This StoryMap series contains a collection of four Dashboards used to display active project data on the Connecticut road network. Dashboards are used to display Capital Projects, Maintenance Resurfacing Program (MRP) projects, and Local Transportation Capital Improvement Program (LOTCIP) projects, as well as a dashboard to display all data together.Dashboards are listed by tabs at the top of the display. Each dashboard has similar capabilities. Projects are displayed in a zoomable GIS interface and a Project List. As the map is zoomed and the extent changes, the Project List will update to only display projects on the map. Projects selected from the Map or Project List will display a Project Details popup. Additional components of each dashboard include dynamic project counts, a Map Zoom By Town function and a Project Number Search.Capital Project data is sourced from the CTDOT Project Work Areas feature layer. The data is filtered to display active projects only, and categorized as "Pre-Construction" or "Construction." Pre-Construction is defined as projects with a CurrentSchedulePhase value of Planning, Pre-Design, Final Design, or Contract Processing.Maintenance Project data is sourced from the MRP Active feature layer. Central Maintenance personnel coordinate with the four districts to develop an annual statewide resurfacing program based upon a variety of factors (age, condition, etc.) that prioritize paving locations. Active MRP projects are incomplete projects for the current year.LOTCIP Project data is sourced from the CTDOT LOTCIP Projects feature layer. The data updates from LOTCIP database nightly. The geometry of the LOTCIP projects represent the approximate outline of the projects limits and does not represent the actual limits of the projects.

Adding student StoryMaps to your class collection will allow you to keep track of their progress. and students to see what each other are working on.Once students have hit publish for the first time, ask them to send you the link to their StoryMap. Open each StoryMap and, in the top right, click the star to add each student StoryMap to your favorites.Find your class StoryMap collection by returning to storymaps.arcgis.com and selecting "Collections" on the left. Click the three dots on your class collection and select "Edit."Select "Add to collection" and click "My favorites." Your student StoryMaps should appear. Select them all and click done. All student StoryMaps should now appear in the class collection.

These interactive energy equity indicators are designed to help identify opportunities to improve access to clean energy technologies for low-income customers and disadvantaged communities; increase clean energy investment in those communities; and improve community resilience to grid outages and extreme events. A summary report of these indicators will be updated each year to track progress on implementation of the recommendations put forth by the Energy Commission’s December 2016 Low-Income Barriers Study mandated by Senate Bill 350 (de León, Chapter547, Statutes of 2015), and monitor performance of state-administered clean energy programs in low-income and disadvantaged communities across the state.Selected energy equity indicators are highlighted on the following California map. The base map highlights areas with median household income of $37,000 or less (60 percent of statewide median income for 2011-2015) and disadvantaged communities eligible for greenhouse gas reduction fund programs. The map also identifies tribal areas. Click to view data for low-income areas with low energy efficiency investments, low solar capacity per capita, or low clean vehicle rebate incentive investments. Additional data layers include high-density low-income areas and low-income areas that have many older buildings, as well as counties with high levels of asthma-related emergency room visit. This information can help identify opportunities for improving clean energy access, investment, and resilience in low-income and disadvantaged communities in California. Additional indicators are available by clicking on the Story Map or Tracking Progress Report links provided above.

This resource contains the test data for the GeoServer OGC Web Services tutorials for various GIS applications including ArcGIS Pro, ArcMap, ArcGIS Story Maps, and QGIS. The contents of the data include a polygon shapefile, a polyline shapefile, a point shapefile, and a raster dataset; all of which pertain to the state of Utah, USA. The polygon shapefile is of every county in the state of Utah. The polyline is of every trail in the state of Utah. The point shapefile is the current list of GNIS place names in the state of Utah. The raster dataset covers a region in the center of the state of Utah. All datasets are projected to NAD 1983 Zone 12N.

App Development: Burhans, Molly. October 2019.Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

StoryMaps offer a way to interact with our data, allowing a user the ability to move around an area of interest, click on a shape to view its pop-up information, or view an embedded map in full-screen. Simply scroll up and down through the Story, just like a normal webpage. If you want to view a map full-screen, click on the double arrows in the top right of that map. Images, simply click on an image within the StoryMaps to view a larger version. Note; some of our graphs are embedded images too. Click "Get Started" to view the first StoryMap.

The Tempe Police Department prides itself in its continued efforts to reduce harm within the community and is providing this dataset on hate crime incidents that occur in Tempe.The Tempe Police Department documents the type of bias that motivated a hate crime according to those categories established by the FBI. These include crimes motivated by biases based on race and ethnicity, religion, sexual orientation, disability, gender and gender identity.The Bias Type categories provided in the data come from the Bias Motivation Categories as defined in the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) manual, version 2020.1 dated 4/15/2021. The FBI NIBRS manual can be found at https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf with the Bias Motivation Categories found on pages 78-79.Although data is updated monthly, there is a delay by one month to allow for data validation and submission.Information about Tempe Police Department's collection and reporting process for possible hate crimes is included in https://storymaps.arcgis.com/stories/a963e97ca3494bfc8cd66d593eebabaf.Additional InformationSource: Data are from the Law Enforcement Records Management System (RMS)Contact: Angelique BeltranContact E-Mail: angelique_beltran@tempe.govData Source Type: TabularPreparation Method: Data from the Law Enforcement Records Management System (RMS) are entered by the Tempe Police Department into a GIS mapping system, which automatically publishes to open data.Publish Frequency: MonthlyPublish Method: New data entries are automatically published to open data. Data Dictionary

This report of the work undertaken by the Energy Infrastructure and Modeling and Analysis Division ( EIMA ) of the U.S. Department of Energy Office of Electricity Delivery and Energy Reliability (OE) assesses the potential sea level rise and storm surge risks to energy assets in the Metropolitan Statistical Area (MSA) of specific cities in the United States. Here's the DOE article about the report which also links to the story map: https://energy.gov/oe/articles/visualizing-energy-infrastructure-exposure-storm-surge-and-sea-level-rise

For author information and the view count for this story map, please see the entry for it: https://www.arcgis.com/home/item.html?id=58f90c5a5b5f4f94aaff93211c45e4ec

This story map was created by ICF International ( Contact Kevin Wright ): http://www.icfi.com/services/it-solutions/geospatial-solutions-gis

StoryMaps offer a way to interact with our data, allowing a user the ability to move around an area of interest, click on a shape to view its pop-up information, or view an embedded map in full-screen. Simply scroll up and down through the Story, just like a normal webpage. If you want to view a map full-screen, click on the double arrows in the top right of that map. Images, simply click on an image within the StoryMaps to view a larger version. Note; some of our graphs are embedded images too.Click "Get Started" to view the first StoryMap.

To create this app:Make a map of the AfDB projects CSV file in the Training Materials group.Download the CSV file, click Map (at the top of the page), and drag and drop the file onto your mapFrom the layer menu on your Projects layer choose Change Symbols and show the projects using Unique Symbols and the Status of field.Make a second map of the AfDB projects shown using Unique Symbols and the Sector field.HINT: Create a copy of your first map using Save As... and modify the copy.Assemble your story map on the Esri Story Maps websiteGo to storymaps.arcgis.comAt the top of the site, click AppsFind the Story Map Tabbed app and click Build a Tabbed Story MapFollow the instructions in the app builder. Add the maps you made in previous steps and copy the text from this sample app to your app. Explore and experiment with the app configuration settings.=============OPTIONAL - Make a third map of the AFDB projects summarized by country and add it to your story map.Add the World Countries layer to your map (Add > Search for Layers)From the layer menu on your Projects layer choose Perform Analysis > Summarize Data > Aggregate Points and run the tool to summarize the projects in each country.HINT: UNCHECK "Keep areas with no points"Experiment with changing the symbols and settings on your new layer and remove other unnecessary layers.Save AS... a new map.At the top of the site, click My Content.Find your story map application item, open its Details page, and click Configure App.Use the builder to add your third map and a description to the app and save it.

This resource links to the Hurricane Maria Story Map https://arcg.is/00f1ij This story map provides access to a number of Hurricane Maria datasets not hosted on hydroshare.org. Maps with FEMA damage, USGS landslide, forest disturbance, power outages, and health data are browsable here. Additional photos from the event and links to other resources are also presented. Other resources include datasets from NASA, NOAA, FEMA, USGS, as well as other organizations.

StoryMaps offer a way to interact with our data, allowing a user the ability to move around an area of interest, click on a shape to view its pop-up information, or view an embedded map or image in full-screen. Simply scroll up and down through the Story, just like a normal webpage. If you want to view a map full-screen, click on the double arrows in the top right of that map. Images, simply click on an image within the StoryMaps to view a larger version. Note; some of our graphs are embedded images too.Click "Get Started" to view the first StoryMap.

This dataset is published as Open DataA snapshot of the datasets available within the Open Data platform giving the created and modified dates, along with descriptions and associated reference urls.Please note: Not all content available to view within the platform will be listed in the catalogue. These items include maps, apps, and other datasets in support of StoryMaps or applications specifically created allowing a user to interact with our information.

StoryMaps offer a way to interact with our data, allowing a user the ability to move around an area of interest, click on a shape to view its pop-up information, or view an embedded map in full-screen. Simply scroll up and down through the Story, just like a normal webpage. If you want to view a map full-screen, click on the double arrows in the top right of that map. Images, simply click on an image within the StoryMaps to view a larger version. Note; some of our graphs are embedded images too. Click "Get Started" to view the first StoryMap.

The NYC Department of City Planning's (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP's annual Housing Production Snapshot summarizing findings from the 21Q4 data release "https://storymaps.arcgis.com/stories/1c9138dc24064b2e8142ff156345a719">here. Additional Housing and Economic analyses are also available. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

This map service contains the Project Points layer, indicating the general _location and type of projects conducted under the San Francisco Bay Water Quality Improvement Fund (SFBWQIF). SFBWQIF is a competitive grant program that is helping implement TMDLs to improve water quality, protect wetlands, and advance green development planning throughout the Bay Area. This service is incorporated in the San Francisco Bay Water Quality Improvement Fund Story Map, an interactive series of maps and QlikSense graphics highlighting the competitive grant program which supports projects to protect and restore San Francisco Bay. The application can be found on the EPA GeoPlatform at: "https://epa.maps.arcgis.com/apps/MapSeries/index.html?appid=db223d22741140b9b10baf7e91815271 "https://epa.maps.arcgis.com/apps/MapSeries/index.html?appid=db223d22741140b9b10baf7e91815271 The story map is also embedded in the following EPA web page:"https://www2.epa.gov/sfbay-delta/sf-bay-water-quality-improvement-fund-interactive-project-map"> https://www2.epa.gov/sfbay-delta/sf-bay-water-quality-improvement-fund-interactive-project-map

StoryMaps offer a way to interact with our data, allowing a user the ability to move around an area of interest, click on a shape to view its pop-up information, or view an embedded map in full-screen. Simply scroll up and down through the Story, just like a normal webpage. If you want to view a map full-screen, click on the double arrows in the top right of that map. Images, simply click on an image within the StoryMaps to view a larger version. Note; some of our graphs are embedded images too.Click "Get Started" to view the first StoryMap.

This layer contains 58 polygon features representing California counties clipped from the national Tele Atlas county dataset. Dynamap(R)/2000 County Boundary file with a shoreline buffer is a non-generalized polygon layer that represents all U.S. government-defined entities named County. A County is a type of governmental unit that is the primary legal subdivision of every U.S. state. This file contains a 'shoreline buffer' that extends out from the shoreline to the political limits of the census entity. The political limit is usually 12 miles or less from the shoreline into the abutting water feature. The cartographical representation of a shoreline buffer is visibly apparent for census areas that border the Pacific Ocean. These features are incorporated in the San Francisco Bay Water Quality Improvement Fund Story Map, an interactive series of maps and QlikSense graphics highlighting the competitive grant program which supports projects to protect and restore San Francisco Bay. The application can be found on the EPA GeoPlatform at: https://epa.maps.arcgis.com/apps/MapSeries/index.html?appid=db223d22741140b9b10baf7e91815271 The story map is also embedded in the following EPA web page: https://www2.epa.gov/sfbay-delta/sf-bay-water-quality-improvement-fund-interactive-project-map

This dataset depicts the likelihood of an increase in invasive annual grass cover after a wildfire, based on the primary biotic and abiotic factors identified in Anthony et al 2023 (https://doi.org/10.1111/avsc.12759). This map includes four of the most influential drivers of post-fire invasion risk, including elevation, pre-fire annual:perennial ratio, pre-fire shrub cover, and pre-fire annual grass cover to form categories of Low, Medium, Medium-High, High, and Very High post-fire likelihood of an increase in invasive annual grasses. Data sources include: 1) elevation was derived from a 10m DEM using two thresholds at 5000' and 7000', 2) Threat-Based Ecostate maps of rangeland condition (averaged over 2021-2023; see https://storymaps.arcgis.com/stories/5b3558b88d6f494f94a5236ca8bc3345) categorize vegetation based on the annual:perennial ratio and shrub cover using similar thresholds as found in Anthony et al 2023, and 3) cover of annual forbs and grasses (averaged over 2021-2023) is based on the Rangeland Analysis Platform (https://rangelands.app/) with a threshold of 13% cover. Areas with Threat-Based Ecostates mapped as ecostate D are denoted as Annual Dominated, since an increase in annual grasses is unlikely to be ecologically significant in areas that are already heavily impacted. Areas with >20% tree are marked as No Data as this model does not apply to forests or woodland systems. Agriculture, barren, and developed areas were masked out, This dataset replaces and improves upon a previous map depicting likelihood of post-fire transition to annuals, which had been based largely on expert judgment.

This tree canopy assessment is the first of its kind conducted within a 536 square mile study area in the central part of the state of Oklahoma. This assessment was conducted during the spring and summer of 2019. This comprehensive assessment identifies and quantifies the area's land cover, structure, and numerous environmental benefits, which include energy savings, air and water quality improvements, stormwater interception, quality of living, human benefits and other socioeconomic benefits.Part of this assessment included using high resolution satellite imagery to provide a birds-eye view of the entire forest within the study area showing the land cover type and distribution of existing tree canopy. A planting priority analysis was conducted to provide guidance and recommendations for future plantings to help mitigate the impacts of urban heat islands, improve human health benefits, help mitigate stormwater runoff, and provide other valuable environmental and aesthetic benefits.