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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

View More author details

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

Why subscribe?

16. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

adaptive kernel 215

affinity propagation 177

agglomerative hierarchical clustering (AHC) algorithm 177, 184-187

Analysis Ready Data (ARD)

Collection 1 ARD 29

Collection 2 ARD 29

Annual Community Survey (ACS) 167

antipodal meridian 36

application programming interface (API) 66

areal interpolation 80

azimuthal equidistant projection 45

azimuthal projection 44

B

bandwidth parameter 215

Behrmann map projection 41

C

Calinski-Harabasz score 189

reference link 189

Canada’s Open Government 30

reference link 30

Capacitated Vehicle Routing Problem (CVRP) 221, 231, 245

exploring 245-247

cartogram 86

cartography 100

Census API

used, for extracting geodemographic data 166-169

Census Data API User Guide

reference link ...

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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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