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.

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...

Various real-time services in a traffic centered scenario

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..

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.

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.

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

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.

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

This layer shows median household income by race and by age of householder. 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. 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): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data 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/.

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 web map supports the visualization and interpretation of Chronic Wasting Disease (CWD) surveillance data across the province. It is designed for use within a public-facing ArcGIS Dashboard to aid in monitoring spatial trends and communicating surveillance efforts.The map contains the following data layers:CWD Mandatory Surveillance Area: Displays the boundary of the current mandatory CWD management and surveillance region. All cervid harvests within this area must have a sample submitted to a Manitoba Wildlife Health biological depot.Biological Sample Depots: Locations where hunters can submit harvested cervid samples for CWD testing, represented as point features.Sampling Distribution Heat Map: A density-based heat layer visualizing the spatial intensity of cervid sample submissions, providing insight into survey coverage and effort.Positive Case Locations: Symbolized point features indicating where confirmed positive CWD test results were detected in Manitoba.This map is intended to facilitate understanding of chronic wasting disease, guide future sampling efforts, and inform stakeholders and the public.

Credit Card Financial Dashboard

This project presents a comprehensive Credit Card Financial Report created using Power BI. It aims to provide a detailed analysis of credit card operations on a weekly basis, offering real-time insights into key performance metrics and trends. The dashboard empowers stakeholders to monitor and analyze credit card operations effectively, facilitating informed decision-making processes.

Project Overview:

The "Credit Card Financial Dashboard" leverages a Kaggle dataset containing anonymized credit card transaction data. The dataset includes information such as transaction volume, transaction types, transaction amounts, customer demographics, spending behavior, and credit limits.

Project Contents:

1. Credit Card Transaction Report: This page provides a detailed analysis of credit card transactions, including transaction volume, types of transactions (e.g., purchases, cash advances), transaction amounts, and any anomalies or trends observed. Visualizations such as bar charts, line graphs, and pie charts are utilized to present the data effectively. 2. Credit Card Customer Report: The second page focuses on analyzing customer-related metrics, such as customer demographics (age, gender), spending behavior (average transaction amount, frequency of transactions), credit limits, and any customer-specific insights that can aid in decision-making processes. Visualizations such as demographic distributions, spending patterns, and credit limit distributions are included to provide a comprehensive overview of customer behavior.

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.

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

Road Operations Digital Performance Dashboards Market Outlook

According to our latest research, the global Road Operations Digital Performance Dashboards market size reached USD 2.3 billion in 2024. The market is experiencing robust momentum, with a compound annual growth rate (CAGR) of 13.7% anticipated from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 7.1 billion. This impressive growth is primarily driven by the increasing adoption of digital technologies for real-time road monitoring, data-driven decision-making, and the need for enhanced transparency and efficiency in road operations worldwide.

The surge in demand for Road Operations Digital Performance Dashboards is fundamentally linked to the rapid urbanization and expansion of smart city initiatives globally. As cities grow and transportation networks become more complex, stakeholders are under mounting pressure to ensure seamless traffic flow, minimize congestion, and improve road safety. Digital dashboards provide a centralized platform for aggregating and visualizing critical data from various sources, including traffic sensors, cameras, and IoT devices. This enables road authorities and operators to monitor performance metrics in real time, optimize resource allocation, and swiftly respond to incidents. The integration of artificial intelligence and machine learning further enhances predictive capabilities, allowing for proactive maintenance and incident prevention, which is a significant growth catalyst for the market.

Another key driver fueling the market expansion is the increasing regulatory emphasis on road safety and sustainable transportation. Governments and regulatory bodies across the globe are mandating stricter compliance with safety standards and environmental norms. Digital performance dashboards play a pivotal role in helping organizations track compliance metrics, monitor carbon emissions, and generate comprehensive reports for regulatory submissions. Additionally, the growing focus on reducing operational costs and improving the efficiency of road infrastructure investments is prompting transportation authorities and toll operators to invest in advanced dashboard solutions. These platforms not only streamline operations but also provide actionable insights that drive long-term strategic planning and resource optimization.

The proliferation of connected vehicles and the advent of 5G technology are also contributing significantly to the growth of the Road Operations Digital Performance Dashboards market. With vehicles and infrastructure becoming increasingly interconnected, there is a rising need for platforms that can seamlessly collect, process, and visualize vast volumes of data in real time. Digital dashboards are evolving to accommodate these requirements, offering advanced analytics, customizable interfaces, and integration with third-party systems such as emergency response and navigation applications. As a result, the market is witnessing a steady influx of investments from both public and private sectors, further accelerating technological innovation and adoption.

Regionally, North America continues to dominate the market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. This leadership is attributed to the early adoption of smart transportation technologies, robust infrastructure investments, and strong government support for digital transformation initiatives. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid urbanization, large-scale infrastructure projects, and increasing government focus on intelligent transportation systems. Latin America and the Middle East & Africa are also showing promising growth trajectories, driven by ongoing modernization efforts and the need to address traffic congestion and road safety challenges.

Component Analysis

The Component segment of the Road Operations Digital Performance Dashboards market is categ

ArcGIS for Utilities and Telecommunications provides a standard set of templates that include maps, apps, and tools to support water, electric, gas, and telecommunication industry workflows. In this seminar, you will learn how to configure and deploy these templates to support common asset inspection workflows. The presenters also show how to quickly configure the templates to feature your GIS content.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Standard Or Advanced)ArcGIS OnlineCollector for ArcGIS (iOS) 10.3Operations Dashboard for ArcGIS 10.3

311 is a City of Cleveland program for reporting non-emergency issues, such as potholes or problems with trash pick-up. 311 service requests submitted by residents are entered as tickets which are addressed by the city. This report gives an overview of 311 data since 2/29/2024, when the city undertook a major upgrade of its 311 system. (Historic data is available as an archive here.)The underlying dataset updates once per day, meaning the status of requests on this report may be up to 24 hours out of date.InstructionsSearch by reference number or address to look up specific service requests on the table view.Use filters to specify which group of requests you are interested in.Clicking on chart elements filters the page and highlights relevant groups of values. For example, if you want to see data for a specific day, click on the relevant bar in the bar chart.This application uses the following dataset(s):311 Service Requests (updates daily at 7 AM EST)ContactsFor inquiries about 311 services, please contact the 311 program.