Focus on Geodatabases in ArcGIS Pro introduces readers to the geodatabase, the comprehensive information model for representing and managing geographic information across the ArcGIS platform.Sharing best practices for creating and maintaining data integrity, chapter topics include the careful design of a geodatabase schema, building geodatabases that include data integrity rules, populating geodatabases with existing data, working with topologies, editing data using various techniques, building 3D views, and sharing data on the web. Each chapter includes important concepts with hands-on, step-by-step tutorials, sample projects and datasets, 'Your turn' segments with less instruction, study questions for classroom use, and an independent project. Instructor resources are available by request.AUDIENCEProfessional and scholarly.AUTHOR BIODavid W. Allen has been working in the GIS field for over 35 years, the last 30 with the City of Euless, Texas, and has seen many versions of ArcInfo and ArcGIS come along since he started with version 5. He spent 18 years as an adjunct professor at Tarrant County College in Fort Worth, Texas, and now serves as the State Director of Operations for a volunteer emergency response group developing databases and templates. Mr. Allen is the author of GIS Tutorial 2: Spatial Analysis Workbook (Esri Press, 2016).Pub Date: Print: 6/17/2019 Digital: 4/29/2019 Format: PaperbackISBN: Print: 9781589484450 Digital: 9781589484467 Trim: 7.5 x 9.25 in.Price: Print: $59.99 USD Digital: $59.99 USD Pages: 260

Raczynski, K., Babineaux, C., & Cartwright, J. H. (2025). GEO Tutorial: Nine Ways For Spatial Data Interpolation in ArcGIS Pro. Mississippi State University: Geosystems Research Institute. [View Document] GEO Tutorial Number of Pages: 8Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

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.

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 ...

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.

GIS_data_and_jupyter_python_notebook.zip: Data for Modeling SDS via Random Forest Models. Contains a ArcGIS Pro project with example data collected at Marston Farm (Boone, IA) and cropped Planet scope 4-band imagery of the area for 2016, 2017 and 2018.Preview for jupyter notebook: preview of a jupyter (Python 3) notebook that demonstrates the use of Random forest classifier using the GIS data.

This packaged data collection contains all of the outputs from our primary model, including the following data layers: Habitat Cores (vector polygons) Least-cost Paths (vector lines) Least-cost Corridors (raster) Least-cost Corridors (vector polygon interpretation) Modeling Extent (vector polygon) Please refer to the embedded spatial metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in shapefile (.shp) or raster GeoTIFF (.tif) formats.

This project explores the feasibility of integrating solar-powered infrastructure into bike pathways as a sustainable energy and transportation solution for California. Using advanced tools like ArcGIS (for analysis), PVWatts, SAM, and JEDI, this study evaluates the economic, environmental, and technical implications through a conceptual case study based in Riverside. Insights drawn from global case studies and stakeholder feedback highlight challenges such as financial constraints, regulatory complexities, and technical design considerations, while also identifying opportunities for renewable energy generation, greenhouse gas emission reductions, and enhanced urban mobility. The conceptual case study serves as a framework for assessing potential benefits and informing actionable strategies. Recommendations address barriers and align implementation with California’s climate action and sustainability goals, offering a roadmap for integrating renewable energy with active transportation sy..., The data collection and processing methods for this project utilized a combination of publicly available tools and resources to ensure accuracy and usability. Key geospatial, energy modeling, and economic analysis data were gathered using reliable tools such as ArcGIS, SAM, JEDI, and PVWatts, with outputs systematically processed into accessible formats. This approach enabled comprehensive analysis of bike path integration, energy performance, and economic impacts.

Data Collection:

BikePaths_Riverside.qgz: Geospatial data detailing bike paths in Riverside was gathered from publicly available sources and initially analyzed using ArcGIS Pro. To ensure open access and reusability, the data has been converted to a .qgz project file compatible with QGIS (version 3.42), a free and open-source GIS platform.

SAM_Input_Variable_Values.csv: Input parameters were collected based on standard system specifications, financial assumptions, and default or adjusted inputs available in the System Ad..., , # Data for: Solar bike path feasibility study in California

https://doi.org/10.5061/dryad.4tmpg4fn1

Description of the data and file structure

The data was collected to evaluate the feasibility, technical requirements, and potential impacts of integrating solar-powered infrastructure into bike pathways. The study utilized geospatial data from ArcGIS for spatial analysis and site evaluation, combined with energy modeling tools such as PVWatts and SAM to estimate energy production, greenhouse gas reductions, and financial metrics. The JEDI model was employed to assess economic and job creation impacts. These efforts were guided by a conceptual case study in Riverside, California, to simulate real-world scenarios and inform actionable strategies for renewable energy integration. Feedback from stakeholders further shaped the analysis, addressing technical, economic, and regulatory challenges while aligning with California's sustainability goa...,

This study utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime lights (NTL) data from January 2018 to June 2023, sourced from the Google Earth Engine (GEE) platform. In total, 1,211 500-meter grids were created and analyzed based on the administrative boundaries of Taipei City. Various indicators, including land use, road networks, population, electricity consumption, and business prosperity, were integrated into temporal, spatial, and spatiotemporal models using Integrated Nested Laplace Approximations (INLA). Additionally, the Space Time Cube tool in ArcGIS Pro was employed for spatiotemporal pattern analysis.

Download In State Plane Projection Here The 2024 Parcel Fabric Data is a copy of the Lake County Chief Assessor's Office spatial dataset, consisting of separate layers which represent the boundaries for Tax Parcels, Lots, Units, Subs, Condos, Rights of Way, and Encumbrance parcels, along with points, lines, and PLSS townships for reference, which have all been captured for the 2024 Tax Year.This data is spatial in nature and does not include extensive fields of attributes to which each layer may be associated. This data is provided for use to individuals or entities with an understanding of Esri's ArcGIS Pro (specifically the Parcel Fabric), and those with access to ArcGIS Pro, which is necessary to view or manipulate the data.Casual users can find the standalone Tax Parcel Boundary Data here and Parcel Attribute Data here. Update Frequency: This dataset is updated on a yearly basis.

Spatial data layers of stream crossing point locations, cross-section polyline, centerline polyline, and bank polyline shapefiles have been developed for selected stream crossings in the Squannacook River basin, Massachusetts. The spatial data and calculated attribute values are model input data for U.S. Army Corps of Engineer’s Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic models. The stream crossing point locations were derived from the North Atlantic Aquatic Connectivity Collaboration (NAACC) database. The stream channel cross-sections, centerlines, and bank polylines were derived using automated methods in a Geographic Information System (GIS) using ArcGIS Pro and Python programming language. The polyline shapefiles are Z-enabled and have elevation data derived from Light Detection and Ranging (lidar) Digital Elevation Models (DEM) for Z-coordinate vertex values in units of feet. The polyline shapefiles are also M-enabled and have profile stationing values for the M-coordinate vertex values in units of feet. The automated GIS processes delineated a series of stream channel cross-sections along lidar-derived stream centerlines and have stream channel bathymetry estimated from Massachusetts bankfull channel geometry equations (Bent and Waite, 2013). The bankfull equations were also used to derive stream bank polylines. This data release contains the following shapefiles in the Spatial_Data_Layers.zip file: 1. Stream_Crossing_Locations.shp - Esri point shapefile derived from the NAACC stream crossing database. 2. Stream_Crossing_Watersheds.shp - Esri polygon shapefile of lidar-derived watershed boundaries that estimate the upstream drainage area for each stream crossing location. 3. Model_Cross_Sections.shp - Esri Z- and M-enabled polyline shapefile of the cross-section data used for hydraulic model input. 4. Model_Flowpaths.shp - Esri Z- and M-enabled polyline shapefile of the stream centerline and stream bank line data used for hydraulic model input. References: Bent, G.C., and Waite, A.M., 2013, Equations for estimating bankfull channel geometry and discharge for streams in Massachusetts: U.S. Geological Survey Scientific Investigations Report 2013–5155, 62 p., http://dx.doi.org/10.3133/sir20135155

Areas of land with slopes equal to or greater than 25 percent (approximately 12 degrees), derived from the 2019 Metro Lidar Project bare earth digital elevation model (DEM). The lidar data were acquired by NV5 and processed by GeoTerra, Inc. under contract to Metro. GeoTerra created a 3-foot bare earth DEM meeting USGS Quality Level 1 (QL1) standards with a vertical accuracy of less than or equal to 10 cm RMSE(z). The slope polygons were created by Metro using ArcGIS Pro Spatial Analyst tools and custom ArcPy workflows. The same 2019 DEM used to generate the Contours (two foot interval) and Slope (10 Percent) datasets served as the elevation source, ensuring consistency across all elevation-based layers. Date of last data update: 2019-08-31 This is official RLIS data. Contact Person: Franz Arend franz.arend@oregonmetro.gov 503-797-1742 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/3859 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use

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.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

Ice cover on the Great Lakes plays an important role in regional climate, supports tourism and recreation, and provides ecological habitat. As the climate warms, ice cover in the Great Lakes is expected to decline, which in turn will create more lake effect precipitation, reduce ice cover for recreation, and alter habitat for fishes. Therefore, it is important to understand historical ice patterns to better understand and predict future ice cover on the lakes. However, Great Lakes ice cover data prior to 1973 is scarce, due to the limited routine satellite observations. Our dataset aims to fill this gap by providing historical spatial ice duration layers to be used for modeling species distributions. ;ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview), QGIS (https://qgis.org/) or other spatial data software will be required to view this dataset.

Many people assume that poor credit scores translate to higher interest rates. But is this assumption true? Follow Jonathan Blum, New York author and journalist, as he attempts to answer this question using GIS. In this lesson, you'll map variations in online loan interest rates. Then, you'll use regression analysis to build a predictive model, quantifying the relationship between interest rates and loan grade rankings.

This workflow can be used to map and measure the correlation between any two variables. It's perfect for anyone interested in regression analysis in ArcGIS Pro.

In this lesson you will build skills in these areas:

• Mapping interest rate hotspots
• Performing regression analysis
• Interpreting regression results
• Finding minimum neighbor distance
• Building the spatial regression model

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.

BackgroundLimited access to water caused by its scarcity is becoming one of the most serious problems facing several developing countries including East Africa. For this, understanding the spatial distribution of limited access to water source service is more pertinent. The purpose of this study is to analyze the spatial distribution of limited access to water source services in East Africa countries and seeks to inform the development and implementation of targeted interventions and policies in East Africa.MethodsThis study analyzed data from recent demographic and health surveys conducted in 12 East African nations between 2012 and 2023. Data were gathered from 206,748 households. Global spatial autocorrelation was performed to analyze whether the pattern of limited access to drinking water service is clustered, dispersed, or random across the study areas. Once a positive global autocorrelation was confirmed, a local spatial autocorrelation analysis (Getis-OrdGi* statistics) was employed to detect local clusters. ArcGIS Pro 2.8.0 was used to map the clusters and Kulldorff SaTScan version 10.0.2 software using Bernoulli model were used for spatial scan statistical tests. The geostatistical ordinary kriging spatial interpolation technique was used to predict for unsampled areas based on sampled clusters.ResultLimited access to drinking water service were spatially clustered in the study area (Moran’s I: 0.16) (p