This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

For more information about this tool see Batch Metadata Modifier Tool Toolbar Help.Modifying multiple files simultaneously that don't have identical structures is possible but not advised. Be especially careful modifying repeatable elements in multiple files that do not have and identical structureTool can be run as an ArcGIS Add-In or as a stand-alone Windows executableExecutable runs on PC only. (Not supported on Mac.)The ArcGIS Add-In requires ArcGIS Desktop version 10.2 or 10.3Metadata formats accepted: FGDC CSDGM, ArcGIS 1.0, ArcGIS ISO, and ISO 19115Contact Bruce Godfrey (bgodfrey@uidaho.edu, Ph. 208-292-1407) if you have questions or wish to collaborate on further developing this tool.Modifying and maintaining metadata for large batches of ArcGIS items can be a daunting task. Out-of-the-box graphical user interface metadata tools within ArcCatalog 10.x are designed primarily to allow users to interact with metadata for one item at a time. There are, however, a limited number of tools for performing metadata operations on multiple items. Therefore, the need exists to develop tools to modify metadata for numerous items more effectively and efficiently. The Batch Metadata Modifier Tools toolbar is a step in that direction. The Toolbar, which is available as an ArcGIS Add-In, currently contains two tools. The first tool, which is additionally available as a standalone Windows executable application, allows users to update metadata on multiple items iteratively. The tool enables users to modify existing elements, find and replace element content, delete metadata elements, and import metadata elements from external templates. The second tool of the Toolbar, a batch thumbnail creator, enables the batch-creation of the graphic that appears in an item’s metadata, illustrating the data an item contains. Both of these tools make updating metadata in ArcCatalog more efficient, since the tools are able to operate on numerous items iteratively through an easy-to-use graphic interface.This tool, developed by INSIDE Idaho at the University of Idaho Library, was created to assist researchers with modifying FGDC CSDGM, ArcGIS 1.0 Format and ISO 19115 metadata for numerous data products generated under EPSCoR award EPS-0814387.This tool is primarily designed to be used by those familiar with metadata, metadata standards, and metadata schemas. The tool is for use by metadata librarians and metadata managers and those having experience modifying standardized metadata. The tool is designed to expedite batch metadata maintenance. Users of this tool must fully understand the files they are modifying. No responsibility is assumed by the Idaho Geospatial Data Clearinghouse or the University of Idaho in the use of this tool. A portion of the development of this tool was made possible by an Idaho EPSCoR Office award.

Basic Viewer is a configurable app template that can be used as a general purpose app for displaying a web map and configuring a variety of tools. This app offers a clean, simple interface that accentuates the web map and includes a toolbar and floating panel.Use CasesDisplays a set of commonly used tools within a floating pane. This is a good choice for balancing the need for a collection of tools while still maximizing the amount of screen real estate dedicated to the map. The app includes the ability to toggle layer visibility, print a map, and show pop-ups in the floating pane.Provides editing capabilities in the context of a general-purpose mapping app. This is a good choice when your audience needs additional tools or information about the map to support their editing activities.Configurable OptionsUse Basic Viewer to present content from a web map and configure it using the following options:Choose a title, sub title, logo, description, and color scheme.Configure a custom splash screen that will display when the app loads.Use custom CSS to customize the look and feel of the app.Enable tools on a toolbar including a basemap gallery, bookmarks, layer list, opacity slider, legend, measure, overview map, etc.Enable an editor tool and an editor toolbar giving users editing capabilities on editable feature layers.Configure a printing tool that can utilize all available print layouts configured in the hosting organization.Configure the ability for feature and location search.Set up custom URL parameters that define how the app and web map appear on load.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled so that the applications can restrict the displayed scenes to a specific date range. This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.For more information on each of the individual layers, see http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ; http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ; http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6

Expands the use of internal data for creating Geographic Information System (GIS) maps. SSA's Database Systems division developed a map users guide for GIS data object publishing and was made available in an internal Sharepoint site for access throughout the agency. The guide acts as the reference for publishers of GIS objects across the life-cycle in our single, central geodatabase implementation.

The Ontario GeoHub Item Report contains details on the items present in Ontario GeoHub and CarrefourGéo Ontario. The report can be filtered using the "date of last data update" to find recently updated items. See below for a list of the fields and descriptions.

Status

On going: data is being continually updated

Maintenance and Update Frequency

Daily: data is updated each day

Contact

Land Information Ontario Support, lio@ontario.ca

Data Dictionary

    dataset_id - Identifier assigned to the item by ArcGIS Hub. For items based on a feature service this is a combination of the ArcGIS Online id and the layer number of the feature layer in the associated service. Format [agol_id]_[layer number].

    item_id - ArcGIS Online identifier associated with the item

    slug - Easy-to-read identifier for the dataset used in URLs

    url_dataset_id - Complete URL to the item using the dataset_id

    url_slug - Complete URL to the item using the slug

    item_tile - Item title

    snippet - Short description of the item

    item_type - ArcGIS Hub item type

    site - The site that the item resides in. Possible values: 'Ontario GeoHub' or 'CarrefourGéo Ontario'

    metadata_lang - Language of the metadata based on mdLang tag in metadata record

    detected_lang - Language of the metadata based on detection of the title and description using Google Translate libraries

    tags - ArcGIS Online tags associated with the item

    grp_name - Name of the Open Data Group through which this item was shared to ArcGIS Hub

    grp_id - Identifier of the Open Data Group through which this item was shared to ArcGIS Hub

    grp_owner - Name of the ArcGIS Online organization that owns the Open Data Group

    dataset_id_eng - The dataset_id of the related English record in Ontario GeoHub. Only applies to records where site = 'CarrefourGéo Ontario'

    dataset_id_fre - The dataset_id of the the related French record in CarrefourGeo Ontario. Only applied to records where site = 'Ontario GeoHub'
  item_id_eng - The item_id of the related English record in Ontario GeoHub. Only applies to records where site = 'CarrefourGéo Ontario'
  item_id_fre - The item_id of the the related French record in CarrefourGeo Ontario. Only applied to records where site = 'Ontario GeoHub'

    legacy_id - Identifier of the source record in the legacy Metadata Management Tool, where applicable

    ccsn - LIO Concrete Class Short Name associated with the item, where applicable

    agol_owner - Username of the ArcGIS Online user that owns the item

    agol_org - Name of the ArcGIS Organization to which the item belongs

    publisher - Item "source" organization as displayed in Hub search results

    publisher_src - Location from which Hub pulled the value of publisher

    data_url - The data url associated with the item

    fgdb_link - Link to the LIO-generated file geodatabase download package associated with the item, where applicable

    shp_link - Link to the LIO-generated shapefile download package associated with the item, where applicable

    created_dt - Item creation date

    modified_dt - Item modified date

    dl_package_dt - Esri download creation date

    dl_lastrety_dt - Date of last attempt to generate Esri download

    data_currency_dt - Date of last data update

    data_currency_dt_src - Source from which data_currency_dt was retrieved

    metadata_present - Indicates whether an ISO-19115 NAP metadata record exists for this item

    metadata_url - Direct URL to the unformatted XML metadata for the item

IMPORTANT: This is the source of the feature layer template in the LearnArcGIS Lesson: Prepare for SAR Incidents and for the MapSAR Solution. If this layer is cloned or copied, the owner of the items needs to update the item details to reflect this. Purpose: This is a feature layer template for use in missing person search operations. It is based on the MapSAR (ArcGIS Desktop) Data Model but simplified for use in web maps and apps. Please see MapSAR GitHub for more information on this project.Maps are at the core of any Search and Rescue (SAR) operation. Geographic information system (GIS) software allows rescue personnel to quickly generate maps that depict specific aspects of the operation and show what is happening on the ground over time. The maps and operations data can be shared over a network to supply an enhanced common operating picture throughout the Incident Command Post (ICP). A team of GIS and SAR professionals from Sierra Madre Search and Rescue Team, Esri, Sequoia and Kings Canyon National Park, Yosemite National Park, Grand Canyon National Park, and the Mountaineer Rescue Group came together to develop the tools and instructions to fit established SAR workflows. The goal is to meet the critical need to provide standards, documents, and training to the international SAR community and establish more widespread and effective integration of GIS into operations.See Comments below for updates to the data model.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

Hosted feature layers used in TCB Adopt-a-Site editing map. These layers replaced the sde layers originally published in 2015 as the ultimate source of truth. Associated Elements:HubHub Site: https://tucson-clean-and-beautiful-cotgis.hub.arcgis.com/Hub Item Details: https://cotgis.maps.arcgis.com/home/item.html?id=e5a76516027f4f718857477f21b23696#overviewOpen to PublicVolunteer Report Map: https://cotgis.maps.arcgis.com/home/item.html?id=8b0cfefce0a04883967353fd64d70a0c#overviewVolunteer Report App: https://cotgis.maps.arcgis.com/home/item.html?id=e01073c557c344328a07544fb82478d2#overviewFeature Layer View: https://cotgis.maps.arcgis.com/home/item.html?id=1103f4e81c2743b795f6e8a7250147ffAdopt-a-Site App: https://cotgis.maps.arcgis.com/home/item.html?id=d40db845579c4b208906ad55358a7e52Adopt-a-Site Map: https://cotgis.maps.arcgis.com/home/item.html?id=7c9855e5a0a542089ac2a66dc4091f4bOpen to Volunteers and TCB StaffSurvey: https://cotgis.maps.arcgis.com/home/item.html?id=b45b263db42d4b6b93f13d9ed60a735e#overviewOpen to TCB StaffEditing Map: https://cotgis.maps.arcgis.com/home/item.html?id=fef204a9647946ae893ce5ac5b780ec1#overviewEditing Layers: https://cotgis.maps.arcgis.com/home/item.html?id=7a20673361904073bc7ee204252bb24e

This Hub is used to help Tucson Clean and Beautiful volunteers and staff manage Adopt-a-Site volunteer reporting. A volunteer work report should be completed through the online form below by the volunteer leader after each project/event. To complete the survey, start by signing in using the link in the upper right corner, or create an account by clicking on the Join the TC&B Volunteer Team button below.HubHub Site: https://tucson-clean-and-beautiful-cotgis.hub.arcgis.com/Hub Item Details: https://cotgis.maps.arcgis.com/home/item.html?id=e5a76516027f4f718857477f21b23696#overviewOpen to PublicVolunteer Report Map: https://cotgis.maps.arcgis.com/home/item.html?id=8b0cfefce0a04883967353fd64d70a0c#overviewVolunteer Report App: https://cotgis.maps.arcgis.com/home/item.html?id=e01073c557c344328a07544fb82478d2#overviewFeature Layer View: https://cotgis.maps.arcgis.com/home/item.html?id=1103f4e81c2743b795f6e8a7250147ffOpen to Volunteers and TCB StaffSurvey: https://cotgis.maps.arcgis.com/home/item.html?id=b45b263db42d4b6b93f13d9ed60a735e#overviewOpen to TCB StaffEditing Map: https://cotgis.maps.arcgis.com/home/item.html?id=fef204a9647946ae893ce5ac5b780ec1#overviewEditing Layers: https://cotgis.maps.arcgis.com/home/item.html?id=7a20673361904073bc7ee204252bb24e

Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This layer shows total trips by mode and their corresponding emissions across different neighborhoods in Seattle. The data is mapped to census tracts.

This layer shows the aggregated emissions resulting from energy consumption in buildings across different neighborhoods and sectors (i.e., residential, commercial and industrial). The data is mapped to census tracts.

This layer has been populated with utility energy consumption data procured directly from Seattle City Light (electricity), aggregated and anonymized by sector, quarter, and census tract. Some tracts have their data combined and averaged with neighboring tracts for privacy purposes. If data is aggregated in a tract, the "grouped flag" field will read "true".

This abstract contains links to public ArcGIS maps that include locations of carbonate springs and some of their characteristics. Information for accessing and navigating through the maps are included in a PowerPoint presentation IN THE FILE UPLOAD SECTION BELOW. Three separate data sets are included in the maps:

1. Geochemistry data from the US Water Quality Portal (WQP), which compiles geochemistry data from the USGS and other federal agencies.
2. Discharge data from WoKaS, a world wide spring discharge data set (Olarinoye et al., 2020).
3. Regional karst data from selected US state agencies.

Several base maps are included in the links. The US carbonate map describes and categorizes carbonates (e.g., depth from surface, overlying geology/ice, climate). The carbonate springs map categorizes springs as being urban, specifically within 1000 ft of a road, or rural. The basis for this categorization was that the heat island effect defines urban as within a 1000 ft of a road. There are other methods for defining urban versus rural to consider. Map links and details of the information they contain are listed below.

Map set 1: The WQP map provides three mapping options separated by the parameters available at each spring site. These maps summarize discrete water quality samples, but not data logger availability. Information at each spring provides links for where users can explore further data.

Option 1: WQP data with urban and rural springs labeled, with highlight of springs with or without NWIS data https://www.arcgis.com/home/item.html?id=2ce914ec01f14c20b58146f5d9702d8a

Options 2: WQP data by major ions and a few other solutes https://www.arcgis.com/home/item.html?id=5a114d2ce24c473ca07ef9625cd834b8

Option 3:WQP data by various carbon species https://www.arcgis.com/home/item.html?id=ae406f1bdcd14f78881905c5e0915b96

Map 2: The worldwide carbonate map in the WoKaS data set (citation below) includes a description of carbonate purity and distribution of urban and rural springs, for which discharge data are available: https://www.arcgis.com/apps/mapviewer/index.html?webmap=5ab43fdb2b784acf8bef85b61d0ebcbe.

Reference: Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B. and Auler, A., 2020. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Scientific Data, 7(1), pp.1-9.

Map 3: Karst and spring data from selected states: This map includes sites that members of the RCN have suggested to our group.

https://uageos.maps.arcgis.com/apps/mapviewer/index.html?webmap=28ed22a14bb749e2b22ece82bf8a8177

This data set is incomplete (as of October 13, 2022 it includes Florida and Missouri). We are looking for more information. You can share data links to additional data by typing them into the hydroshare page created for our group. Then new sites will periodically be added to the map: https://www.hydroshare.org/resource/0cf10e9808fa4c5b9e6a7852323e6b11/

Acknowledgements: These maps were created by Michael Jones, University of Arkansas and Shishir Sarker, University of Kentucky with help from Laura Toran and Francesco Navarro, Temple University.

TIPS FOR NAVIGATING THE MAPS ARE IN THE POWERPOINT DOCUMENT IN THE FILE UPLOAD SECTION BELOW.

Announcement: Project Ended on October 15, 2021After over 18 months of collaboration between hundreds of GISCorps volunteers, Esri's Disaster Response Program, Coders Against COVID, HERE Technologies, dozens of government agencies, and hundreds of testing providers, GISCorps has decided to end our COVID-19 Testing and Vaccination Sites Data Creation Project as of October 15th, 2021. Our data will remain available for use by researchers and analysts, but it should not be considered a reliable source of current testing and vaccination site location information after October 15th. We are grateful for the support we have received by so many throughout the life of this monumental undertaking. Read more about this effort https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-data.Item details page: https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the original COVID-19 Testing Locations in the United States - public dataset. A backup copy also exists: https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same. This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and on findcovidtesting.com. All information was sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated by GISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document: https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups: https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

This tabbed application consist of dashboards with Indicators(numbers) and maps related to CRM data for the last 30 days.

Description Dashboard for use in a tabbed application that displays 30 days worth of CRM data and gives total counts by: New, In Process, and Closed totals, Frequently requested 311 service requests in the last 30 days,

Tab one: The tab display's the Frequently Requested 311 services in the last 30 days. https://dallasgis.maps.arcgis.com/apps/opsdashboard/index.html#/b97656a615f34403b1355ff30dcddf38

The map that feeds Frequently requested 311 services dashboard is : https://dallasgis.maps.arcgis.com/home/item.html?id=459e13227340402cb5a3396137df368e

Tab two: City Wide 30 days pie's/indicators and map- https://dallasgis.maps.arcgis.com/home/item.html?id=75ecfb596ab74cc6b990b3fbdc818b5e

The map that feeds this: https://dallasgis.maps.arcgis.com/home/item.html?id=c6735edd5b2d4e77875e8699cdb00cf7

Third Tab: City wide 30 Day Graphs - https://dallasgis.maps.arcgis.com/home/item.html?id=236cfc648bc74ca79f8dd3cc1ebb49f8

Disclaimer The accuracy is not to be taken / used as data produced by a Registered Professional Land Surveyor for the State of Texas. For this level of detail, supervision and certification of the produced data by a Registered Land Surveyor for the State of Texas would be required. "This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries."

(Texas Government Code § 2051.102)

https://gis.dallascityhall.com/documents/COD_DataDisclaimer.pdf

This survey is completed by TCB volunteers when they clean up and adopted site. Survey results appear in the map as a table related to individual feature classes based on GUID/Global ID fields. Associated Elements:HubHub Site: https://tucson-clean-and-beautiful-cotgis.hub.arcgis.com/Hub Item Details: https://cotgis.maps.arcgis.com/home/item.html?id=e5a76516027f4f718857477f21b23696#overviewOpen to PublicVolunteer Report Map: https://cotgis.maps.arcgis.com/home/item.html?id=8b0cfefce0a04883967353fd64d70a0c#overviewVolunteer Report App: https://cotgis.maps.arcgis.com/home/item.html?id=e01073c557c344328a07544fb82478d2#overviewFeature Layer View: https://cotgis.maps.arcgis.com/home/item.html?id=1103f4e81c2743b795f6e8a7250147ffAdopt-a-Site App: https://cotgis.maps.arcgis.com/home/item.html?id=d40db845579c4b208906ad55358a7e52Adopt-a-Site Map: https://cotgis.maps.arcgis.com/home/item.html?id=7c9855e5a0a542089ac2a66dc4091f4bOpen to Volunteers and TCB StaffSurvey: https://cotgis.maps.arcgis.com/home/item.html?id=b45b263db42d4b6b93f13d9ed60a735e#overviewOpen to TCB StaffEditing Map: https://cotgis.maps.arcgis.com/home/item.html?id=fef204a9647946ae893ce5ac5b780ec1#overviewEditing Layers: https://cotgis.maps.arcgis.com/home/item.html?id=7a20673361904073bc7ee204252bb24e

This dataset contains model-based place (incorporated and census designated places) 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. PLACES 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 2020 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 2020 Census place boundary file in a GIS system to produce maps for 40 measures at the place 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

Line item details from purchase orders in the accounts payable subledger for Current Fiscal YearYou can view this data on the Boroughs Online Checkbook. https://msb.maps.arcgis.com/apps/opsdashboard/index.html#/12734a1be92a4a999c2349ce9dc13a2b

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.