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You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

Continuing the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro 2.6 teaches new and existing GIS users how to get started solving problems using ArcGIS Pro. Using ArcGIS Pro for these tasks allows you to understand complex data with the leading GIS software that many businesses and organizations use every day.Getting to Know ArcGIS Pro 2.6 introduces the basic tools and capabilities of ArcGIS Pro through practical project workflows that demonstrate best practices for productivity. Explore spatial relationships, building a geodatabase, 3D GIS, project presentation, and more. Learn how to navigate ArcGIS Pro and ArcGIS Online by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments. Using figures to show each step, Getting to Know ArcGIS Pro 2.6 demystifies complicated process like developing a geoprocessing model, using Python to write a script tool, and the creation of space-time cubes. Cartographic techniques for both web and physical maps are included.Each chapter begins with a prompt using a real-world scenario in a different industry to help you explore how ArcGIS Pro can be applied for operational efficiency, analysis, and problem solving. A summary and glossary terms at the end of every chapter help reinforce the lessons and skills learned.Ideal for students, self-learners, and seasoned professionals looking to learn a new GIS product, Getting to Know ArcGIS Pro 2.6 is a broad textbook and desk reference designed to leave users feeling confident in using ArcGIS Pro on their own.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOMichael Law is a cartographer and GIS professional with more than a decade of experience. He was a cartographer for Esri, where he developed cartography for books, edited and tested GIS workbooks, and was the editor of the Esri Map Book. He continues to work with GIS software, writing technical documentation, teaching training courses, and designing and optimizing user interfaces.Amy Collins is a writer and editor who has worked with GIS for over 16 years. She was a technical editor for Esri, where she honed her GIS skills and cultivated an interest in designing effective instructional materials. She continues to develop books on GIS education, among other projects.Pub Date: Print: 10/6/2020 Digital: 8/18/2020 ISBN: Print: 9781589486355 Digital: 9781589486362 Price: Print: $84.99 USD Digital: $84.99 USD Pages: 420 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1 Introducing GISExercise 1a: Explore ArcGIS OnlineChapter 2 A first look at ArcGIS Pro Exercise 2a: Learn some basics Exercise 2b: Go beyond the basics Exercise 2c: Experience 3D GISChapter 3 Exploring geospatial relationshipsExercise 3a: Extract part of a dataset Exercise 3b: Incorporate tabular data Exercise 3c: Calculate data statistics Exercise 3d: Connect spatial datasetsChapter 4 Creating and editing spatial data Exercise 4a: Build a geodatabase Exercise 4b: Create features Exercise 4c: Modify featuresChapter 5 Facilitating workflows Exercise 5a: Manage a repeatable workflow using tasks Exercise 5b: Create a geoprocessing model Exercise 5c: Run a Python command and script toolChapter 6 Collaborative mapping Exercise 6a: Prepare a database for data collection Exercise 6b: Prepare a map for data collection Exercise 6c: Collect data using ArcGIS CollectorChapter 7 Geoenabling your projectExercise 7a: Prepare project data Exercise 7b: Geocode location data Exercise 7c: Use geoprocessing tools to analyze vector dataChapter 8 Analyzing spatial and temporal patternsExercise 8a: Create a kernel density map Exercise 8b: Perform a hot spot analysis Exercise 8c: Explore the results in 3D Exercise 8d: Animate the dataChapter 9 Determining suitability Exercise 9a: Prepare project data Exercise 9b: Derive new surfaces Exercise 9c: Create a weighted suitability modelChapter 10 Presenting your project Exercise 10a: Apply detailed symbology Exercise 10b: Label features Exercise 10c: Create a page layout Exercise 10d: Share your projectAppendix Image and data source credits Data license agreement GlossaryGetting to Know ArcGIS Pro 2.6 | Official Trailer | 2020-08-10 | 00:57

Video based training seminar.

The GIS database used in this project serves as a centralized repository for all spatial datasets required for wildfire threat analysis and model training. It includes CAL FIRE’s Wildland Fire Threat layer, which provides pixel-based classifications of wildfire potential across California, as well as transportation infrastructure layers, including primary and secondary roads and railways.To support impact analysis, 1,000-foot buffer zones were generated around each infrastructure feature to define zones of interest for wildfire segmentation. The database is structured for integration into both machine learning workflows and GIS environments, enabling seamless overlay, visualization, and spatial querying within platforms such as ArcGIS Pro or QGIS.

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.

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.

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.

This data is a graphic representation of natural gas pipelines. The file has not been certified by a Professional Surveyor. This data is not suitable for legal purposes. The purpose of this data is to provide a generalized statewide view of natural gas pipelines.

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).

vfillDL.zip

dems: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . extents: extents of the training, testing, and validation areas. These extents were defined by the researchers. vectors: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.

Learn, Reconnect, and Discover the latest advances in Geographic Information Systems (GIS) technology when the New Zealand Esri User Conference returns in-person. Join hundreds of users from around the New Zealand and the South Pacific to discover how they’re leveraging GIS capabilities to solve problems, create shared understanding, and map common ground.This year's 3-day event includes not-to-be-missed opportunities for training, networking and sharing your own stories and experiences.A 2-day option is available for those short on time, while a 4-day option includes discounted instructor-led training for migrating to ArcGIS Pro.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

An ArcMap map package is a portable file that contains a map document (.mxd) as well as the data referenced by the map layers. (ArcGIS Pro map packages are similar but have different file extensions.) Even if you're not an ArcMap user, you may need at some point to bring a map document or map package into ArcGIS Pro. You don't need ArcMap software to do this tutorial.Estimated time: 20 minutesSoftware requirements: ArcGIS Pro

ArcGIS Pro allows you to store multiple items, such as maps, layouts, tables, and charts, in a single project and work with them as needed. The application also responds contextually to your work. Tabs on the ribbon change depending on the type of item you're working with.In this tutorial, you'll explore the main components of the ArcGIS Pro user interface—the ribbon, views, and panes—and their interactions.

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.

Esri's Water Resources GIS Platform offers a comprehensive suite of tools and resources designed to modernize water resource management. It emphasizes geospatial solutions for monitoring, analyzing, and modeling water systems, helping decision-makers tackle challenges like drought resilience, flood mitigation, and environmental protection. By leveraging the capabilities of ArcGIS, users can transform raw water data into actionable insights, ensuring more efficient and effective water resource management.A central feature of the platform is Arc Hydro, a specialized data model and toolkit developed for GIS-based water resource analysis. This toolset allows users to integrate, analyze, and visualize water datasets for applications ranging from live stream gauge monitoring to pollution control. Additionally, the platform connects users to the ArcGIS Living Atlas of the World, which offers extensive water-related datasets such as rivers, wetlands, and soils, supporting in-depth analyses of hydrologic conditions. The Hydro Community further enhances collaboration, enabling stakeholders to share expertise, discuss challenges, and build innovative solutions together.Esri’s platform also provides training opportunities and professional services to empower users with technical knowledge and skills. Through instructor-led courses, documentation, and best practices, users gain expertise in using ArcGIS and Arc Hydro for their specific water management needs. The combination of tools, datasets, and community engagement makes Esri's water resources platform a powerful asset for advancing sustainable water management initiatives across public and private sectors.

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If you have geographic information stored as a table, ArcGIS Pro can display it on a map and convert it to spatial data. In this tutorial, you'll create spatial data from a table containing the latitude-longitude coordinates of huts in a New Zealand national park. Huts in New Zealand are equivalent to cabins in the United States—they may or may not have sleeping bunks, kitchen facilities, electricity, and running water. The table of hut locations is stored as a comma-separated values (CSV) file. CSV files are a common, nonproprietary file type for tabular data.Estimated time: 45 minutesSoftware requirements: ArcGIS Pro

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