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

An ArcGIS Blog tutorial that guides you through creating your first dashboard using ArcGIS Dashboards.ArcGIS Dashboards is a configurable web app available in ArcGIS Online that enables users to convey information by presenting interactive charts, gauges, maps, and other visual elements that work together on a single screen.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._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

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.

Various real-time services in a traffic centered scenario

Download this file and save it onto your computer, make any edits and publish it as a Feature Layer in ArcGIS Online.

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

In previous activities we created this interactive web map showing the maximum monthly temperatures in 2019In this activity we will use the web map to create an interactive dashboard like this one. Before we start building try exploring the dashboard so that you get an idea of how it will work..

This layer shows computer ownership and internet access by education. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of the population age 25+ who are high school graduates (includes equivalency) and have some college or associate's degree in households that have no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28006 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the COVID-19 wastewater study. The collection area polygons are published with related wastewater testing data, which are provided by scientists from Arizona State University's Biodesign Institute.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19. People infected with SARS-CoV-2 excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. Tempe wastewater samples are collected downstream of a community and the samples are brought to the ASU lab to analyze for the virus. Analysis is based on the genetic material inside the virus. This dashboard focuses on the genome copies per liter. The absence of a value in a chart indicates that either no samples were collected or that samples are still being analyzed. A value of 5,000 represents samples that are below detection or reporting limits for the test being used. Note of Caution:The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. In the future these trend data could be used alongside other authoritative data, including the number of daily new confirmed cases in Tempe published by the Arizona Department of Health and data documenting the state and local interventions (i.e. social distancing, closures and safe openings). The numeric values of the results should not be viewed as actionable right now; they represent one potentially helpful piece of information among various data sources.We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, what we measure, the COVID-19-related RNA in wastewater, we know is real and we share that info with our community.In the Tempe COVID -19 Wastewater Results Dashboard, please note:These data illustrate a trend of the signal of the weekly average of COVID-19 genome copies per liter of wastewater in Tempe's sewage. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs.While testing can successfully quantify the results, research has not yet determined the relationship between these genome values and the number of people who are positive for COVID-19 in the community.The quantity of RNA detected in sewage is real; the interpretation of that signal and its implication for public health is ongoing research. Currently, there is not a baseline for determining a strong or weak signal.The shedding rate and shedding duration for individuals, both symptomatic and asymptomatic, is still unknown.Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. The quantifiable level of 5,000 copies per liter is the lowest amount measurable with current testing. Results that are below the quantifiable level of 5,000 copies per liter do not suggest the absence of the virus in the collection area. It is possible to have results below the quantifiable level of 5,000 on one day/week and then have a greater signal on a subsequent day/week.For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up the majority of Collection Area 1 samples. After the collection period of April 7-24, 2020, Collection Area 1 samples include only Tempe wastewater.For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. This operations dashboard is used in an associated story map Fighting Coronavirus/COVID-19 with Public Health Data https://storymaps.arcgis.com/stories/e6a45aad50c24e22b7285412d2d6ff2a about the COVID-19 wastewater testing project. This operations dashboard also support's the main Tempe Wastewater BioIntel Program hub site https://wastewater.tempe.gov/.

Opioid wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the opioid wastewater study. The collection area polygons are published with related wastewater testing data, which are provided by scientists from Arizona State University's Biodesign Institute.

The wastewater is tested for the presence of prescription opioid parent drug compounds such as Fentanyl, Oxycodone, Codeine, the illegally made opioid parent drug compound Heroin and opioid metabolite drug compounds including Norfentanyl, 6-Acetylmorphine and Noroxycodone.This dashboard focuses on the average monthly population normalized mass load values (mg/day/1000 capita) by collection area. The absence of a value in a chart indicates that either no samples were collected, or sample values are below detection or reporting limits.

This operations dashboard is used in an associated story map Fighting Opioid Misuse by Monitory Community Health https://arcg.is/PKWuz about the opioid wastewater testing project.

Carbon-Neutral Airline Operations Dashboard Market Outlook

According to our latest research, the global Carbon-Neutral Airline Operations Dashboard market size in 2024 stands at USD 1.23 billion, reflecting the industry’s rapid embrace of sustainability-driven digital transformation. The market is projected to grow at a robust CAGR of 17.8% from 2025 to 2033, reaching an estimated USD 5.17 billion by 2033. This growth is primarily fueled by increasing regulatory pressures, heightened environmental awareness among stakeholders, and the aviation sector’s commitment to achieving net-zero emissions targets.

One of the primary growth factors driving the Carbon-Neutral Airline Operations Dashboard market is the intensifying global focus on decarbonization within the aviation industry. Airlines are under mounting pressure from international regulatory bodies, such as the International Civil Aviation Organization (ICAO) and the European Union, to report and reduce their carbon footprints. The deployment of advanced dashboards enables airlines to monitor, analyze, and optimize their carbon emissions in real time, facilitating compliance with evolving standards and supporting transparent sustainability reporting. This compliance imperative is pushing both legacy carriers and new entrants to invest heavily in digital solutions that provide granular insights into operational carbon outputs and facilitate data-driven decision-making for emission reduction.

Another significant driver is the technological evolution in data analytics and cloud computing, which has made sophisticated carbon-neutral dashboards more accessible and effective. The integration of artificial intelligence (AI), machine learning (ML), and IoT sensors allows these dashboards to aggregate vast amounts of operational data, from fuel consumption to maintenance cycles, and translate it into actionable sustainability metrics. Airlines can leverage these capabilities to identify inefficiencies, optimize flight paths, and implement fuel-saving measures, all of which contribute to lower emissions. The growing partnership between airlines and technology vendors is further catalyzing innovation, leading to continuous improvements in dashboard functionalities and user experience.

Additionally, the surge in consumer demand for sustainable travel options is compelling airlines to visibly demonstrate their commitment to environmental stewardship. Carbon-neutral dashboards play a pivotal role in communicating these efforts to passengers, investors, and regulatory authorities by providing transparent, verifiable data on emissions reductions. This not only enhances brand reputation but also creates new revenue streams through the offering of carbon offset programs and eco-friendly flight options. The competitive advantage gained by early adopters is prompting a ripple effect across the industry, as more airlines recognize the strategic value of investing in comprehensive carbon management solutions.

From a regional perspective, North America and Europe are currently leading the adoption of carbon-neutral airline operations dashboards, driven by stringent environmental regulations, robust technological infrastructure, and proactive sustainability initiatives by major carriers. Asia Pacific is rapidly emerging as a high-growth market, fueled by the expansion of commercial aviation and increasing governmental focus on green technologies. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as airlines in these regions gradually align with global sustainability standards and invest in digital transformation.

Component Analysis

The Component segment of the Carbon-Neutral Airline Operations Dashboard market is broadly categorized into software, hardware, and services, each playing a distinct but interdependent role in enabling airlines to achieve sustainability goals. The software component, which includes the core dashboard platforms, data analytics engines, and integration modules, accounts for the largest revenue share in 2024. These platforms are designed to aggregate and process data from multip

This catalog resource shows demonstrative application configurations to support Hurricane events.Applications are authored using the Esri solutions templates,application templates and the Operations Dashboard, data is from Esri's ArcGIS Living Atlas of the World Within this catalog, you'll find: Public Information MapSituational Awareness ViewerHurricane DashboardStream Gauges (Flood Stage)Precipitation Forecast AppEsri Living Atlas LayersArcGIS Solutions

Although tornadoes can occur throughout the year, prime time for twisters in the U.S. is spring and early summer. Larger symbols show more violent tornadoes. Zoom into the map to see approximate tornado tracks.

At the US Centers for Disease Control and Prevention (CDC), a new effort is under way to assist states in creating or enhancing localized COVID-19 dashboards and maps for the public. This effort with states has an external focus, aiming to help the them deliver data to residents, civic leaders, and public health administrators. Armed with this information, states and localities will be better equipped to monitor the impacts and mitigate risks, and federal resources can go where they are needed most, because everyone will be working from the same data._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This operation view contains services with shipping, maritime boundaries, and weather information for the west coast of the United States. The services in this web map are powered by ArcGIS GeoEvent Extension for Server and contain alerts for ships in certain boundaries, such as nature preserves, or inclement weather.Some of the widgets contained in this operation view are lists that sort the most important data such as those in geofences and those reporting with hazardous cargo. Data contained in this operation view includes:Maritime Boundaries and Port Information:Maritime Boundaries - Various maritime boundaries information provided by the National Oceanic and Atmospheric Administration (NOAAShipping Information:Proximity Alert - Generated buffer information created from an ArcGIS for GeoEvent Extension for Server processor of military vessels.Ship Position- Simulated shipping information obtained from the US Coast Guard (USCG).Weather Information:Meteorological Service of Environment Canada - Web map service with forecast, analysis, and observation layersforunderstanding current meteorological or oceanographic data.NOAA Lightning Strike Density - Time-enabled map service providing maps of experimental lightning strike density data.NOAA Weather Observations - Time-enabled map service providing map depicting the latest surface weather and marine weather observations.NOAA Weather Radar Mosaic - Time-enabled map service providing maps depicting mosaics of base reflectivity images across the United States.NOAA Weather Satellite Information - Time-enabled map service providing maps depicting visible, infrared, and water vapor imagery.

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

Analysis of ‘Coffee shop sample data (11.1.3+)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ylchang/coffee-shop-sample-data-1113 on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

This sample data module contains representative retail data from a fictional coffee chain. The source data is contained in an uploaded file named April Sales.zip. Source: IBM.

We have created sample data for a fictional coffee shop chain with three locations in New York city. The chain has purchased IBM Cognos Analytics to identify factors that contribute to their success, and ultimately to make data-informed decisions.

Amber and Sandeep are the co-founders of the coffee chain. They uploaded their data in a series of spreadsheets and created a data module. From that data, they designed an operations dashboard and a marketing dashboard.

Inventory

Amber and Sandeep have created two dashboards and one data module that is based on nine spreadsheets:

• Coffee operations: This sample dashboard demonstrates operational data from a fictional coffee chain. Location: Team content > Samples > Dashboards.
• Coffee marketing: This sample dashboard demonstrates marketing data from a fictional coffee chain. Location: Team content > Samples > Dashboards.
• Coffee sales and marketing: This sample data module contains representative retail data from a fictional coffee chain. Location: Team content > Samples > Data.
• April Sales.zip: This sample data contains representative retail data from a fictional coffee chain. This ZIP file contains nine related CSV files. Location: Team content > Samples > Data > Source files > Retail.

Content

Data

The sample data module named Coffee sales and marketing can be found in Team content > Samples > Data. There are nine tables:

• Sales Receipts
• Pastry Inventory
• Sales Targets
• Customer
• Dates
• Product
• Sales Outlet
• Staff
• Generation

Acknowledgements

https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/12/beanie-coffee-1113

--- Original source retains full ownership of the source dataset ---

EXAMPLE ONLYThis dashboard application has been created to help emergency personnel and city officials identify the level of damage after a disaster event. Additional information provided in the dashboard inform officials about the level of need based on the damage assessment survey and related map.This application is not for public use and was created solely for educational purposes.

Daily Activity Dashboard is a configuration of ArcGIS Dashboards that can be used by law enforcement command staff to monitor public safety incidents sourced from computer aided dispatch or records management data. It can be used to visualize recent incident reports (for example, crimes, calls for service, arrests, and warrants) and allows command staff to review daily activity in each precinct or district, maintain incident awareness, understand short-term trends, and monitor officer engagements.The Daily Activity Dashboard includes a geoprocessing toolbox that can be used to source incident data from computer aided dispatch or records management systems and create a series of incident layers for your agency. The Record Import toolbox can be downloaded and configured to load incident data in to your ArcGIS organization on an ad-hoc basis, or on a regularly scheduled interval. Once loaded in to your ArcGIS organization, the incident layers provide a foundation for a collection of operational, analytical, and public information maps and apps that can be used by your agency.

2024 Monitoring dashboard used as crews sample for invasive Quagga/Zebra mussel veligers in Idaho. Veligers are the juvenile planktonic stage of Quagga/Zebra mussels. This dashboard can also be used to view data related to surveys for adult Quagga/Zebra mussels. For more information please visit our website.