This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

Your manager has just assigned you to help the Park Service select some new observation points within Dinosaur National Park. These new observation points should meet a set of criteria based on their location. Twenty potential observation points have been identified. So, what is your next step? How can you use ArcGIS Pro to accomplish the analysis efficiently and accurately?After completing this course, you will be able to perform the following tasks:Use the appropriate geoprocessing tool for a given spatial problem.Demonstrate multiple methods for accessing geoprocessing tools.Use ArcGIS Pro to set geoprocessing environments.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Python Scripting for ArcGIS Pro stars with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. Leam how to execute geoprocessing tools, describe, create and update data, as well as execute a number of specialized tasks. See how to write simple, Custom scripts that will automate your ArcGIS Pro workflows.Some of the key topics you Will learn include:Python fundamentalsSetting up a Python editorAutomating geoprocessing tasksExploring and manipulating spatal and tabular dataWorking With geometriesMap scriptingDebugging ard error handlingHelpful "points to remember," key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and exercises are available online.Whether want to learn python or already have some experience, Python Scripting for ArcGlS Pro is comprehensive, hands-on book for learning versatility of Python coding as an approach to solving problems and increasing your productivity in ArcGlS Pro. Follow the step-by-step instruction and common workflow guidance for automating tasks and scripting with Python.Don't forget to also check out Esri Press's other Python title:Advanced Python Scripting for ArcGIS ProAUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPaul A Zandbergen is an associate professor of geography at the University of New Mexico in Albuquerque. His areas of expertise include geographic information science; spatial and statistical analysis techniques using GIS; error and uncertainty in spatial data; GIS applications in criminology, economics, health, and spatial ecology; terrain analysis and modeling; and community-based mapping using GIS and GPS.Pub Date: Print 7/7/2020 Digital: 7/7/2020ISBN: Print 9781589484993 Digital: 9781589485006 Price: Print: $79.99 USD Digital: $79.99 USD Pages: 420 Trim: 8 x 10 in.Table of ContentsPrefaceAcknowledgmentsChapter 1. Introducing Py%onChapter 2. Working with Python editorsChapter 3. Geoprocessing in ArcGIS ProChapter 4. Leaming Python language fundamentalsChapter 5. Geoprocessing using PythonChapter 6. Exploring spatial dataChapter 7. Debugging and error handlingChapter 8. Manipulating spatial and tabular dataChapter 9. Working with geometriesChapter 10. Working with rastersChapter 11. Map scriptingIndexPython Scripting and Advanced Python Scripting for ArcGIS Pro | Official Trailer | 2020-07-12 | 01:04Paul Zandbergen | Interview with Esri Press | 2020-07-10 | 25:37 | Link.

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

The ArcGIS system provides access to both imagery and tools for visualizing and analyzing imagery. Imagery collections from the ArcGIS Living Atlas of the World can be viewed through apps such as the Landsat Explorer app, ArcGIS Online Map Viewer, and ArcGIS Pro, while the Spatial Analyst extension and ArcGIS Image Analyst for ArcGIS Pro, more commonly know as the Image Analyst extension, provide raster functions, classification and change detection tools, and other advanced image interpretation and analysis tools. The tutorials in the Working with Imagery in ArcGIS learning path will introduce you to exploring and selecting imagery in ArcGIS web applications, applying indices and raster functions to imagery in ArcGIS Pro, and performing image classification and change detection in ArcGIS Pro.This ArcGIS Pro project package contains data for Tutorial 3, Performing Image Classification in ArcGIS Pro, and Tutorial 4, Performing Change Detection in ArcGIS Pro, of the learning path. Click Download to download the .ppkx file or click Open in ArcGIS Pro then open the pitemx file to download and open the package.Software Used: ArcGIS Pro 2.8. Project package may be opened in 3.x versions.File Size: 170mbDate Created: November 7, 2022Last Tested: December 5, 2024

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $39 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based GIS solutions offers enhanced accessibility, scalability, and cost-effectiveness, particularly appealing to smaller organizations. Secondly, the burgeoning need for precise spatial data analysis in various applications, including urban planning, geological exploration, and water resource management, significantly contributes to market growth. Thirdly, advancements in technologies such as AI and machine learning are integrating into GIS tools, leading to more sophisticated analytical capabilities and improved decision-making. Finally, the increasing availability of high-resolution satellite imagery and other geospatial data further fuels market expansion. However, market growth is not without challenges. High initial investment costs associated with implementing and maintaining sophisticated GIS systems can pose a barrier to entry for smaller businesses. Furthermore, the complexity of GIS software and the need for specialized skills to operate and interpret data effectively can limit widespread adoption. Despite these restraints, the market’s overall trajectory remains positive, with the cloud-based segment projected to maintain a dominant market share due to its inherent advantages. Growth will be geographically diverse, with North America and Europe continuing to be significant markets, while Asia-Pacific is expected to experience the fastest growth due to rapid urbanization and infrastructure development. The continued development of user-friendly interfaces and increased integration with other business intelligence tools will further accelerate market expansion in the coming years.

This map shows benthic marine habitats and reef complexes surroounding Sceirde Rocks off the West Coast of Ireland. MBES bathymetry and backscatter were interpreted to classifiy the map into reef areas. ArcGIS Pro Spatial Analyst tools were used to extract the rock outcrops and sediment classes were groundtruthed using sample data.

Gerinnesystem des Rindbach-Einzugsgebietes.

Daten Details

• Art der Daten: Gerinnesystem und Flussordnungszahl nach Strahler.

• Wie die Daten gesammelt oder abgeleitet wurden: Die Daten wurden mithilfe des "Spatial Analyst" tools mit ARCGIS Pro aus einem 1m digitalem Höhenmodell generiert.

• Koordinaten-Referenzsystem: EPSG:4312

• Namen-Konvention:
  • Strahler: Flussordnungszahl nach Strahler
  • Shape_Length: Länge des Gerinneabschnittes in Meter.

Pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. Recently discovered, pockmarks along the Aquitaine slope within the French EEZ, were manually mapped although two semi-automated methods were tested without convincing results. In order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition GAZCOGNE1, 2013. Bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the Kongsberg EM302 ship-borne multibeam echosounder of the R/V Le Suroît at a speed of ~8 knots, operated at a frequency of 30 kHz and calibrated with ©Sippican shots. Precision of seafloor backscatter amplitude is +/- 1 dB. Multibeam data, processed using Caraibes (©IFREMER), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. The present table includes 11 morphological attributes extracted from a Geographical Information System project (Mercator 44°N conserved latitude in WGS84 Datum) and additional parameters related to seafloor backscatter amplitudes. Pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on GAZCOGNE1 and BOBGEO2 bathymetric datasets. The pockmark area and its perimeter were calculated with the “Calculate Geometry” tool of Arcmap 10.2 (©ESRI) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). A first method to calculate pockmark internal depth developed by Gafeira et al. was tested (Gafeira J, Long D, Diaz-Doce D (2012) Semi-automated characterisation of seabed pockmarks in the central North Sea. Near Surface Geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). This method is based on the “Fill” function from the Hydrology toolset in Spatial Analyst Toolbox Arcmap 10.2 (©ESRI), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. The difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. Thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. For the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark. Latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “Zonal Geometry as Table” tool from Spatial Analyst Toolbox in ArcGIS 10.2 (©ESRI) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). Pockmark elongation was calculated as the ratio between the major and minor axis length. Cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). Cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “Zonal Statistics as Table” tool from Spatial Analyst Toolbox in ArcGIS 10.2 (©ESRI). Slope was calculated from bathymetry with “Slope” function from Spatial Analyst Toolbox in ArcGIS 10.2 (©ESRI) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). Seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneWhat can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location.This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme.Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields.The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

This layer is a subset of World Populated Footprint in 2020 Tile Image Layer.This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building. Also see the Urban Density Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for urban populations.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 14 to NoData (Null) and all other values become 1. The figure of 14 was empirically derived as a good balance between reducing errors of commission, i.e., false-positive cells with lower values, while not introducing errors of omission by eliminating obviously populated cells.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building. Also see the Urban Density Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for urban populations.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 14 to NoData (Null) and all other values become 1. The figure of 14 was empirically derived as a good balance between reducing errors of commission, i.e., false-positive cells with lower values, while not introducing errors of omission by eliminating obviously populated cells.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

This webmap is a subset of Global Urban Density Footprint in 2020 Tile Image Layer. This layer represents an estimate of the footprint of urban settings in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis. This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers.Also see the Populated Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for the footprint of total population.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 1499 to NoData (Null) and all other values become 1.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

This stormwater forecast script tool was developed by the Natural Resources Department at the Atlanta Regional Commission.WHAT IS THE STORMWATER FORECAST?In 2022, the District developed a novel water quantity-based indicator, the Stormwater Forecast, to support watershed managers with ongoing challenges related to water quality, streambank erosion, and nuisance flooding.The Stormwater Forecast is a planning-level estimate of the total potential storage volume required by Stormwater Control Measures to manage runoff from development at a basin scale under both current and future conditions. Based on current development patterns, the results of the Stormwater Forecast show the 15-county Metro Atlanta region should be managing up to 27 billion cubic feet of runoff volume with Stormwater Control Measures, and if regulations remain the same the total volumes are estimated to increase by up to 100 percent by 2040. STORMWATER FORECAST USER GUIDEThe Stormwater Forecast User Guide outlines steps for calculating stormwater runoff volumes for an area of interest using the Stormwater Forecast and performing a Stormwater Forecast Gap Analysis using the custom stormwater runoff volume results.STORMWATER FORECAST GEOPROCESSING PACKAGEThe Stormwater Forecast Geoprocessing Package contains the Stormwater Forecast Script Tool and a geodatabase with the following four parameters needed to execute the tool. AreaofInterestStormwaterForecastDevelopedAreaNLCD_Imperviousness_2019.tifThe Stormwater Forecast Script Tool provides users with an automated calculation method for calculating custom stormwater runoff volumes within an area of interest using the Stormwater Forecast.FIELD ABBREVIATIONS AND DESCRIPTIONS FOR STORMWATER FORECAST RESULTSUnique_ID = Unique Identification Characters for Stormwater Forecast SubcatchmentNHD_Sub_ID = National Hydrography Dataset Subcatchment Identification Numbers HUC_12 = Hydrologic Unit Code-12 Identification Numbers County = County Name HUC_8 = Hydrologic Unit Code-8 Identification Numbers MRB = HUC-8 Major River Basin Name Area_Dev_a = 2019 Developed Area, in acresImpv_Area = 2019 Total Impervious Area within Developed Area, in acresAOI_19_WQ = 2019 Water Quality Volume for Area of Interest, in cubic feet AOI_19_CP = 2019 Channel Protection Volume for Area of Interest, in cubic feetAOI_19_OF = 2019 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_30_WQ = 2030 Water Quality Volume for Area of Interest, in cubic feet AOI_30_CP = 2030 Channel Protection Volume for Area of Interest, in cubic feetAOI_30_OF = 2030 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_40_WQ = 2040 Water Quality Volume for Area of Interest, in cubic feet AOI_40_CP = 2040 Channel Protection Volume for Area of Interest, in cubic feetAOI_40_OF = 2040 Overbank Flood Protection Volume for Area of Interest, in cubic feetRequired Software: Esri’s ArcGIS Pro and Esri’s Spatial Analyst and Image Analyst Extensions

This web map is a subset of World Terrestrial Ecosystems to focus on Central Asia and Caucasus Region.The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneWhat can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location.This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme.Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields.The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

This layer is a subset of Populated Footprint in 2020 Global Coverage for the Pacific Region. This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building. Also see the Urban Density Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for urban populations.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 14 to NoData (Null) and all other values become 1. The figure of 14 was empirically derived as a good balance between reducing errors of commission, i.e., false-positive cells with lower values, while not introducing errors of omission by eliminating obviously populated cells.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

In this lesson you will build skills in these areas:

• Creating training schema
• Digitizing training samples
• Using deep learning tools in ArcGIS Pro
• Calculating VARI
• Extracting data to points

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

This topo data was generated from Lidar flown by USGS for the Salinas Watershed Basin in January through May 2018. Vertical accuracy is 10 cm. Point spacing is .7m. More information on the base Lidar data can be found at https://viewer.nationalmap.gov/basic/. https://inport.nmfs.noaa.gov/inport/item/48243Using ArcGIS Pro v 2.5, LAS files covering the City of Paso Robles were added to a Las Dataset (.lasd). Next, using the LAS Dataset to Raster tool, a DEM was created showing ground only with a cell size of 10, Interpolation Type of Binning, Cell Assignment of Average, and Void Fill Method of Linear. The DEM was next added to ArcMap 10.7 to utilize the spatial analyst license and where the Contour tool was used to create 5 ft. elevation contours. The contour data was next loaded back into ArcGIS Pro where the Smooth Line tool was used to smooth out the topo lines with a smoothing tolerance of 10 ft. and the Polynominal Approximation with Exponential Kernal (PEAK) smoothing algorithm. https://inport.nmfs.noaa.gov/inport/item/48243https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet