This Dashboard displays parcel status information by city and town, in a map, table and pie chart.Click on a city or town in the map to view information about that municipality.Click on a row in the table to zoom to that city or town.The pie chart displays the fiscal yer currency of parcel data updates.

This map is used in the application dashboard All of the services displayed on the map are publicly available through either the City of Dallas or the world wide web.

Beta update version of the Kansas Flood Mapping Dashboard featuring new volume-based stage interpolation.

This web map is used in this dashboard: https://seattlecitygis.maps.arcgis.com/apps/dashboards/c6f477cfb51742c8b0ab62d983ef2e73

This dataset tracks the updates made on the dataset "CDC Social Vulnerability Index (SVI) Mapping Dashboard" as a repository for previous versions of the data and metadata.

This map is created to populate related dashboards for public use on the website.

The interactive maps are visual representations of the Social Vulnerability Index (SVI). Data were extracted from the US Census and the American Community Survey.

The purpose of LADOT's data dashboards is to measure performance of meaningful indicators related to the department's values and goals.

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

This dashboard provides access to Covid-19 data provided by the Alabama Department of Health, Johns Hopkins University, and Esri. Maps, graphs, and infographics are interactive and allow users to explore data relevant to public health in their community.

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

This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.

Green Vue Insights include Green Vue Personas (nine distinct propensities) profiles that contrast demos, interests, behaviors and geographies. The dashboard has color coded maps by state, county, zip code and neighborhood. The BI tool can be used to quickly compare markets across the US at both a macro and micro level in terms of overall green propensity and likely interest in green economy products & services.

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

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

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

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

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

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

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

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

(Texas Government Code § 2051.102)

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

This layer was developed for the 2024 Atlanta Region Freight Mobility Plan (2024 ARFMP) and published by the Atlanta Regional Commission.The 2024 ARC Freight Dashboard serves as a tool to help explain how and where freight moves. This dashboard consolidates data collected for the 2024 Atlanta Regional Freight Mobility Plan.The following list describes the layers included in the ARC Freight Dashboard. Asthma Rates (Asthma_Rates): Current asthma among adults aged greater than or equal to 18 years (percentile). Source: US Council on Environmental Quality. Climate and Economic Justice Screening Tool (CEJST).Atlanta Airport (Atlanta_Airport): Atlanta Hartsfield-Jackson International Airport. Environmental Justice Score (EJ_Score): Formerly referred to as Equitable Target Areas (ETA), this equity analysis model considers racial minority, ethnic minority, and low-income status as indicators of the potential inequality in the Atlanta region. Freight Clusters (_20240322_2024_ARC_Freight_Cluster_Areas): Freight clusters designated by the Atlanta Regional Commission for the 2024 ARFMP. GA_net: Expressways and Regional Truck Routes. Georgia: State of Georgia. County Boundaries (County Boundary): Atlanta region county boundaries. MPO Planning Area (MPO_Planning_Area): Atlanta metropolitan planning organization (MPO) area. MPO Planning Area Border (MPO_Planning_Area_Border): Atlanta MPO Planning Area Border. National Highway Freight Network (NHFN): The National Highway Freight Network (NHFN) is a federally designated system of highways critical to the movement of freight in the United States, including the Primary Highway Freight System (PHFS), other Interstate highways not on the PHFS, critical rural and urban freight corridors, and connectors to freight facilities. North_American_Rail_Network_Lines: Rail lines in North America including attribute data. Data from the Bureau of Transportation Statistics’ National Transportation Atlas Database. Rail Yards (Rail_yards): Rail yards in the Atlanta region. Symbolized according to yard type. Rail_Net_Clip: Rail lines in Georgia. Rail_OutsideMPO (Rail_OutsideMPO): Railroads outside the MPO boundaries. Regional Truck Routes (Regional_Truck_Routes): Regional Truck Routes are an ARC-designated network of roadways critical for accommodating and facilitating the efficient movement of freight within the region while minimizing impacts on local communities and balancing mobility and safety needs. US Counties (County_Boundary): US Counties from 2023 US Census Tiger Line files. US Water (US_Water): Waterbodies.

Map shows bathing water locations and their quality for the latest as well as previous bathing seasons whereas all symbols (charts, circles and squares with bather) are coloured according to achieved quality status in the most recent season. Data are presented on two levels: country (less detailed scales) and bathing water (more detailed scales).

Data aggregated by country are visualised as stacked charts showing the distribution of bathing water quality classes by country. Number of bathing waters per each quality class is listed in pop-up window which appears when user clicks on the selected country on the map. In more detailed scales individual bathing waters are coloured according to quality status achieved in the most recent season. Pop-up windows can be opened with a click on individual bathing water monitoring site. The pop-up window shows various information regarding particular bathing water such as bathing water name, coordinates, link (URL) to national bathing water profile and assessment statuses.

The assessment statuses are as follows:

1. monitoring calendar status – describes implementation of monitoring calendar in the last reporting season as defined in Directive 2006/7/EC,Annex IV. The monitoring calendar status is assessed as “implemented” if pre-season sample was taken before the bathing season start, no fewer than four (alternatively, three) samples were taken during the bathing season and interval between sampling dates never exceeded one month. The monitoring calendar is assessed as “not implemented” if at least one of these requirements is not met and the reasons for suspension are not abnormal situation, inaccessible bathing water or implementation of changes.

2. management status – describes management in the last assessment period, whether the bathing water was continuously monitored or not. If bathing water has been monitored in each bathing season in the last assessment period the management status is “Continuously monitored”. Bathing waters which were identified for the first time within the last assessment period are assessed as “Newly identified”. Such status is assigned until the complete four-year dataset is available. If a bathing water was subject to changes described in Directive 2006/7/EC,Article 4.4 within the last assessment period, management status “Quality changes" is applied until complete four-year dataset of samples is available. “Monitoring gap” management status is applied to bathing waters which were not monitored for at least one season in the last assessment period. They can be quality-classified in parallel if enough samples are available in the period before and after the monitoring gap. No quality classification is made if not enough samples are reported for the most recent season.

3. quality status– the bathing waters are quality classified according to the two microbiological parameters (Escherichia coli and Intestinal enterococci) defined in Directive 2006/7/EC Annexes I and II. Quality statusdescribes microbiological quality of water as defined as enough samples are available (defined in Directive 2006/7/EC,Art. 4.3). Bathing water can achieve one of four quality classes: “Excellent”, “Good”, “Sufficient” or “Poor”. If not enough samples for quality classification are available, status “Not classified” is applied. Historical data, listed in the pop-up window include bathing water quality classification for the last ten years. For comparability with classification of the preceding Bathing Water Directive 76/160/EEC, quality classes “Good” and “Sufficient” are merged for the bathing waters (monitored and reported in 2014 and before) for which assessment dataset could not yet facilitate quality assessment under the provisions of Directive 2006/7/EC.

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

The foundation of chemical safety testing relies on chemistry information such as high-quality chemical structures and physical chemical properties. This information is used by scientists to predict the potential health risks of chemicals.The iCSS CompTox Dashboard is part of a suite of dashboards developed by EPA to help evaluate the safety of chemicals. The dashboard provides access to a variety of information on over 700,000 chemicals currently in use. Within the dashboard, users can access chemical structures, experimental and predicted physicochemical and toxicity data, and additional links to relevant websites and applications. It maps curated physicochemical property data associated with chemical substances to their corresponding chemical structures.This data are compiled from sources including the EPA’s computational toxicology research databases, and public domain databases such as the National Center for Biotechnology Information’s PubChem database. This dataset is a mapping file between the dashboard chemicals and the associated InChIStrings and InChIKeys. This file is the version produced by an export of the database on July 1st 2016

Municipal Parcel Status data as used in MassGIS' Parcel Mapping Dashboard. This is a layer saved from an ArcGIS Server-based feature service. MassGIS stores the data in the GISDATA.L3_STATUS feature class.Fields include:MunicipalityCity/Town IDParcel Data Fiscal YearTax Bill CycleCAMA SystemParcels Maintained ByParcels Update ReceivedParcels Passed QARecertification Fiscal YearView and download the data at the MassGIS Data Hub.Also see the MassGIS Property Tax Parcels metadata page.

This document describes a collection of Open Access dashboards and the accompanying metadata schema.