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Making Spatial Decisions Using ArcGIS Pro

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This is a Higher Education GIS problem-solving, real-world scenario based guide, which features lessons from Keranen and Kolvoord's popular "Making Spatial Decisions" series that have been updated ...
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  • 25 September 2017
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"How can we protect...?" "Where do we allocate...?" "What's the extent and pattern of...?" You have questions in a spatial context; Making Spatial Decisions Using ArcGIS Pro has answers based in The Science of Where™.


Making Spatial Decisions Using ArcGIS Pro is a textbook that provides the user with a broad overview of the capabilities of using ArcGIS Pro to use geospatial tools to solve real-world problems. This book takes full advantage of the integrative nature of ArcGIS Pro and its advanced capabilities to seamlessly unite cloud-based and desktop GIS. The lessons included in this book have been adapted and updated from lessons from Keranen and Kolvoord's popular first three Esri Press books: Making Spatial Decisions Using GISMaking Spatial Decisions Using GIS and Remote Sensing, and Making Spatial Decisions Using GIS and Lidar.

Note: This e-book requires ArcGIS software. You can download the ArcGIS Trial at http://www.esri.com/arcgis/trial, contact your school or business Esri Site License Administrator, or purchase a student or individual license through the Esri Store.

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Price: $83.99
Publisher: Esri Press
Imprint: Esri Press
Series: Making Spatial Decisions
Publication Date: 25 September 2017
ISBN: 9781589484856
Format: eBook
BISACs: Cartography, map-making and projections, Technology: general issues, Reference works
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Preface
About the Authors
Acknowledgements

Introduction

Module 1: Hazardous emergency decisions
Project 1: An explosive situation in Springfield, Virginia
Project 2: Skirting the spill in Mecklenburg County, North Carolina

Module 2: Hurricane damage decisions
Project 1: Coastal flooding from Hurricane Katrina
Project 2: Hurricane Wilma storm surge

Module 3: Law enforcement decisions
Project 1: Crime in the nation’s capital
Project 2: Analyzing crime in San Diego, CA

Module 4: Composite Images
Project 1: Creating multispectral imagery of the Chesapeake Bay
Project 2: Multispectral Composite Bands of the Las Vegas Area

Module 5: Unsupervised Classification
Project 1: Unsupervised classification of the Chesapeake Bay
Project 2: Calculating unsupervised classification of Las Vegas, Nevada

Module 6: Supervised Classification
Project 1: Calculating supervised classification of the Chesapeake Bay
Project 2: Calculating supervised classification of Las Vegas, Nevada

Module 7: Basic Lidar Skills
Project 1: Basic lidar skills using Baltimore, MD data
Project 2: San Francisco, CA

Module 8: Location of Solar Panels
Project 1: James Madison University, Harrisonburg, Virginia
Project 2: San Francisco University, San Francisco, California

Module 9: Forest Vegetation Height
Project 1: George Washington National Forest, Virginia
Project 2: Michaux State Forest, PA

Appendix A: Image and Data Credits
Book Resources