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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

Introducing Geographic Information Systems and Geospatial Data Science

It is estimated that human society generates several quintillion bytes of data every day. The amount and speed with which our society generates data are also estimated to increase yearly as more and more devices become connected. Devices in the palms of our hands generate rich data assets ranging from detailed human movement data to data on purchasing behavior that connects online transactions with those made at physical storefronts. At the same time, remote sensing devices located outside our atmosphere are generating detailed images, known as satellite imagery, of the Earth at a 0.5-meter resolution, and this detail is improving at a breakneck pace.

Our ability to produce data is only rivaled by our ability to process that same data. Computer ecosystems are rapidly evolving and Moore’s Law, which states that computing power will double roughly every 2 years, is alive and well! Advances in CPUs, GPUs, and data storage components, combined with improved coding languages and analytical methods, allow us to process data and make data-informed decisions faster than ever.

With all of this data and improved technology, organizations and individuals are looking for better and more efficient ways to derive meaning from data that may have once been treated as a byproduct of a technical process. This desire to find meaning in data has led to data science being one of the most in-demand skill sets of the 21st century.

In the introductory chapter of this book, Geospatial Data Science with Python, we’ll begin by defining Geographic Information Systems (GIS), data science, and geospatial data science. These definitions will lay the groundwork and begin to develop a common vernacular that will enable you, the data scientists and traditional GIS professionals, to work in harmony to solve some of the most complex and, dare we say it, fun problems of modern times.

In this chapter, we will cover the following topics:

  • What is GIS?
  • What is data science?
  • What is geospatial data science?
You have been reading a chapter from
Applied Geospatial Data Science with Python
Published in: Feb 2023
Publisher: Packt
ISBN-13: 9781803238128
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