Stamp Out COVID-19An apple a day keeps the doctor away.Linda Angulo LopezDecember 3, 2020https://theconversation.com/coronavirus-where-do-new-viruses-come-from-136105SNAP Participation Rates, was explored and analysed on ArcGIS Pro, the results of which can help decision makers set up further SNAP-D initiatives.In the USA foods are stored in every State and U.S. territory and may be used by state agencies or local disaster relief organizations to provide food to shelters or people who are in need.US Food Stamp Program has been ExtendedThe Supplemental Nutrition Assistance Program, SNAP, is a State Organized Food Stamp Program in the USA and was put in place to help individuals and families during this exceptional time. State agencies may request to operate a Disaster Supplemental Nutrition Assistance Program (D-SNAP) .D-SNAP Interactive DashboardAlmost all States have set up Food Relief Programs, in response to COVID-19.Scroll Down to Learn more about the SNAP Participation Analysis & ResultsSNAP Participation AnalysisInitial results of yearly participation rates to geography show statistically significant trends, to get acquainted with the results, explore the following 3D Time Cube Map:Visualize A Space Time Cube in 3Dhttps://arcg.is/1q8LLPnetCDF ResultsWORKFLOW: a space-time cube was generated as a netCDF structure with the ArcGIS Pro Space-Time Mining Tool : Create a Space Time Cube from Defined Locations, other tools were then used to incorporate the spatial and temporal aspects of the SNAP County Participation Rate Feature to reveal and render statistically significant trends about Nutrition Assistance in the USA.Hot Spot Analysis Explore the results in 2D or 3D.2D Hot Spotshttps://arcg.is/1Pu5WH02D Hot Spot ResultsWORKFLOW: Hot Spot Analysis, with the Hot Spot Analysis Tool shows that there are various trends across the USA for instance the Southeastern States have a mixture of consecutive, intensifying, and oscillating hot spots.3D Hot Spotshttps://arcg.is/1b41T43D Hot Spot ResultsThese trends over time are expanded in the above 3D Map, by inspecting the stacked columns you can see the trends over time which give result to the overall Hot Spot Results.Not all counties have significant trends, symbolized as Never Significant in the Space Time Cubes.Space-Time Pattern Mining AnalysisThe North-central areas of the USA, have mostly diminishing cold spots.2D Space-Time Mininghttps://arcg.is/1PKPj02D Space Time Mining ResultsWORKFLOW: Analysis, with the Emerging Hot Spot Analysis Tool shows that there are various trends across the USA for instance the South-Eastern States have a mixture of consecutive, intensifying, and oscillating hot spots.Results ShowThe USA has counties with persistent malnourished populations, they depend on Food Aide.3D Space-Time Mininghttps://arcg.is/01fTWf3D Space Time Mining ResultsIn addition to obvious planning for consistent Hot-Hot Spot Areas, areas oscillating Hot-Cold and/or Cold-Hot Spots can be identified for further analysis to mitigate the upward trend in food insecurity in the USA, since 2009 which has become even worse since the outbreak of the COVID-19 pandemic.After Notes:(i) The Johns Hopkins University has an Interactive Dashboard of the Evolution of the COVID-19 Pandemic.Coronavirus COVID-19 (2019-nCoV)(ii) Since March 2020 in a Response to COVID-19, SNAP has had to extend its benefits to help people in need. The Food Relief is coordinated within States and by local and voluntary organizations to provide nutrition assistance to those most affected by a disaster or emergency.Visit SNAPs Interactive DashboardFood Relief has been extended, reach out to your state SNAP office, if you are in need.(iii) Follow these Steps to build an ArcGIS Pro StoryMap:Step 1: [Get Data][Open An ArcGIS Pro Project][Run a Hot Spot Analysis][Review analysis parameters][Interpret the results][Run an Outlier Analysis][Interpret the results]Step 2: [Open the Space-Time Pattern Mining 2 Map][Create a space-time cube][Visualize a space-time cube in 2D][Visualize a space-time cube in 3D][Run a Local Outlier Analysis][Visualize a Local Outlier Analysis in 3DStep 3: [Communicate Analysis][Identify your Audience & Takeaways][Create an Outline][Find Images][Prepare Maps & Scenes][Create a New Story][Add Story Elements][Add Maps & Scenes] [Review the Story][Publish & Share]A submission for the Esri MOOCSpatial Data Science: The New Frontier in AnalyticsLinda Angulo LopezLauren Bennett . Shannon Kalisky . Flora Vale . Alberto Nieto . Atma Mani . Kevin Johnston . Orhun Aydin . Ankita Bakshi . Vinay Viswambharan . Jennifer Bell & Nick Giner

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods.

Demo scene about tourism in Lyon. Find the related Story Map here.Points of interest in Lyon: museums, hotels, parcs, restaurants and churches.Source: Tourism PoI LyonTextured bridges (2012) from CityGMLSource: Textured bridgesOrthoimage from 2012 with a resolution of 10cm/pixel.Source: Orthoimage of Lyon 2012Elevation model from CityGML with 0.5m/Pixel resolutionSource: Elevation modelTextured buildings (2012) from CityGMLSource: Textured buildings

This dataset is about books and is filtered where the book is Camino de Santiago : St. Jean Pied de Port, Roncesvalles, Santiago de Compostela, Finisterre. Maps = Mapas = Cartes, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Here we present a geospatial dataset representing local- and regional-scale aquifer system boundaries, defined on the basis of an extensive literature review and published in GebreEgziabher et al. (2022). Nature Communications, 13, 2129, https://www.nature.com/articles/s41467-022-29678-7

The database contains 440 polygons, each representing one study area analyzed in GebreEgziabher et al. (2022). The attribute table associated with the shapefile has two fields (column headings): (1) aquifer system title (Ocala Uplift sub-area of the broader Floridan Aquifer System), and (2) broader aquifer system title (e.g., the Floridan Aquifer System).

• to explore the available data via an interactive app please visit the following URL: https://ucsb.maps.arcgis.com/apps/instant/nearby/index.html?appid=2a238e4ed2434d21b3b53c861a064d8f
• to explore the 3D nature of a dozen aquifer systems please see the following storymap: https://storymaps.arcgis.com/stories/a91f061759e64e44af38daf6cefa4259

The Global Mobile 3D Glass Market is segmented by 3D Enabled Mobile Devices (Smartphones, Notebooks, Tablets, and Eyewear), Device Components (Image Sensors, and 3D Displays), 3D Applications (Animations, Maps and Navigation, Mobile Gaming, and Mobile Advertisements), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle-East & Africa). The market sizes and forecasts are provided in terms of value (USD million).

C-ThroughBy prototyping c-through – an interactive 3D web application based on the ArcGIS JavaScript API 4.3 – we are aiming to support decision making in urban planning by providing tools that help explore and analyze the implications of usage distribution on a unit scale – that is e.g., on the level of apartments, stores or offices, commonly referred to as “spaces”. The web application developed in the scope of a 3-month summer internship was implemented for three locations on different scopes: Zurich, Vancouver and Dubai. The following description will focus on the Zurich use case.

Best viewed in fullscreen :)

"Walk beneath the largest animal on Earth and explore dozens of other exhibits representing 4.5 billion years of natural history.

Hintze Hall is the gateway to our collections and galleries.

Inside it, you can wander among meteorites, mammals, fish, birds, minerals, plants and insects, and hear stories about the people whose work and ideas have shaped the Museum." ~ http://www.nhm.ac.uk/visit/galleries-and-museum-map/hintze-hall.html

View the sames scene as a point cloud: https://sketchfab.com/3d-models/hintze-hall-nhm-london-point-cloud-be909aa8afa545118be6d36397529e2f

900 photos, RealityCapture, unofficial scan :)

https://i.imgur.com/0NH2iK8.png" alt=""> https://i.imgur.com/JzUBotw.png" alt=""> https://i.imgur.com/OsJJ0B6.png" alt="">

Source: Objaverse 1.0 / Sketchfab

A representation of the 12 primary ignitions which created the Smith River Complex. Aug 2023.This scene, when inserted into a story map, can create multiple views and this was the 3D scene to create all views. This scene includes ignitions and growth from Aug 16-30, from points to polygons. You are able to interactively play with each data layer when opened.

If you compressed the entire history of the Earth into 24 hours, the first Homo sapiens would arrive with just a couple seconds remaining on the clock. In this brief time our species has accomplished extraordinary things, especially in pursuit of our most foundational needs. But we've also had a profound impact on the planet—one that is unmatched by any other life form in the history of Earth.Our actions have pushed the planet into a new era: the Anthropocene, a time when human activity dominates the Earth. What does this mean for the Earth and all its other inhabitants? This series of story maps sets out to explore that very question through a combination of satellite imagery, 3D globes, interactive maps, and other data visualizations. Installments: Part 1: The Human Reach and AtlasPart 2: The Living Land and AtlasData: Esri World Imagery

LIDAR data was collected in 2008 covering Bastrop, Fayette, Hays counties. Products include Point Cloud, Bare Earth, Intensity imagery, 3D breaklines, and Contour data for the entire area. This Lidar operation was designed to provide a high density set of mass points within the defined areas suitable for the development of contours for use in hydraulic/hydrologic model development, and for assessing environmental impacts. This data is made available via the Texas Natural Resource Information System. The raw LIDAR data was converted to a digital elevation model at 1-m resolution using Lp360. This slope dataset was created using the ArcGIS slope tool.Full details on the Camp Swift Fire Experiment 2014 can be accessed through the "Camp Swift Fire Experiment 2014: Integrated Data Quality Assessment" story map. The full set of published data is contained on the United States Department of Agriculture Forest Service Research Data Archive.