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 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.

We're glad that you're eager to get started with ArcGIS StoryMaps, our latest and most versatile place-based storytelling tool. We think you'll find it easier than ever to make beautiful and informative multimedia narratives.This tutorial gives you the basics you need to get started.

Enjoy the map story maps created by many LOJIC agencies.

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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 is number 1 of 3 data sets that accompany Open Data Maps Data Story on VA's Open Data Site.

Specifically this is a crosswalk data set that identifies VA facilities and their locations via postal address with zip codes and Latitude and Longitude information for facility geo plotting postal addresses. Facility location information as of 2018.

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Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

This is a story map that reviews digital delivery projects and pilot projects identified by UDOT. For more information on this map and Digital Delivery please contact: udotdigitaldelivery@utah.gov

Baton Rouge's unique past has shaped the city that we live in today. The layout of the city's streets, the arrangement of prominent government and religious structures, the clustering of businesses, the distribution of residential neighborhoods, and the placement of parks and schools all speak to the long term processes of urban growth. Society invests tremendous effort in creating its urban centers and citizens develop attachments to those places. It is the investment of human effort that stimulates a sense of place and allows individuals to develop strong feelings about their home city. Sense of place is constantly reinforced by contact with the common, everyday landscapes that surround us. In Baton Rouge, the two principal university campuses, the state government complex, along with various historic neighborhoods and structures all stand as perpetual reminders of the city's past. Many familiar and, at the same time, unique landscape features of Baton Rouge shape our sense of place. Much has been written about the distinctive buildings that come to mind when Baton Rouge is mentioned, but what of the larger districts and neighborhoods? Residents generally are most familiar with their immediate surroundings or those places where they work and play and these surroundings ofter constitute more than a building or two. Children comprehend their immediate neighborhoods and those who move about a city come to know and develop ideas about the city's larger units. Geographers and planners like to think of cities in terms of these larger assemblages

This is number 3 of 3 data sets that accompany Data Set for Maps Data Story on VA's Open Data Site. Specifically this identifies zip codes where VA facilities could perform benefits examinations during phase 3. Data was acquired October 2020.

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.

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 story map covers many of the landmarks and attractions that can be found in Downtown and Central Baton Rouge. This part of the city holds the state Capitol and many other important legislative buildings. It also contains many important historical buildings, museums, and landmarks from Baton Rouge's early years as a settlement. There are also many modern amenities, the product of an extensive re-vitalization campaign over the last few decades.

This dataset is about book subjects. It has 1 row and is filtered where the books is Map stories : the art of discovery. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The world is a crowded place, with more than 7 billion people on the planet as of 2014. About half of this population lives in urban areas, and ongoing migration into city centers has given rise to the megacity—a metropolitan area with 10 million people or more. This story map was produced by Esri's story map team. It is a customization of the Esri Story Map Journal app, and was created in collaboration with the Smithsonian Institution. This story map was also published on Smithsonian.com:https://www.smithsonianmag.com/science-nature/make-cities-explode-size-these-interactive-maps-180952832/

Check out the stories that can be told using maps in New Zealand or by New Zealanders at OR for stories from around the world @