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Applied Geospatial Data Science with Python

You're reading from Applied Geospatial Data Science with Python Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

Product type Paperback

Published in Feb 2023

Publisher Packt

ISBN-13 9781803238128

Length 308 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

David S. Jordan

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Table of Contents (17) Chapters

Preface

1. Part 1:The Essentials of Geospatial Data Science

2. Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science FREE CHAPTER

3. Chapter 2: What Is Geospatial Data and Where Can I Find It?

4. Chapter 3: Working with Geographic and Projected Coordinate Systems

5. Chapter 4: Exploring Geospatial Data Science Packages

6. Part 2: Exploratory Spatial Data Analysis

7. Chapter 5: Exploratory Data Visualization

8. Chapter 6: Hypothesis Testing and Spatial Randomness

9. Chapter 7: Spatial Feature Engineering

10. Part 3: Geospatial Modeling Case Studies

11. Chapter 8: Spatial Clustering and Regionalization

12. Chapter 9: Developing Spatial Regression Models

13. Chapter 10: Developing Solutions for Spatial Optimization Problems

14. Chapter 11: Advanced Topics in Spatial Data Science

15. Index

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Authors (1)

David S. Jordan

David S. Jordan

David S. Jordan has made a career out of applying spatial thinking to tough problem spaces in the domains of real estate planning, disaster response, social equity, and climate change. He currently leads distribution and geospatial data science at JPMorgan Chase & Co. In addition to leading and building out geospatial data science teams, David is a patented inventor of new geospatial analytics processes, a winner of a Special Achievement in GIS (SAG) Award from Esri, and a conference speaker on topics including banking deserts and how great businesses leverage GIS.

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