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.

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 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].

November 2023 update: in 2019, TNRIS took over maintenance of the Texas Address Database, which is now updated annually as part of the StratMap program [5]. Also, TNRIS changed its name this year to the Texas Geographic Information Office (TxGIO). The StratMap and DataHub download sites still use the tnris.org domain but that may change .

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/3e251d7d70884abd928d7023e050cbdc/] [3] Texas Basemap - Hydrology Map Data [https://doi.org/10.4211/hs.adb14c9c073e4eee8be82fadb21a0a93/] [4] Texas Basemap - Transportation Map Data [https://www.hydroshare.org/resource/ab3a463be73c4fd988a492b5d1b4c573/] [5] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]

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

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

Stories hub page

In 1860, about half of Fauquier County’s population was made up of free and enslaved African Americans. On the heels of the historical periods of slavery, Reconstruction, Jim Crow, the great migration, civil rights and integration, descendants of these residents now make up less than 10 percent of Fauquier’s population. Only remnants of their many communities are still present, yet their contributions to Fauquier County remain.

This story map attempts to tell the history of the lives of these often overlooked and forgotten Americans.

As community-driven nonprofit organizations, the Afro-American Historical Association of Fauquier County and The Piedmont Environmental Council rely on member support, feedback and engagement. If you feel inspired by what you see in the story map, learn more about Fauquier’s historic African American communities and expand or share your knowledge by:

Visiting www.aahafauquier.org and searching the available databases for more information which include 1867 Voters, African American Marriages, Bible Records, Born Free & Emancipated, and AAHA Archives. Reaching out to info@aahafauquier.org with photos, information, documentation, stories etc…or simply to share your response to the story map. We would love to hear from you! Contacting your elected officials and asking them to support greater local, state and federal recognition of these important, but often overlooked communities and historic resources. Looking forward, AAHA and PEC are already thinking on ways to add, enhance or add new resource layers to this story map project. Potential future additions include: church and community cemeteries, burial sites of the enslaved, and known small family burial sites; sites of impactful historical events; names and location of early African-American owned businesses; sites of fraternal lodges and the story of civic role they played in the African American community, and more!Stay tuned and please contact AAHA with questions and feedback: www.aahafauquier.org/contact

Enjoy the map story maps created by many LOJIC agencies.

Create a basic Story Map: Disease investigations (Learn ArcGIS PDF Lesson). This lesson will show you how to prepare a story map explaining John Snow’s famous investigation of the 1854 cholera outbreak in London._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...

Welcome to ArcGIS StoryMaps! This template will guide you through the basic skills that you need to present information effectively and accessibly in StoryMaps. Our instructions here can pair with coursework across different academic disciplines and are adaptable to various course and grade levels.This is a 6-part exercise that will cover the main functions that the platform can offer. Part 1 gives and overview of setting up and designing a StoryMap, as well as adding text of various sizes and images with credits. Parts 2-4 walk through different options for presenting images and maps with accompanying written content - slideshows, sidecars, and swipes, in that order. Part 5, the map tour, and Part 6, the timeline, look at two features of StoryMaps that are respectively more rooted in attention to space and time.

Understanding natural and human systems is an essential first step toward reducing the severity of climate change and adapting to a warmer future. Maps and geographic information systems are the primary tools by which scientists, policymakers, planners, and activists visualize and understand our rapidly changing world. Spatial information informs decisions about how to build a better future. This Story Map Journal was created by Esri's story maps team. For more information on story maps, visit storymaps.arcgis.com.

To create this app:

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.

A StoryMap offers an effective method for converting your narratives into interactive material, engaging and motivating all types of readers with information and inspiration. The content of this informative StoryMap delves into the process involved in crafting a fundamental StoryMap.

The Biden-Harris administration announced the launch of a new Voluntary Community-Driven Relocation program, led by the Department of the Interior, to assist Tribal communities severely impacted by environmental threats. Through investments from President Biden’s Bipartisan Infrastructure Law and Inflation Reduction Act, the Department is committing $115 million for 11 severely impacted Tribes to advance relocation efforts and adaptation planning. Additional support for relocation will be provided by the Federal Emergency Management Administration (FEMA) and the Denali Commission. Alaska communities located along coastlines and tidally influenced rivers are vulnerable to coastal erosion. These communities face advanced planning decisions, such as implementing shore protection or moving infrastructure. This work aims to provide quantitative erosion exposure data to Alaskans that can be combined with local knowledge and evidence for developing hazard mitigation plans and strategies to address erosion. DGGS Report of Investigation 2021-3, Erosion exposure assessment of infrastructure in Alaska coastal communities, provides estimated erosion exposure for 48 communities from the Bering to the Beaufort seas. The Division of Geological & Geophysical Surveys conducted a shoreline change assessment to forecast 20-, 40-, and 60-year erosion estimates using the Digital Shoreline Analysis System (DSAS; Himmelstoss and others, 2018), and estimated the replacement cost of infrastructure in the forecast area. The geodatabase includes mean erosion forecasts and maximum uncertainties for 38 communities along with infrastructure locations and classification derived from Alaska Division of Community & Regional Affairs digital mapping products (DCRA, 2021) for 44 communities. All files are available from the DGGS website: https://doi.org/10.14509/30672. The sea level rise (SLR) coastal inundation layers were created using existing federal products: the (1) NOAA Coastal Digital Elevation Models (DEMs) and (2) 2022 Interagency Sea Level Rise Technical Report Data Files. The DEMs for the Continental United States (CONUS) are provided in North American Vertical Datum 1988 (NAVD 88) and were converted to Mean Higher High Water (MHHW) using the NOAA VDatum conversion surfaces; the elevation values are in meters (m). The NOAA Scenarios of Future Mean Sea Level are provided in centimeters (cm). The MHHW DEMs for CONUS were merged and converted to cm and Scenarios of Future Mean Sea Level were subtracted from the merged DEM. Values below 0 represent areas that are below sea level and are “remapped” to 1, all values above 0 are remapped to “No Data”, creating a map that shows only areas impacted by SLR. Areas protected by levees in Louisiana and Texas were then masked or removed from the results. This was done for each of the emissions scenarios (Lower Emissions = 2022 Intermediate SLR Scenario Higher Emissions = 2022 Intermediate High SLR Scenario) at each of the mapped time intervals (Early Century - Year 2030, Middle Century - Year 2050, and Late Century - Year 2090). The resulting maps are displayed in the CMRA Assessment Tool. County, tract, and tribal geographies summaries of percentage SLR inundation were also calculated using Zonal Statistics tools. The Sea Level Rise Scenario year 2020 is considered “baseline” and the impacts are calculated by subtracting the baseline value from each of the near-term, mid-term and long-term timeframes. Thumbnail image and following quote courtesy of The Yurok Tribe, “Klamath River estuary on the Yurok Indian Reservation, anticipated area of greatest direct impact from sea level rise.”

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.

The AmeriCorps Research Grantee Story Map is designed to provide information and data on AmeriCorps Research Grantees funded to conduct research about civic engagement, volunteering, and national service.

About WCS Canada:

Our Vision

WCS Canada envisions a world where wildlife thrives in healthy lands and seas, valued by societies that embrace and benefit from the diversity and integrity of life on earth.

Our Mission

WCS Canada saves wildlife and wild places in Canada through science, conservation action, and by inspiring people to value nature.

Our Approach

WCS Canada uses a unique blend of on-the-ground scientific research and policy action to help protect wildlife across Canada. Our scientists are leaders in developing solutions to address conservation challenges, from the impacts of climate change on wildlife and wild areas to the cumulative effects of resource development and other human impacts. We work in some of the wildest corners of Canada to build a scientific case for the conservation of globally important wild areas, like the Ontario Northern Boreal, the Northern Boreal Mountains of BC and Yukon, and the Arctic Ocean, where there is still a big opportunity to protect intact ecosystems. We combine insights gained from our “muddy boots” fieldwork with a big-picture conservation vision to speak up for species such as caribou, wolverine, bats, bison, freshwater fish and marine mammals.

Public places and spaces highlighting the history and contributions of L.A.'s diverse Latinx communities. Access the Controller's story map for all the public places mentioned here: https://storymaps.arcgis.com/stories/af99ea8efdef4790a4c8b151b30dfb27

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