The map data and summary statistics data are sourced from Johns Hopkins University and Esri’s Living Atlas. The charts are being sourced from a database created by Timmons Group GIS that leverages the temporal data provided by JHU on github.

Why did we do this?

1. The JHU dashboard is focused on Global and one can only drill down to a country-level for charting and summary statistics
2. We wanted to create a US Centric dashboard that one could drill down to the State level and County level for charting and summary statistics

How did we do this?

The raw data from JHU does not support the temporal charting at the State level or County level, so we created a data pipeline to leverage JHU’s source data files and transforms their raw data into our data model

Key features:

1. The only US centric dashboard with State and County level temporal charts of COVID-19 data
2. Ability to select multiple States or Counties and have maps and charts reflect the aggregate of those states/counties
3. Truly responsive design web-app; our dashboard works on desktop/tablet/phone without the need for users to select multiple apps
4. Ability to see the hardest impact States from the State table and exploring their associated charts
5. Ability to see the hardest impacted counties by the County table and exploring their associated charts
6. Ability to see the hardest impacted counties per State by selecting a State and exploring their associated charts

Check out our other ArcGIS Dashboard powered by the new ArcGIS Experience Builder to explore the COVID-19 curves at the country level around the world - Explore the COVID-19 Curve

For additional information, please contact:

• Mike Wiley
• Brian Kingery, GISP (@briankingery87)
• Jenn Kennedy

The polygons are generated from the parcel numbers of the applications from the ARC Review Memo twice a month. Development boundaries represent the parcel's configuration during the initial application. The parcel boundary may change during subdivision recordation. The results field will give a general overview of application. Final Approval is the outcome of City Council, Planning Commission and ARC Review. The Final action date will be the date of the Final Action.

Activity Project Area Sale Area Improvement (SAI) Plan represents an area (polygon) within which one or more Sale Area Improvement (SAI) related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.

Activity Project Area NEPA represents an area (polygon) within which one or more activities related to the National Environmental Policy Act (NEPA) are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityProjectAreas_01/MapServer/0 https://www.fs.fed.us/emc/nepa/index.htm For complete information, please visit https://data.gov.

The Light Rail Tracker App displays the current location of trains for all stops on Light Rail lines. Users can view specific lines or all lines at once. System maps, schedules and next trail arrivals are also available.Provided by the Maryland Transit Administration (MTA)

The U.S. Vessel Traffic application is a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns, and download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts. To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript. Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.

Activity Project Area Timber Sale represents an area (polygon) within which one or more Timber Sale related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityProjectAreas_01/MapServer/2 https://www.fs.fed.us/forestmanagement/products/contracts.shtml For complete information, please visit https://data.gov.

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents activities of hazardous fuel treatment reduction that are polygons. All accomplishments toward the unified hazardous fuels reduction target must meet the following definition: Vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions. These conditions should be measurable or predictable using fire behavior prediction models or fire effects models. Metadata

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

The SilvTSI (Silviculture Timber Stand Improvement) feature class represents activities associated with the following performance measure: Forest Vegetation Improved (Release, Weeding, and Cleaning, Precommercial Thinning, Pruning and Fertilization). The Activities data set portrays the areas where activities are accomplished as a part of the silviculture program of work, funded through the budget allocation process and reported through the Forest Service Activity Tracking System (FACTS) database within the Natural Resource Manager (NRM) suite of applications. The activities are part of the Performance Measures used to rate Agency performance in meeting the Department's Strategic Goals. It is important to note that this layer may not contain all accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. As spatial data reporting is enforced by the application and acceptance of reporting increases for both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years. Metadata and Downloads.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This deep learning model is used to detect and segment trees in high resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.This deep learning model is based on DeepForest and has been trained on data from the National Ecological Observatory Network (NEON). The model also uses Segment Anything Model (SAM) by Meta.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.Input8 bit, 3-band high-resolution (10-25 cm) imagery.OutputFeature class containing separate masks for each tree.Applicable geographiesThe model is expected to work well in the United States.Model architectureThis model is based upon the DeepForest python package which uses the RetinaNet model architecture implemented in torchvision and open-source Segment Anything Model (SAM) by Meta.Accuracy metricsThis model has an precision score of 0.66 and recall of 0.79.Training dataThis model has been trained on NEON Tree Benchmark dataset, provided by the Weecology Lab at the University of Florida. The model also uses Segment Anything Model (SAM) by Meta that is trained on 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.CitationsWeinstein, B.G.; Marconi, S.; Bohlman, S.; Zare, A.; White, E. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens. 2019, 11, 1309Geographic Generalization in Airborne RGB Deep Learning Tree Detection Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan P White bioRxiv 790071; doi: https://doi.org/10.1101/790071

*The data for this dataset is updated daily. The date(s) displayed in the details section on our Open Data Portal is based on the last date the metadata was updated and not the refresh date of the data itself.*This layer displays the FDEP Environmental Resource Permits (ERP) permits issued between 2007 and the present. This contains all ERP permit types from the DEP Permit Application Tracking System (PA) issued by the ERP and Beaches and Coastral Systems programs, minus the DE, EE, and ME types and FD-07. These data are also published in the Florida Water Permitting Portal (FWPP) - http://flwaterpermits.com/. Users will view the following ERP data elements upon searching and finding a specific DEP ERP permit in the FWPP- Application number, Permit number, Project name, Permittee name, Issue date, and a link to documents, if available. Note: The Florida's Water Permitting Portal is maintained by St. John's Water Management District.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

An ArcGIS Experience Builder app used by internal and external stakeholders to monitor active capital projects.

Use the bus tracker app to locate your bus and know when it will arrive at your stop. Explore the app's many features including trip planning, route schedules, real time information, find my stop and more...Provided by the Maryland Transit Administration (MTA)

Plats are submitted in various stages and for different reasons; This layer excludes Final Plats or C3F; More info can be found on www.houstontx.gov/planning or in Chapter 42 City Ordinance.

These layers are used in the The U.S. Vessel Traffic application; a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns of vessel activity such as around ports and fishing grounds, or download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts.To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript.Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

Public information application for tracking ongoing State of Alaska Department of Natural Resources Division of Forestry Fuel Treatment workAll layers are non editable views of the internal tracking application. If you would like to see more information in this app than what is currently offered contact the Forestry GIS section at dof.gis@alaska.gov.