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
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Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science
Part 2: Exploratory Spatial Data Analysis
Part 3: Geospatial Modeling Case Studies

What Is Geospatial Data and Where Can I Find It?

To answer the first part of our question, geospatial data, in simplest terms, is data that has a geographic component—that is, a component that ties the data to a point on, or adjacent to, the Earth’s surface. To answer the second part of our question, geospatial data is quite literally all around you.

There are large volumes of geospatial open data that is collected, maintained, and released by public entities such as government agencies or non-governmental organizations (NGOs) as well as private corporations. The availability of geospatial data within the open data ecosystem has led to the rise of robust data standards that were developed and are actively used by the geospatial community. The development of community standards is talked about briefly in this chapter as it relates to data formats and publications.

In this chapter, you’ll learn about the following topics:

  • Static and dynamic geospatial...