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.

The Story Maps, developed by the Joint Research Centre, the Commission's science and knowledge service, inform in an easily accessible way about several initiatives across Europe linked to cultural heritage. These include actions like the European Heritage Days, the EU Prize for Cultural Heritage or the European Heritage Label, funded by Creative Europe, the EU programme that supports the cultural and creative sectors. The website also contains links to the digital collections of Europeana – the EU digital platform for cultural heritage. This platform allows users to explore more than 50 million artworks, artefacts, books, videos and sounds from more than 3500 museums, galleries, libraries and archives across Europe. These maps will be updated and developed, for example taking into account tips from young people exploring Europe's cultural heritage through the new DiscoverEU initiative.

Esri story maps are an exciting and popular feature of the ArcGIS platform that combine maps, photos, text, and other media, in a single interactive application. Any topic or project that includes a map can be a story map. In this seminar, you will learn about Esri application templates that simplify story map creation and require no coding. The presenters will discuss how to choose the best template for a project and the steps to create a compelling story map from a template.

This resource links to the Hurricane Harvey 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Harvey.

November 2023 updates: Esri has deprecated the previous story map template, so a new story map has been generated. Most of the content is the same as before, with these exceptions: - The Vulnerabilities and the Harvey Stories pages have been removed, due to nonfunctioning web links to other Harvey resources out of our control. - Story map links to HydroShare resource pages have been updated to the most current HydroShare resource versions.

References [1] Hurricane Harvey Story Map [https://arcg.is/1rWLzL0]

Story Maps serve as virtual visits to the wide variety of Pennsylvania natural wonders, recreational amenities, and conservation-focused points of interest. By combining intelligent web maps that incorporate text, photos, and interactive map-related functions, story maps will take you to places you may never get to see, or inspire you to visit in the near future. Take a journey around Pennsylvania with the story maps listed below.

This resource links to the Hurricane Irma 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Irma.

References [1] Hurricane Irma Story Map [https://arcg.is/19z9jL]

Referenced external maps Irma crowdsource photos story map (NAPSG) [https://arcg.is/1WOr4b]

In September 2020, the Loudoun County Board of Supervisors directed staff to document telecommunication projects completed, in-progress, and future projects, using the 2014 Wireless GAP Analysis and the Segra Dark Fiber Area Network. Staff mapped the data identified by the Board, as well as other information related to telecommunication projects. This information was then used to identify select unserved or underserved geographic areas of the county.

The companion interactive map allows the user to turn on or off all layers used in the project.

This dataset shows points of interest around Wicklow Mountains National Park, which have been included in an online mapping application - Wicklow Mountains Story Map Tour. CSV file contains points of interest in Wicklow Mountains National Park, along with descriptions and coordinates (Irish Transverse Mercator, Irish Grid and WGS84). Zip folder contains the images used in the Story Map.

This resource links to the Hurricane Harvey 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Harvey.

References [1] Hurricane Harvey Story Map [https://arcg.is/1GWyKi]

This resource links to the Texas Address and Base Layers Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download Texas statewide address points, as well as contextual map layers including roads, rail, bridges, rivers, dams, low water crossings, stream gauges, and others. The addresses were compiled over the period from June 2016 to December 2017 by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide.

For detailed compilation notes, see [2]. Contextual map layers will be found at [3] and [4].

References [1] Texas Address and Base Layers story map [https://arcg.is/19PWu1] [2] Texas-Harvey Basemap - Addresses and Boundaries [https://www.hydroshare.org/resource/d2bab32e7c1d4d55b8cba7221e51b02d/] [3] Texas Basemap - Hydrology Map Data [https://www.hydroshare.org/resource/5efdb83e96da49c5aafe5159791e0ecc/] [4] Texas Basemap - Transportation Map Data [https://www.hydroshare.org/resource/106b38ab28b54f09a2c7a11b91269192/]

All the maps in the 'Black Saturday' - The Beginning of the Blitz StoryMap have been created using the same dataset. This dataset is accessed through a Google Sheet on bombsight.org and includes fields that provide information on the order in which the bombs fell, the time they fell on September 7th, 1940, the closest address to where the bomb fell, the type of bomb, and details about the damage caused by each bomb.In these exercises, we will teach you how to create these maps and then use Story Maps to narrate the events of the first night of the Blitz using this data.An quick overview of the steps we will take today are:

This Story Map is designed to help teachers to create a web application that is similar to the National Geographic Map Maker app.This application is made with the Atlas ArrcGIS Online Instant App TemplateNo audio is included in any of the videos in this StoryMap

This dataset shows points of interest around Ballycroy National Park, which have been included in an online mapping application - Ballycroy Story Map Tour. CSV file contains points of interest in Ballycroy National Park, along with descriptions and coordinates (Irish Transverse Mercator, Irish Grid and WGS84). Zip folder contains the images used in the Story Map.

This story maps steps through Australia's history of significant earthquakes, plotted on an interactive story map.

The map provides information on earthquakes, beginning with geological evidence of events that occurred up to 70 000 years ago, through to the earthquakes that have affected Australian communities in recent years.

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.

Open the Data Resource:https://gis.chesapeakebay.net/wip/meboverview/ This story map provides more information about the 2024 Most Effective Basins mapping project. It complements an interactive map and downloadable dataset. A total of $23 million has been directed to support Most Effective Basins (MEB) implementation in FY2024. MEBs targeted for this funding were identified based on load effectiveness, which is a measure of the ability of management practices implemented in each area (basin) to have a positive effect on dissolved oxygen in the Chesapeake Bay. Unless otherwise approved, implementation activities are expected to occur within these areas.

The dataset presents a list of laboratories set up in the humanities, digital humanities, and media studies within universities across the world in 1983-2018. The data are collected and organized in an interactive map designed in the digital StoryMapJS tool, creating a valuable visible representation of the laboratory concept from a geographical and historical perspective. Based on the interactive map, I analyze the history of the laboratory in the humanities within a global context from the 1980s to 2018. The dataset includes 214 laboratories.

Data collection

I identified laboratories by using different resources such as universities’ websites, articles, and research projects. Besides, I sent a questionnaire to the most relevant networks in October 2018 to identify even more labs created in (digital) humanities and media studies at universities.

Data organization

I collected data about each lab based on its website and other resources. I extracted the following data: year established, year ended (if applicable), lab’s name, university, city, country, affiliation and location (if provided), disciplines and keywords (based on labs’ statements and projects and aiming to situate a lab), selected projects (if provided), purpose (a short quotation of a lab’s statement published on its website), website, and geographical latitude and longitude. I organized all the data in chronological order according to year established in Google Sheets. Next, I used StoryMapJS, a free tool designed by the Northwestern University’s Knight Lab, to map my data.

This resources contains PDF files and Python notebook files that demonstrate how to create geospatial resources in HydroShare and how to use these resources through web services provided by the built-in HydroShare GeoServer instance. Geospatial resources can be consumed directly into ArcMap, ArcGIS, Story Maps, Quantum GIS (QGIS), Leaflet, and many other mapping environments. This provides HydroShare users with the ability to store data and retrieve it via services without needing to set up new data services. All tutorials cover how to add WMS and WFS connections. WCS connections are available for QGIS and are covered in the QGIS tutorial. The tutorials and examples provided here are intended to get the novice user up-to-speed with WMS and GeoServer, though we encourage users to read further on these topic using internet searches and other resources. Also included in this resource is a tutorial designed to that walk users through the process of creating a GeoServer connected resource.

The current list of available tutorials: - Creating a Resource - ArcGIS Pro - ArcMap - ArcGIS Story Maps - QGIS - IpyLeaflet - Folium

This Story Map outlines the evolution of a partnership between Ada County and City of Boise formed to create a new and improved Ridge to Rivers online interactive map. It also explains some of the key differences between the old and new maps and discusses some of the technology involved.

You have uploaded your spreadsheet data as point data to ArcGIS Online and opened it in the map viewer. Now, you need to start creating the maps for your StoryMap. In this exercise, you will create the first map to include in your StoryMap. This map will feature animated symbology to highlight key events from the first night of The Blitz and create a map tour similar to the key events map tour in the 'Black Saturday' - Beginning of The Blitz StoryMap.In this exercise you will:Change the symbology of the bombing data from 7th September 1940 so it is animated Create layers that can be used to show only specific bomb events (this is so you can highlight these in your story map later)Explore the Living Atlas and ArcGIS Online to find a basemap produced closer to the time the first night of The Blitz took place