The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

CSV file showing recent NOAA News Releases mapped by geographic location. Users can click on each state and bring up relevant recent NOAA News Releases from that geographic area, thus making it easier to assess what NOAA news is occurring across the entire United States. This is one of four components (i.e., a CSV file, map, feature layer and experience) that contribute to the final NOAA News release product. This product was created using the ESRI Experience builder, specifically the JewelryBox template which displays features in a list combined with a map.

This data release contains the analytical results and the evaluated source data files of a geospatial analysis for identifying areas in Alaska that may have potential for sediment-hosted Pb-Zn (lead-zinc) deposits. The spatial analysis is based on queries of statewide source datasets Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. "SedPbZn_Results_gdb.zip". The analytical results (.gbd - geodatabase format) for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. An mxd file, layer file, and cartographic feature classes are provided for display of the results in ArcMap. 2. "SedPbZn_Results_kmz.zip". The analytical results (.kmz - keyhole markup language format) for sediment-hosted Pb-Zn deposits are shown as polygons containing the points scored for each source data layer query, the accumulative score, and designation of high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. This file may be viewed in an Earth viewer, such as Google Earth. 3. "SedPbZn_Results_shape.zip". The analytical results (.shp - Esri shapefile format) for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The results are also provided as a CSV file. 4. "SedPbZn_SourceData_gdb.zip". Source data (.gbd - geodatabase format); layers include AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are two python scripts 1) to score the ARDF records based on the presence of certain keywords, and 2) to evaluate the ARDF, AGDB3, and lithology layers for the potential for sediment-hosted Pb-Zn deposits within subwatershed polygons. Users may modify the scripts to design their own analyses. 5. "SedPbZn_SourceData_shape.zip". Source data (.shp - Esri shapefile and .csv tabular formats); layers include ARDF and lithology from SIM3340, and HUC subwatersheds. The ARDF keyword tables available in the geodatabase package are presented here as CSV files. 6. "SedPbZn_Appendices.zip". Appendices 2-4 (.csv and .xlsx tabular formats) of the associated USGS Open File Report: OFR 2020-1147; https://doi.org/10.3133/ofr20201147

In 2010, the Deepwater Horizon (DWH) oil spill had unprecedented impacts on the Gulf of America ecosystem, including the twenty cetacean species inhabiting the oceanic waters of this semi-enclosed large marine ecosystem. Due to the impacts from DWH oil, restoration projects focused on oceanic cetaceans are being enacted in the Gulf. These projects require basic information on species’ spatiotemporal density patterns, Gulf-wide movement patterns, Gulf-wide population sizes, long-term abundance trends, and species’ responses to oceanographic and anthropogenic processes, along with information on Gulf-wide ambient noise levels and the contributions from anthropogenic noise sources. To address these needs, NOAA’s Southeast Fisheries Science Center (SEFSC), UCSD’s Scripps Institution of Oceanography (SIO), and partners initiated a comprehensive, long-term, multi-scale passive acoustic monitoring program throughout US and Mexican Gulf waters over the 2020 – 2025 period. This program collects data needed to develop predictive habitat models to assess the processes driving seasonal, interannual, and decadal trends in spatial distribution, density, and abundance of oceanic cetaceans and to assess contributions of ambient noise sources to the Gulf soundscape. This collaborative study annually deploys moored HARP instruments, continuously recording over the 10 Hz to 100 kHz band, over the five-year period at a total of:• 8 five-year long-term sites to identify temporal trends and variability at reference sites over the study period,• 20 one-year short-term sites over a broad area of the Gulf to capture spatial trends and variability in cetacean density and environmental processes,• 3 six-month sites with targeted sampling using tracking arrays to obtain acoustic behavior data for density estimation, and• 2 three-to-five-year sites focused on areas of importance to the DWH Restoration noise reduction project.This feature layer contains data that was focused on anthropogenic noise, converted from a csv file to a feature layer.

Use the Zone Lookup template to allow users to search for an address or use their current location to identify locations that are within a zone or region. With apps created with this template, users can learn more about a location and features of interest in the surrounding area. Grouping results by layer provides an organized view of search results. You can also include the export tool to capture images of the map with the search results. Examples: Facilitate finding hurricane evacuation zones by address in an emergency. Build an app where users can identify schools within a school district, based on a searched address or location. Provide city planning information by zone or area. Data requirements The Zone Lookup template requires a feature layer to use all of its capabilities. Key app capabilities Results - Customize result panel location information with feature attributes from a configured pop-up. Show selected result only - Display the selected result feature in the map while hiding the other features. Attribute filter - Configure map filter options that are available to or added by app users. Sketch a zone - Enable app users to draw a search zone with sketch tools, including buffer capabilities. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Additionally, download the search results as a .csv file. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Included HIFLD Open Layers:

Metadata Portal Metadata Information

Included HIFLD Open Layers:

Active Incident feature layer used to track open shelters entered into the CalEOC reporting system.

Use the Nearby template to guides your app users to places of interest close to an address. This template helps users find focused types of locations (such as schools) within a search distance of an address, their current location, or other place they specify. They can adjust distance values to change the search radius and get directions to locations they select. For users who are searching, you can set a range for the distance slider so users can define their search buffer or pan the map to see results from the map view. Include directions to help users navigate to locations within a defined search radius. Include the export tool to allow users to capture images of the map along with results from the search. Examples: Create a store locator app that allows customers to input a location, find a nearby store, and navigate to it. Create an app for finding health care facilities within a specified distance of a searched address. Provide users with directions and information for election polling locations. Build an app where users can find nearby trails and view an elevation profile of each result. Data requirements The Nearby template requires a feature layer to take full advantage of its capabilities. Key app capabilities Distance slider - Set a minimum and maximum search radius for finding results. Map extent result - Show all the results in the map view. Panel options - Customize result panel location information with feature attributes from a configured pop-up. Results-focused layout - Keep the map out of the app to maintain focus on the search and results. Attribute filter - Configure map filter options that are available to app users. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Alternatively, download the search results as a .csv file. Directions - Provide directions from a searched location to a result location. Elevation profile - Generate an elevation profile graph across an input line feature that can be selected in the scene or from drawing a single or multisegment line using the tool. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Purpose:This feature layer describes water quality sampling data performed at several operating coal mines in the South Fork of Cherry watershed, West Virginia.Source & Data:Data was downloaded from WV Department of Environmental Protection's ApplicationXtender online database and EPA's ECHO online database between January and April, 2023.There are five data sets here: Surface Water Monitoring Sites, which contains basic information about monitoring sites (name, lat/long, etc.) and NPDES Outlet Monitoring Sites, which contains similar information about outfall discharges surrounding the active mines. Biological Assessment Stations (BAS) contain similar information for pre-project biological sampling. NOV Summary contains locations of Notices of Violation received by South Fork Coal Company from WV Department of Environmental Protection. The Quarterly Monitoring Reports table contains the sampling data for the Surface Water Monitoring Sites, which actually goes as far back as 2018 for some mines. Parameters of concern include iron, aluminum and selenium, among others.A relationship class between Surface Water Monitoring Sites and the Quarterly Monitoring Reports allows access to individual sample results.Processing:Notices of Violation were obtained from the WV DEP AppXtender database for Mining and Reclamation Article 3 (SMCRA) Permitting, and Mining and Reclamation NPDES Permitting. Violation data were entered into Excel and loaded into ArcGIS Pro as a CSV text file with Lat/Long coordinates for each Violation. The CSV file was converted to a point feature class.Water quality data were downloaded in PDF format from the WVDEP AppXtender website. Non-searchable PDFs were converted via Optical Character Recognition, so that data could be copied. Sample results were copied and pasted manually to Notepad++, and several columns were re-ordered. Data was grouped by sample station and sorted chronologically. Sample data, contained in the associated table (SW_QM_Reports) were linked back to the monitoring station locations using the Station_ID text field in a geodatabase relationship class.Water monitoring station locations were taken from published Drainage Maps and from water quality reports. A CSV table was created with station Lat/Long locations and loaded into ArcGIS Pro. It was then converted to a point feature class.Stream Crossings and Road Construction Areas were digitized as polygon feature classes from project Drainage and Progress maps that were converted to TIFF image format from PDF and georeferenced.The ArcGIS Pro map - South Fork Cherry River Water Quality, was published as a service definition to ArcGIS Online.Symbology:NOV Summary - dark blue, solid pointLost Flats Surface Water Monitoring Sites: Data Available - medium blue point, black outlineLost Flats Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineLost Flats NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Surface Water Monitoring Sites: Data Available - medium blue point, black outlineBlue Knob Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineBlue Knob NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Biological Assessment Stations: Data Available - medium green point, black outlineBlue Knob Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Surface Water Monitoring Sites: Data Available - medium blue point, black outlineRocky Run Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineRocky Run NPDES Outlet Monitoring Sites - orange point, black outlineRocky Run Biological Assessment Stations: Data Available - medium green point, black outlineRocky Run Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Stream Crossings: turquoise blue polygon with red outlineRocky Run Haul Road Construction Areas: dark red (40% transparent) polygon with black outlineHaul Road No 2 Surface Water Monitoring Sites: Data Available - medium blue point, black outlineHaul Road No 2 Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineHaul Road No 2 NPDES Outlet Monitoring Sites - orange point, black outline

The catalogues of spatially and temporally independent heavy precipitation events are based on RADKLIM-RW Version 2017.002. The hourly precipitation sums (RW) result from radar-based precipitation estimates on a 1 km x 1 km grid over Germany adjusted to station data. Based on this dataset precipitation sums with 11 different durations (1, 2, 3, 4, 6, 9, 12, 18, 24, 48 and 72 hours) were calculated for each hour. For each duration and hour precipitation objects of adjacent grid boxes were identified, that exceeded DWD's warning level 3 for severe weather (W3). From all object that describe the same precipitation event (e.g. an event might exceed the threshold for several durations or in consecutive hours), the one with the highest extremity (Eta, a parameter dependend on return period and spatial extent) was chosen and listed in the catalogue. In a csv-file all events are listed with date and time, duration, return period, geographical location of the precipitation maximum and further parameter. In addition to the .csv tables, each catalogue is available in GIS-Format, permitting the spatial representation of events. All precipitation maxima are represented in a Point Feature Layer and each event’s zone is a part of the Polygon Feature Layer. Each layer contains a complete attribute table, which fully corresponds to the .csv file. Both datasets are availabe as file geodatabase (.gdb) with RADKLIM native polar stereographic projection.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhood How do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This collection of layers, maps and apps help answer the question.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk (in green) or ten minute drive (in blue) of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. Summarizing this data shows that 20% of U.S. population live within a 10 minute walk of a grocery store, and 90% of the population live within a 10 minute drive of a grocery store. Click on the map to see a summary for each state.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access. As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car? How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying against their own experiences. The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer of Census block centroids can be plugged into an app like this one that summarizes the population with/without walkable or drivable access. Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2020 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of September 2024. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were provided by SafeGraph. The source data included NAICS code 445110 and 452311 as an initial screening. The CSV file was imported using the Data Interoperability geoprocessing tools in ArcGIS Pro, where a definition query was applied to the layer to exclude any records that were not grocery stores. The final layer used in the analysis had approximately 63,000 records. In this map, this layer is included as a vector tile layer. MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in a Lines layer, which shows which origins are within the 10 minute cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer is not published but is used to count how many stores each origin has access to over the road network. The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool used a 100 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle.

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

Application containing parcel, address, and zoning information for Sioux Falls, South Dakota.The City of Sioux Falls Parcel Finder provides access to interactive parcel and address information such as parcel id, owner name, legal description, land use, building photos, zoning, preliminary information, and more. In addition, Parcel Finder has the following features: Search by address, intersection, county parcel id, city parcel id, and owner name. Ability to select features. Selected features can be exported to a csv, or other file types. Layers in the layer list can be turned on and off, and reordered. The layer list, by default, contains the address layer that can be turned on to label the house/building number. Add data from the City of Sioux Falls data repository. Add data featuring Demographic and Lifestyle topics. Measuring tools are back! Drawing tools, allowing you to customize your map, suitable for printing. Expanding printing options.

This feature layer describes surface water samples taken along the Parsons to Davis portion of the Corridor H route in Tucker County, WV, as part of the Citizen Science Trout Unlimited WV-VA Water Quality Monitoring Project. Purpose:This data describes water quality data collected as part of a Citizen Science project organized by Trout Unlimited and local volunteers in West Virginia and Virginia. The purpose of this monitoring effort is to evaluate ambient water quality and evaluate impacts to aquatic ecosystems from nearby construction of Corridor H, a highway project traversing mountainous terrain in West Virginia.Source & Date:Data was received from West Virginia Rivers Coalition (an organizing partner of the effort) on 5/28/2024. Links to individual data records, hosted on the project's Citizen Science page, can be accessed by clicking the monitoring site points in the map.Processing:Data was received in Excel tablular format. Fields were re-ordered and the table was converted to Comma Separated Values (CSV) format. A copy was made and only station-related fields were kept. The CSV tables were imported into a file geodatabase. The Stations were converted to a point feature class using Lat/Long coordinates. A relationship class was created between the two tables based on the CitSci Site Name field. The original Stations CSV file was deleted and the file geodatabase published to ArcGIS Online as a feature service. Popups utilizing the related records were set up in Map Viewer.Symbology:Monitoring Stations: medium blue points

Purpose:This feature layer displays water quality data (grab samples), collected as part of a monitoring partnership between West Virginia Department of Environmental Protection (WVDEP) and the federal Office of Surface Mining Reclamation and Enforcement (OSMRE), focused on turbidity, temperature, Specific Conductance, and TSS, collected in streams known to host the endangered Guyandotte and Big Sandy crayfish in West Virginia.Source & Date:The data was received in CSV tabular format from WVDEP on 1/25/24. The data was received as part of a response to a FOIA request filed on 11/30/2023.Processing:Data from multiple sample locations (and dates) were copied onto a single, standardized worksheet and saved in CSV text format. The table was imported to a file geodatabase and a feature layer created from Lat/Long coordinates. The feature layer was exported as a shapefile, uploaded to ArcGIS Online and published as a feature layer.Symbology:Red circle with black center. Thin black outline.

This app shows August 2017 crime, police stations, and schools in Chicago. Data was downloaded from the City of Chicago Data Portal as CSV files and cleaned. The public schools layer was added from the City of Chicago's ArcGIS Online feature layer. Visualization was configured in ArcGIS Online.

Purpose:This feature layer describes the boundaries of Proposed Critical Habitat for the Rusty Patched Bumble Bee in Virginia and West Virginia.Source & Date:Data was downloaded from Regulations.gov, Document FWS-R3-ES-2024-0132-0016: CORRECTED_Rusty Patched Bumble Bee Critical Habitat Plot Points. Posted by the Fish and Wildlife Service on Dec 6, 2024 and accessible here as of 1/16/2025.Processing:The data was downloaded as a list of Latitude and Longitude coordinates in a PDF document. The PPDF was converted to Microsoft Excel format using Nitro Pro PDF editor. Data was cleaned of extra tabs, spaces, etc., given an OBJECTID field and saved as a comma-separated values (CSV) text file. The CSV file was loaded into ArcGIS Pro and converted to a point feature class using Latitude and Longitude as Y & X coordinates, respectively. The point featureclass was converted to polyline using the Points to Line script in Data management Tools - Features tool set. The polyline feature was converted to Polygon using Feature to Polygon (again in Features tool set). Fields for Square Miles (SqMi) and Acres were added and calculated with Calculate Geometry. The polygon feature class was exported to shapefile, zipped and uploaded to ArcGIS Online, where it was published as a feature layer.Symbology:Varies - default is medium blue polygon with dark gray outline.

This feature layer displays the locations of stream crossings associated with the first phase of geotechnical borings along the route of Appalachian Corridor H, between Parsons and Davis, West Virginia.Purpose:The data was developed as part of the design and planning of the Corridor H highway project between Parsons and Davis, West Virginia. Stream crossings are associated with temporary or permanent access roads that may be constructed as part of an infrastructure project.Source and Date:The data was obtained from page 36 of the Storm Water Pollution Prevention Plan (SWPPP); Parsons – Davis; Core Boring Project; Phase 1; Tucker County. State Project No. X347-H-55.68 00; Federal Project No. NHPP-0484(292), prepared by GPI (Greenman Pedersen, Inc.), an engineering consultant. Data was downloaded in PDF format from West Virginia DEP's Electronic Submission System on June 30, 2023. To access the SWPPP document, follow the previous link and paste the permit number (WVR112141) into the Permit Number text box and click "Go".Processing:ABRA staff extracted pages 35-36 from the SWPPP and applied Optical Character Recognition in a commercial PDF editor. Next, data records were copied and pasted into a text editor. Formatting issues were manually corrected and the data QA/QC'd by manually checking records from the copied text. The final text file was checked again in Excel and saved as a comma-delimited text file (CSV). This csv file was imported into a file geodatabase and XY pairs were converted to a point feature class in the North American 1983 datum (Latitude/Longitude). This Lat/Long layer was projected to the WV State Plane North coordinate system, NAD 1983 datum, in US Survey feet. The feature class was published to ArcGIS Online as a Web Feature Layer on July 14, 2023.Symbology:This describes how the Stream Crossings appear in the Parsons to Davis online map, hosted by ABRA's Conservation Hub.Stream Crossings: medium blue circles with black outlineMore information can be found on ABRA’s project description page, hosted by the Conservation Hub. Additional detailed information is available on the USFS project page.