq.pdf). Please read the guis_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 (guis_geomorphology_metadata.txt or guis_geomorphology_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 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 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).

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Each Rescue Plan project has a City of Portland staff project manager. This table maps the manager ID (from the Project Managers dataset) to subproject ID (from the Subprojects dataset).-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60965

The GIS database has been developed by under the Small Hydropower Mapping and Improved Geospatial Electrification Planning in Indonesia Project [Project ID: P145273]. The scope of the project was to facilitate and improve the planning and investment process for small hydro development both grid and isolated systems through: building up a central database on smal hydro at national scale and validating the mapping of small hydro in NTT, Maluku, Maluku Utara and Sulawesi improved electrification planning by integrating small hydro potential for the provinces of NTT, Maluku, Maluku Utara and Sulawesi into the planning process. Please refer to the country project page for additional outputs and reports: http://esmap.org/re_mapping_indonesia The GIS database contains the following datasets: SHP(promising sites) Admin Divisions Topomas_grid Rivers, Geology Forest_areas Roads RainfallGauges RunoffGauges ElectricSystem, each accompanied by a metadata file. Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Indonesia - Small Hydro GIS Atlas, 2017, https://energydata.info/dataset/indonesia-small-hydro-gis-database-2017"

This dataset contains model-based census tract level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 27 measures at the census tract level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

CDFW BIOS GIS Dataset, Contact: VegCAMP Vegetation Classification and Mapping Program, Description: This project was initiated in 1996 when NatureServe, ESRI, Aerial Information Systems, National Park Service (NPS), and USGS met to discuss the use of Yosemite as a pilot for the NPS mapping program. The principal issues for this first project were to implement a GIS-based gradient-directed transect approach for sampling, integrate the vegetation sampling process with the photointerpretation process, and integrate data collected since the 1930s with the data to be collected for this project.

This is a link to the Automated Geographic Reference Center (AGRC) that houses GIS data for the state of Utah. This includes geoscience, cadastre, elevation and terrain, digital aerial photography, roads, aquifer data, etc. Several GIS datasets used in the Utah FORGE project originated from this site.

This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 27 measures at the place level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details.

The main objectives of the Clarence-Moreton *SEEBASETM and GIS Project study were to provide the NSW Department of Primary Industries with an integrated regional interpretation of the basement composition, lithology, structure and depth of the Clarence-Moreton Basin in New South Wales. This included the construction of a depth to basement image (SEEBASE™) for the area.

The effects of the basement geology on the evolution of the Clarence-Moreton Basin (both onshore and offshore) and of its precursors, the Esk Trough and the Ipswich Basin have been investigated. Attention was focused on the formation and reactivation of the basin controlling structures. The evolution of these structures has been evaluated in the light of the different tectonic events that have affected the area.

Maturity and fluid flow migration maps derived from SEEBASETM grids indicate that the central axis of the onshore depocentres and parts of the offshore basin are mature for present-day oil generation. However, these maps probably underestimate the maturity along the eastern margin of the basin, due to significant uplift and erosion that occurred during the Cenomanian. Such areas are likely to be mature for hydrocarbon generation provided adequate source rocks are present at depth.

Available gravity and magnetic data have been reprocessed and enhanced with an extensive set of wavelength and amplitude filters. An ArcMap 9.0 GIS product has been constructed that includes all structural interpretations, as well as the potential field data.

The revised and expanded interpretation of the structure and basin architecture in the area of the Clarence-Moreton Basin provides an improved understanding of basin evolution in the region, which will contribute to the reduction of exploration risks in the area.

[Taken from executive summary of report cited in History]

This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Department of Industry. http://www.industry.nsw.gov.au/

Dataset History

No specific metadata file or history statement provided. See MR707_report.pdf in 'seebase&strucuralGisProject_cla-mor_nsw-dpi\Report' directory.

Dataset Citation

NSW Department of Primary Industries (2014) Clarence-Moreton SEEBASE & Structural GIS Project data.. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/b1690f8b-4025-45d2-96a0-6feb03ff3e52.

The "Restoration Projects" feature layer is a component of the "Pollinator Restoration 2022" map which is itself a component of the "USFWS Pollinator Restoration Projects Mapper" which is a dashboard showing management projects that benefit pollinators across the Western U.S. See below for a description of the "USFWS Pollinator Restoration Projects Mapper."The "USFWS Pollinator Restoration Projects Mapper" is under development by the Region 1 (Pacific Northwest) USFWS Science Applications program. Completion is anticipated by Winter 2023. Contact: Alan Yanahan (alan_yanahan@fws.gov).The purpose of the "USFWS Pollinator Restoration Projects Mapper" is to inform future pollinator conservation efforts by providing a way to identify geographic areas where additional pollinator conservation may be needed.The "USFWS Pollinator Restoration Projects Mapper" maps the locations of where on-the-ground projects that are beneficial to pollinators have taken place. Its primary focus is projects on public lands. The majority of records included in this tool come from internal databases for the USFWS, US Forest Service, and the Bureau of Land Management, which were queried for relevant projects. The tool is not intended as a database for reporting projects to. Rather, the tool synthesizes records from existing databases.The geographic scope of the tool includes the western states of Arizona, California, Idaho, Nevada, Oregon, Utah, and Washington.When possible, the tool includes projects from 2014 to the present. This timespan was chosen because it matches the timespan of the USFWS Monarch Conservation Database For consistency, the tool groups pollinator beneficial projects into the following four activity types:Restoration: Actions taken after a disturbance, such as planting native forbs after a wildfireMaintenance: Actions taken outside the growing season that maintain habitat quality through regular disturbance using manual or chemical means. Examples: mowing, spraying weeds, prescribed fireConservation: Acquiring land or creating easements that are managed for biodiversityEnhancement: Actions that increase forb diversity and nectar resources, such as planting native milkweedThe tool includes a map that aggregates project point locations within 49 square mile sized hexagon grid cells. Users can click on individual grid cells to activate a pop-up menu to cycle through the projects that occurred within that grid cell. Information for each project include, but are not limited to, acreage, type of activity (i.e., restoration, maintenance, conservation, enhancement), data source, and lead organization.The tool also includes a dashboard to view bar graphs and pie charts that display project acreages and project number based on location (i.e., state), project activity type (i.e., restoration, maintenance, conservation, enhancement), data source, and management type. Data can be filtered by data source, activity type, and year. Data filtering will update the map, bar graphs, and pie charts.

The TxDOT DCIS Projects service layer is a dataset of roadway construction and maintenance projects that are either currently under construction, or are in some phase of the planning process. Projects are grouped into 4 categories: projects that are currently underway or begins soon; projects in the construct authority (begins within the next four years); projects in the develop authority (planned to begin in 5 to 10 years); and projects in the initial phases of project development (corridor studies, construction in 10+ years). The dataset excludes non-letting projects.Update Frequency: NightlySource: TxDOT Connect (TxC)Security Level: PublicOwned by TxDOT: TrueRelated LinksData Dictionary PDF [Generated 2025/05/21]

This dataset contains model-based ZIP Code tabulation Areas (ZCTA) level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 27 measures at the ZCTA level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

This online map displays California’s active Underground Gas Storage (UGS) projects and wells associated to UGS projects. Project data and well data are provided by CalGEM’s Well Statewide Tracking and Reporting System (WellSTAR). Wells are displayed by well type and the association to a UGS project.CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).WellSTAR homepageUpdate Frequency: As Needed

CDFW BIOS GIS Dataset, Contact: VegCAMP Vegetation Classification and Mapping Program, Description: This metadata layer shows the footprint of vegetation mapping projects completed in California that have used the Manual California of Vegetation ( MCV 1st edition) or the National Vegetation Classification Standards (MCV 2nd edition) as a basis. It provides basic information about each project. It is intended to provide those interested in vegetation a view of all the projects, with some information about how and when those projects were completed (or are being completed).

The Digital Environmental Geologic-GIS Map for San Antonio Missions National Historical Park and Vicinity, Texas 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 (saan_environmental_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 (saan_environmental_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 (saan_environmental_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.) A GIS readme file (saan_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (saan_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 (saan_environmental_geology_metadata_faq.pdf). Please read the saan_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: Texas Bureau of Economic Geology, University of Texas at Austin. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (saan_environmental_geology_metadata.txt or saan_environmental_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). Purpose:

Infrastructure projects are compiled from the capital improvement plans for Transportation, Airport & Ferry, Surface Water Management, and Sewers. Programs (chipseal, paving, guardrail) are not displayed. Project location, scope, and schedule are subject to change. Please read metadata for additional information(https://matterhorn.piercecountywa.gov/GISmetadata/pdbpubw_improvement_project_points.html). Any data download constitutes acceptance of the Terms of Use (https://matterhorn.piercecountywa.gov/Disclaimer/PierceCountyGISDataTermsofUse.pdf).