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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

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781803238128
Length 308 pages
Edition 1st Edition
Languages
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Author (1):
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David S. Jordan David S. Jordan
Author Profile Icon David S. Jordan
David S. Jordan
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Table of Contents (17) Chapters Close

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 16. Other Books You May Enjoy

Point pattern analysis

Up until now, this chapter has solely focused on spatial autocorrelation. Spatial autocorrelation is just one spatial structure that can be tested. Another spatial hypothesis test falls within the domain of point pattern analysis. Point pattern analysis centers around the patterns present within point data instead of the attributes associated with the point data.

Studying the patterns present in point data is very common in the study of infectious diseases. As we discussed at the start of this section with respect to first- and second-order spatial effects, diseases are often clustered together around infected individuals or other infectious origins. One of the earliest uses of maps to identify the origin of an infectious disease was Dr. John Snow’s famous cholera map. While Dr. Snow didn’t have the statistics or technology that we have today, he was able to use maps and spatial data to identify that the infection originated from contaminated...

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