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

Spatial Clustering and Regionalization

You’ve learned a lot so far and it has brought you to the final section of this book, Part 3, Geospatial Modeling Case Studies. In this section, you’ll leverage all of the skills you gained so far and develop additional skills, as you work to implement geospatial models throughout a number of case study exercises. These case studies are applicable across a number of industries and will provide you with code that can be modified and enhanced in your work down the road.

In this chapter, we will discuss how you can use geospatial data and methods to assemble your observations into groups known as clusters. The process of creating clusters is known as clustering, which leverages unsupervised machine-learning techniques to define the clusters. Clustering is known as an unsupervised process because there is no ground truth value that you’re training your algorithm on. Instead, clustering attempts to derive structure from the...