Book Image

Applied Geospatial Data Science with Python

By : David S. Jordan
3 (1)
Book Image

Applied Geospatial Data Science with Python

3 (1)
By: David S. Jordan

Overview of this book

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.
Table of Contents (17 chapters)
Part 1:The Essentials of Geospatial Data Science
Free Chapter
Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science
Part 2: Exploratory Spatial Data Analysis
Part 3: Geospatial Modeling Case Studies

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?