The Minnesota DNR Toolbox provides a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR.

Toolsets included in MNDNR Tools:
- Analysis Tools
- Conversion Tools
- General Tools
- LiDAR and DEM Tools
- Sampling Tools

The application download includes a comprehensive help document, which you can also access separately here: ArcGISPro_MNDNR_Toolbox_Pro_User_Guide.pdf

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

The market for GIS Mapping Tools is projected to reach a value of $XX million by 2033, growing at a CAGR of XX% during the forecast period (2025-2033). The market growth is attributed to the increasing adoption of GIS mapping tools by various industries, including government, utilities, and telecom, for a wide range of applications such as geological exploration, water conservancy projects, and urban planning. The convergence of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) is further driving market growth, as these technologies enable GIS mapping tools to provide more accurate and real-time data analysis. The market is segmented by type (cloud-based, web-based), application (geological exploration, water conservancy projects, urban planning, others), and region (North America, Europe, Asia Pacific, Middle East & Africa). North America is expected to remain the largest market for GIS mapping tools throughout the forecast period, due to the early adoption of these technologies and the presence of leading vendors such as Esri, MapInfo, and Autodesk. Asia Pacific is expected to experience the highest growth rate during the forecast period, due to the increasing adoption of GIS mapping tools in emerging economies such as China and India. Key industry players include Golden Software Surfer, Geoway, QGIS, GRASS GIS, Google Earth Pro, CARTO, Maptive, Shenzhen Edraw Software, MapGIS, Oasis montaj, DIVA-GIS, Esri, MapInfo, Autodesk, BatchGeo, Cadcorp, Hexagon, Mapbox, Trimble, and ArcGIS.

Enroll in this plan to get familiar with the user interface, apply commonly used tools, and master the basics of mapping and analyzing data using ArcGIS Pro.Goals Install ArcGIS Pro and efficiently locate tools, options, and user interface elements. Add data to a map, symbolize map features to represent type, categories, or quantities; and optimize map display at various scales. Create a file geodatabase to organize and accurately maintain GIS data over time. Complete common mapping, editing, and analysis workflows.

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 ...

The Travel Time Tool was created by the MN DNR to use GIS analysis for calculation of hydraulic travel time from gridded surfaces and develop a downstream travel time raster for each cell in a watershed. This hydraulic travel time process, known as Time of Concentration, is a concept from the science of hydrology that measures watershed response to a precipitation event. The analysis uses watershed characteristics such as land-use, geology, channel shape, surface roughness, and topography to measure time of travel for water. Described as Travel Time, it calculates the elapsed time for a simulated drop of water to migrate from its source along a hydraulic path across different surfaces of the replicated watershed landscape, ultimately reaching the watershed outlet. The Travel Time Tool creates a raster whereas each cell is a measure of the length of time (in seconds) that it takes water to flow across it, and then accumulates the time (in hours) from the cell to the outlet of the watershed.

The Travel Time Tool creates an impedance raster from Manning's Equation that determines the velocity of water flowing across the cell as a measure of time (in feet per second). The Flow Length Tool uses the travel time Grid for the impedance factor and determines the downstream flow time from each cell to the outlet of the watershed.

The toolbox works with ArcMap 10.6.1 and newer and ArcGIS Pro.

For step-by-step instructions on how to use the tool, please view MN DNR Travel Time Guidance.pdf

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 National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

Geographic Information System (GIS) Analytics Market Outlook

The global Geographic Information System (GIS) Analytics market size is projected to grow remarkably from $9.1 billion in 2023 to $21.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This substantial growth can be attributed to several factors such as technological advancements in GIS, increasing adoption in various industry verticals, and the rising importance of spatial data for decision-making processes.

The primary growth driver for the GIS Analytics market is the increasing need for accurate and efficient spatial data analysis to support critical decision-making processes across various industries. Governments and private sectors are investing heavily in GIS technology to enhance urban planning, disaster management, and resource allocation. With the world becoming more data-driven, the reliance on GIS for geospatial data has surged, further propelling its market growth. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with GIS is revolutionizing the analytics capabilities, offering deeper insights and predictive analytics.

Another significant growth factor is the expanding application of GIS analytics in disaster management and emergency response. Natural disasters such as hurricanes, earthquakes, and wildfires have highlighted the importance of GIS in disaster preparedness, response, and recovery. The ability to analyze spatial data in real-time allows for quicker and more efficient allocation of resources, thus minimizing the impact of disasters. Moreover, GIS analytics plays a pivotal role in climate change studies, helping scientists and policymakers understand and mitigate the adverse effects of climate change.

The transportation sector is also a major contributor to the growth of the GIS Analytics market. With the rapid urbanization and increasing traffic congestion in cities, there is a growing demand for effective transport management solutions. GIS analytics helps in route optimization, traffic management, and infrastructure development, thereby enhancing the overall efficiency of transportation systems. The integration of GIS with Internet of Things (IoT) devices and sensors is further enhancing the capabilities of traffic management systems, contributing to the market growth.

Regionally, North America is the largest market for GIS analytics, driven by the high adoption rate of advanced technologies and significant investment in geospatial infrastructure by both public and private sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the rapid urbanization, infrastructural developments, and increasing government initiatives for smart city projects. Europe and Latin America are also contributing significantly to the market growth owing to the increasing use of GIS in urban planning and environmental monitoring.

Component Analysis

The GIS Analytics market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the continuous advancements in GIS software solutions that offer enhanced functionalities such as data visualization, spatial analysis, and predictive modeling. The increasing adoption of cloud-based GIS software solutions, which offer scalable and cost-effective options, is further driving the growth of this segment. Additionally, open-source GIS software is gaining popularity, providing more accessible and customizable options for users.

The hardware segment includes GIS data collection devices such as GPS units, remote sensing instruments, and other data acquisition tools. This segment is witnessing steady growth due to the increasing demand for high-precision GIS data collection equipment. Technological advancements in hardware, such as the development of LiDAR and drones for spatial data collection, are significantly enhancing the capabilities of GIS analytics. Additionally, the integration of mobile GIS devices is facilitating real-time data collection, contributing to the growth of the hardware segment.

The services segment encompasses consulting, implementation, training, and maintenance services. This segment is expected to grow at a significant pace due to the increasing demand for professional services to manage and optimize GIS systems. Organizations are seeking expert consultants to help them leverage GIS analytics for strategic decision-making and operational efficiency. Additionally, the growing complexity o

Over 40,000 road crossings in Maine are maintained by Maine Department of Transportation (MaineDOT) managers, emergency managers, natural resource planners, and municipalities. Resource managers need a way to quickly and comprehensively assess, during the planning stages of potential transportation-related projects, how ecological, hydrologic, and structural characteristics of bridges and culverts and their watersheds could adversely affect project schedules and budgets. Factors that are critical to evaluate and incorporate into overall assessments of project risk include basin, land-use, and climatic characteristics; vulnerability to specific events, such as floods; and complicating factors in the watershed, such as endangered species, evacuation routes, and historical sites. A Python script tool has been built for ArcGIS Pro as an automated screening tool that draws on existing geographic information system (GIS) data layers to identify potential risk factors and quantify risk scores for bridges and culverts. This tool can help resource managers quickly evaluate projects, during early planning, in terms of variables that may adversely affect schedules or budgets.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes wheat production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Wheat ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BushelsSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.Many other ready-to-use layers derived from the Census of Agriculture can be found in the Living Atlas Agriculture of the USA group.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Geographic Information Systems Platform Market Outlook

The global Geographic Information Systems (GIS) Platform Market size is projected to reach remarkable heights with an estimated value of USD 12 billion in 2023 and is expected to balloon to over USD 25 billion by 2032, reflecting a robust Compound Annual Growth Rate (CAGR) of 8%. This impressive growth trajectory is largely driven by the increasing demand for location-based services across various industries, including urban planning, transportation, and agriculture. As the world becomes increasingly interconnected, the necessity for real-time location data and advanced mapping solutions has never been more crucial, thereby fuelling the expansion of the GIS platform market.

One significant growth factor for the GIS platform market is the rapid urbanization occurring on a global scale. With more than half of the world's population now living in urban areas, cities are becoming larger and more complex. This trend necessitates sophisticated urban planning solutions that can effectively map, analyze, and visualize urban growth patterns. GIS platforms provide critical tools that enable urban planners to make informed decisions about land use, transportation networks, and infrastructure development. By integrating geographic data with socio-economic data, GIS applications help cities manage resources more efficiently and sustainably, thus driving the market forward.

Another driver of growth in the GIS platform market is the escalating need for effective disaster management solutions. Natural disasters such as hurricanes, earthquakes, and floods are becoming more frequent and severe, posing significant challenges for governments and emergency services worldwide. GIS platforms enable authorities to predict, prepare for, and respond to these disasters more effectively by providing detailed maps and models that can simulate potential scenarios and outcomes. The ability to integrate real-time data with historical records allows emergency response teams to optimize resource allocation and logistics, minimizing the impact of disasters on human lives and infrastructure.

The transportation and logistics sector is also a significant contributor to the growth of the GIS platform market. As global trade and e-commerce continue to grow, the demand for efficient and reliable transportation networks is increasing. GIS platforms provide valuable insights into route optimization, traffic management, and supply chain logistics. By enabling companies to analyze geographic data, GIS applications help to reduce transportation costs, improve delivery times, and enhance overall supply chain efficiency. As businesses increasingly look to leverage location-based data to gain a competitive advantage, the GIS platform market is set to experience sustained growth.

The role of a GIS Controller is becoming increasingly vital as the GIS platform market expands. A GIS Controller is responsible for overseeing the integration and management of geographic data within an organization, ensuring that the data is accurate, up-to-date, and accessible. This role involves coordinating with various departments to implement GIS solutions that align with organizational goals and enhance decision-making processes. As organizations across industries recognize the value of geographic data, the demand for skilled GIS Controllers is on the rise. These professionals play a crucial role in optimizing the use of GIS technology, enabling organizations to leverage location-based insights for strategic advantage.

Regionally, North America is anticipated to dominate the GIS platform market due to its advanced technological infrastructure and high adoption rates among various industries. The presence of leading GIS service providers in this region further bolsters its market position. Additionally, Asia Pacific is projected to witness the fastest growth over the forecast period, driven by rapid urbanization and increasing government initiatives to integrate GIS technology into urban planning and disaster management. The Middle East & Africa and Latin America are also expected to emerge as lucrative markets, as these regions look to harness the potential of GIS platforms to address their unique geographic challenges and drive economic development.

Component Analysis

The GIS platform market can be divided into three primary components: software, hardware, and services. Each of these segments plays a vital role in the overall functionality and adap

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.

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

Inspired by "Add GTFS to a Network Dataset" tool by Melinda Morang, I have generated this tool to use GTFS public transit data in ArcGIS so you can run schedule-aware analyses without using the Network Analyst.

The abundant access is the first in series of tools I am developing for ArcGIS to analyse the GTFS data. Simplicity is the main objective here, therefore all the analysis will be done in-fly.

The term "abundant access" is borrowed from Jarrett Walker's book, Human transit. You can use the abundant access to perform transit/pedestrian accessibility analyses, controlling for the number of transfers, walking between transfers, walking to transit and walking from transit. My aim is to develop a method that is useful for practitioners and decision-makers to make day-to-day decisions.

Note: No installation is necessary. This tool is only available for ArcGIS 10.4 or higher. It also works with ArcGIS Pro. This tool is still under development so please feel free to contact me if you encounter bugs or other problems or you simply have ideas or suggestions.For more information and updates, visit www.spatialanalyst.ir.