Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

Feature layer generated from the Current Weather and Wind Station Data Living Atlas layer for the Learn ArcGIS lesson Predict weather with real-time data.

Published to allow joining of spreadsheet data to county geometry in ESRI Maps for Office or Map Analysis Tools, contains Iowa DOM County Code (1-99) as a small integer, Census County FIPS as a both an string and integer. This data was originally created by the Iowa DNR and digitized from USGS 7.5' topographic maps.Click on the data tab above to see an example of expected data. OCIO has a tutorial on how to join your spreadsheet to this Feature layer to create a new feature layer with your county based information. Please contact patrick.wilke-brown@iowa.gov.

Feature layer generated from running the Overlay layers solution.

Feature layer for the tutorial Analyze volcano shelter access in Hawaii.This layer is a copy of the Volcano Lava Flow Hazard Zones by HawaiiStateGIS.The boundaries and classification of lava flow hazard zones on Hawaii Island were first mapped by the US Geological Survey in 1974. This classification scheme divides the island into 18 major zones that are ranked from 1 through 9 based on the probability of coverage by lava flows. The risk levels are based primarily on the location and frequency of historic eruptions (those for which there are written records or that are known from the oral traditions of the Hawaiians) and the geologic mapping and scientific dating of the old flows from prehistoric eruptions.Much of the USGS work was based on a paper called Geologic Map of the Island of Hawaii by Edward Wolfe and Jean Morris.

This feature service is designed to be used as a mapping tool for high schools in Canada in conjunction with the Creating a Collaborative Map tutorial. The service contains more features than are available in the MapMyCampus service and uses symbology that makes them stand out from an underlying basemap.

This feature service is designed to be used as a mapping tool for high schools in Canada in conjunction with the Creating a Collaborative Map tutorial. The service contains more features than are available in the MapMyCampus service and uses symbology that makes them stand out from an underlying basemap.

Feature layer generated from running the Enrich layer solution. Hexagons_within_VA were enriched

When talking about creating a better future, it is inevitable to bring up the topic of climate change, and more specifically, global warming. One of the most pressing issues facing society, global warming may put an end to our future, if we don't act soon.ProcessBuilding on my work on biodiversity last year, I delve deeper into a new issue, Global Warming. This year I focused on the projected impacts of global warming on Missouri. The StoryMap analyzes the projected temperatures, projected precipitation levels, farm sales, corn production, and calculations necessary to suggest possible differences between now and the future.To start, I needed to complete some tutorials teaching me how to use the multidimensional analysis filter on ArcGIS to provide raster projection data. I also needed to complete a tutorial teaching how to make a swipe map as an instant application. After completing these two things, I began putting my new skills to work building maps. After creating the basic outline for my StoryMap, I began creating the maps I would need to illustrate the difference between the 2030 projections and the 2090 projections. To do this, I first filtered down a nationwide layer depicting all the county boundaries to just Missouri. I then took a Bioclimate Projection raster layer, and aggregated the Missouri data by county, summarizing it utilizing a zonal statistics table. I then input the data into Microsoft Excel, and created a pivot table, which gave me a table with county names as the rows, and 12 columns for each of the 4 time zones associated with each of the 3 variables. I exported this file as a CSV into my maps on ArcGIS, and joined the filtered down county feature with this CSV, joining the two by county name. This created a layer of Missouri counties in which I could represent each of the 12 different scenarios by changing which one I wanted to display. As a final added layer of analysis, I created a calculated column showing the change from 2030-2090 across the worst case scenario. This method of analysis was used for both temperature and precipitation projections.The other non-temperature-based maps were created using LivingAtlas filters on ArcGIS online. These maps were the farming sales map, the national corn production map, and the Missouri specific corn production map. Finally, express maps were created to point out movement of crops and pinpoint locations that I would talk about later. There are two of these within this project.Content SourcesAll map layers were derived from ArcGIS Online and the other community layers on ArcGIS.com.Base layers were provided by ArcGIS Online.All other sources of content have been hyperlinked to the text in which they apply to.Content AnalysisTo analyze my content, I created a web map or story map including all the relevant layers, maps, and information about global warming in order to display the projected disastrous effects it could have on the people of Missouri and Missouri's economy. A major player in content analysis was the swipe map, which easily allows the reader to view the differences between two different time period, one in the near future and one in the far future. Using the same color ramp that has the same minimum and maximum values for temperature or precipitation within these swipe maps adds another layer of analysis.Original DataTo complete my original data requirements for this project, I used the multidimensional filter to understand projections for the data. I also aggregated the Bioclimate Projection data to Missouri Counties, using zonal statistics tables. By joining a nationwide county boundary layer I filtered down to Missouri only, and a CSV created by making a pivot table out of the aggregated Bioclimate Projection data, I created another piece of original data. My final piece of original data was the line graph used to analyze the 3 different scenarios for temperature projections.CreditsThanks to Paul Hoelscher, AP World teacher at Clayton High School for serving as the school coordinator for this project. Also thanks to Dr. Bob Coulter for serving as my GeoMentor for this project and providing technical support with ArcGIS Online. Finally, thanks to my parents for coordinating project meetings, and supporting my work on this project.

This feature layer is intended to be used in conjunction with the OSP Activity 420 for FEMA's CRS imagery layer. This layer represents the relevant management attributes of areas that are likely eligible for Open Space Preservation (OSP) - Activity 422a credit through the Federal Emergency Management Agency’s (FEMA) Community Rating System (CRS). It is intended to standardize screening-level OSP data for the U.S. and enable planners and floodplain managers across the nation to participate in the CRS program at a level that was not possible in the past due to data limitations. Ultimately, more communities participating in CRS will 1) help FEMA meet their mission to help communities prepare for, protect against, and recover from flood hazards, 2) help The Nature Conservancy meet their mission to make communities more resilient to flooding by conserving open space and restoring natural floodplain functions, and 3) make flood insurance more affordable for people both inside and outside of the regulatory floodplain. A tutorial on how to use this service can be found at Assess open space to lower flood insurance cost.Under the National Flood Insurance Program (NFIP), CRS is a voluntary program that provides flood insurance discounts to communities that take action to reduce their flood risk. The OSP activity is one of the largest point contributors and can greatly improve a CRS community’s overall score, which incentivizes nature-based solutions to reduce flood risk while also making flood insurance more affordable. The data in this image service are a modified subset of the USGS's Protected Areas Database of the United States (PAD-US). In accordance with the 2017 CRS Manual requirements Esri removed all Federally or Tribally owned or managed lands larger than 10 acres. The National Hydrography Database (NHD) was then intersected with the remaining PADUS areas to extract all bodies of water larger than 10 acres and major rivers. The resulting vector dataset was then converted to 30 m raster and snapped to the National Land Cover Database (NCLD) Impervious Surface Estimation dataset. The final imagery layer represents the IDs of each of those PAD-US polygons.