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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

Hypothesis Testing and Spatial Randomness

In Chapter 5, Exploratory Data Visualization, you started to understand the first step of exploratory spatial data analysis (ESDA), which focused on data visualization through the creation of maps derived from the New York City Airbnb dataset. During your work, you noticed that the prices of Airbnb rentals are heavily skewed across New York’s geography, with what appeared to be groups of census tracts with higher and lower values in different parts of the city. For reference, take a look at Figure 6.1, which represents New York City Airbnb prices as a choropleth map. Areas highlighted by the red circle are groupings of higher values, while areas highlighted by the blue circle are groupings of lower values:

Figure 6.1 – NYC Airbnb prices

Figure 6.1 – NYC Airbnb prices

This chapter focuses on the second critical part of ESDA, which is testing for spatial structure present within data. Testing for spatial structure is important because...

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