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Spatial Data Science

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Spatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can...
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  • 26 November 2024
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Spatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can provide many of the building blocks, it's critical for transforming data into information, knowledge, and solutions.

This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.

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Price: $79.99
Pages: 250
Publisher: Esri Press
Imprint: Esri Press
Publication Date: 26 November 2024
Trim Size: 9.00 X 7.00 in
ISBN: 9781589486102
Format: Paperback
BISACs: Geographical information systems, geodata and remote sensing, Computer science, Data science and analysis: general
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Dr. John P. Wilson

USC Professor and Founding Director

Spatial Sciences Institute

USC Dana and David Dornsife

College of Letters, Arts and Sciences

Professor, Departments of Civil & Environmental Engineering and Computer Science, Viterbi School of Engineering

Professor, Department of Population and Public Health Sciences, Keck School of Medicine of USC

Professor, Department of Sociology, Dornsife College of Letters, Arts and Sciences

Professor, USC School of Architecture

Editor, Transactions in GIS, Wiley-Blackwell

Editor, Geographic Information Science & Technology Body of Knowledge, University Consortium for Geographic Information Science

Fellow, University Consortium for Geographic Information Science (2014)

Fellow, American Association of Geographers (2021)

Table of Contents

Introduction

Our Changing Planet

Role and Character of Geographic Information Science, Systems, and Services Today

 Special Characteristics of Spatial Information

The Digital Era

The Opportunities Afforded by Spatial Data Science

Conclusions and Future Prospects