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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

The fundamentals of ESDA

Mapmaking, also known as cartography, is the first step in ESDA. Mapmaking is a blending of art and science. It is an art form in that you’re taking data and representing it in a visual format that is easy to interpret and derive meaning from. Representing data in a visual format is critical for the understanding of both technical and non-technical stakeholders. It is science in that the visuals must be derived from data and they must honor the underlying metadata such as the coordinate reference systems from which they were collected.

Mapmaking is not a standard practice in more traditional EDA, which is traditionally focused on understanding basic statistics of the data such as mean and standard deviation. EDA also focuses on understanding the distribution of the data, dealing with missing data, identifying outliers, and understanding the correlations among variables. In this chapter, you’ll work with data and will be conducting the work...

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