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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

Exploratory Data Visualization

In Part 1, The Essentials of Geospatial Data Science, we provided you with a framework for working with spatial data and progressing through a spatial data science workflow. As a refresher, that framework looked like the one displayed in Figure 5.1:

Figure 5.1 – Data science pipeline

Figure 5.1 – Data science pipeline

In Part 2, Exploratory Spatial Data Analysis, the content will focus on the first three steps in the framework: Collecting, Cleaning, and Exploring. For the most part, the collecting step will largely be completed for you, but that will not be the case in the real world. When it comes to the cleaning step, it is often said that data scientists can spend as much as 80-90% of their time cleaning data. Even though we’ve collected most of the data for you, the data has not yet been cleaned, as learning to clean data—as you can see—is a much-needed and valuable skill to learn.

While the collecting and cleaning stages...

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