In this tutorial you will create a simple dashboard using ArcGIS Dashboards. The dashboard uses a map of medical facilities in Los Angeles County (sample data only) and includes interactive chart and list elements.A dashboard is composed of several elements that work together. Each element is unique and has its own unique configuration settings. The dashboard will include four elements; a map, serial chart, list, and header. The map will be configured to interact with the chart and list, and the list will be configured to interact with the map.

The Common Operating Picture (COP) Incident Dashboards contain information on incidents of interest as defined by the National Operations Center (NOC) and related geospatial and incident-specific information to provide strategic enterprise situational awareness by incident type.

The National Weather Service collects reports of severe weather events and weather-related damage and continuously updates this information each day. These reports are preliminary, whereas official verified reports are housed at the NOAA National Centers for Environmental Information. This ArcGIS Dashboard displays Local Storm Reports from the 122 forecast offices across the U.S. and aggregated by Esri. Each element in the dashboard is interactive and will filter based on interacting with either the graphical or map elements. Other related layers in the ArcGIS Living Atlas of the World include USA Storm Reports and USA Weather Watches and Warnings.

The statistics and data points on this dashboard will update based on user-selected date range and the current map view extent. Search, pan and zoom to an area of interest, and select the time frame to see how many observations are being collected in your area during the Mosquito Habitat Photo Challenge! The date range selector is in the upper right corner, with options to view All of 2021, Prior to Challenge, and During Challenge. Note that data is limited to observations collected after 2021-01-01.

Each element of the dashboard can be expanded to fill the screen (look for the expand icon in the upper right of the element), or simply drag the boundaries of the element to resize it.

If you would like to create a custom view of the dashboard to bookmark or share, use the MHPC Dashboard Extent Tool to generate a custom URL.

Blockchain data dashboard: Omni Elements

This dashboard defaults to a presentation of the crash points that will cluster the crash types and determine a predominant crash type. In the case two crash types have the same number of crashes for that type the predominant type will not be colored to either of the crash types. Clicking on the clusters will include a basic analysis of the cluster. These clusters are dynamic and will change as the user zooms in an out of the map. The clustering of crashes is functionality availalble in ArcGIS Online and the popups for the clusters is based on items that include elements configured with the Arcade language. Users interested in learning more about point clustering and the configuration of popups should read through some of the examples of the following ESRI Article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/) . The dashboard itself does include a map widget that does allow the user to toggle the visibility of layers and/or click on the crashes within the map. The popups for single crashes can be difficult to see unless the map is expanded (click in upper right of map widget). There is a Review Crashes tab that allows for another display of details of a crash that may be easier for users.This dashboard includes selectors in both the header and sidebar. By default the sidebar is collapsed and would need to be expanded. The crash dataset used in the presentation includes columns with a prefix of the unit. The persons information associated to each unit would be based on the Person that was considered the driver. Crash data can be filtered by clicking on items in chart widgets. All chart widgets have been configured to allow multiple selections and these selections will then filter the crash data accordingly. Allowing for data to be filtered by clicking on widgets is an alternative approach to setting up individual selectors. Selectors can take up a lot of space in the header and sidebar and clicking on the widget items can allow you to explore different scenarios which may ultimately be setup as selectors in the future. The Dashboard has many widgets that are stacked atop each other and underneath these stacked widgets are controls or tabs that allow the user to toggle between different visualizations. The downside to allowing a user to filter based on the output of a widget is the need for the end user to keep track of what has been clicked and the need to go back through and unclick.Many of the Crash Data Elements are based on lookups that have a fairly large range of values to select. This can be difficult sometimes with charts and the fact that a user may be overwhelmed by the number of items be plotted. Some of these values could potentially benefit by grouping similar values. The crash data being used in this dashboard hasn't been post processed to simplify some of the groupings of data and represent the value as it would appear in the Crash System. This dashboard was put together to continue the discussion on what data elements should be included in the GIS Crash Dataset. At the moment there is currently one primary dataset that is used to present crash data in Map Services. There is lots of potential to extend this dataset to include additional elements or it might be beneficial to create different versions of the crash data. Having an examples like this one will hopefully help with the discussion. Workable examples of what works and doesn't work. There are lots of data elements in the Crash System that could allow for an even more detailed safety analysis. Some of the unit items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash.Most Damaged AreaExtent of DamageUnit TypeDirection of Travel (Northbound, Southbound, Eastbound, Westbound)Pre-Crash ActionsSequence of Events 1-4Most Harmful Event Some of the persons items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash and the person would be based on the driver.Condition at Time of CrashDriver Action 1Driver Action 2Driver DistractedAgeSexPerson Type (Driver/Owner(6), Driver(1))In addition to the Units and Persons information included above each crash includes the standard crash data elements which includesDate, Time, Day of Week, Year, Month, HourInjury Level (K,A,B,C,PD)Type of CrashTownname, County, MDOT RegionWeather ConditionsLight ConditionsRoad Surface ConditionsRoad GradeSchool Bus RelatedTraffic Control DeviceType of LocationWork Zone ItemsLocation Type (NODE, ELEMENT) used for LRS# of K, # of A, # of B, # of C, # of PD InjuriesTotal # of UnitsTotal # of PersonsFactored AADT (Only currently applicable for crashes along the roadway (ELEMENT)).Location of First Harmful EventTotal Injury Count for the CrashBoolean Y/N if Pedestrian or Bicycles are InvolvedContributing EnvironmentsContributing RoadRoute Number, Milepoint, Element ID, Node ID

Diagnose indicators linked to potential contributing indicators where data are available in the food systems dashboard.