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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

Spatial Feature Engineering

As we kick off this chapter, it’s helpful for us to recall the data science pipeline and identify where we are within it at this stage of the book. Take a look at Figure 7.1, the data science pipeline.

Figure 7.1 – Data science pipeline

Figure 7.1 – Data science pipeline

In Chapter 5, Exploratory Data Visualization, and Chapter 6, Hypothesis Testing and Spatial Randomness, you focused on exploring some datasets and testing for spatial relationships. Recall that in the New York Airbnb dataset, you identified that there was spatial autocorrelation present at both a global and local level. In this chapter, you’ll be focused on the part of the data science pipeline that we call processing, as highlighted in red in Figure 7.1. Other texts may refer to this step in the data science pipeline as data engineering or feature engineering. Within this step of the pipeline, your focus is on manipulating and transforming raw data into features that are...

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