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


In this chapter, we introduced you to a number of clustering algorithms, including K-means and AHC. We also introduced you to a variant of AHC that leverages a spatial constraint via the spatial weights matrix to develop geographically constrained clusters known as regions.

For each of the clustering models, we evaluated the clusters through cluster profiling. We produced maps of each cluster and also calculated a variety of descriptive statistics, including cluster tract counts, average cluster tract area, and mean values. We then used this information to produce choropleth maps of each of the clustering algorithms.

In the final section of the chapter, we introduced you to the Calinski-Harabasz score, the Davies-Bouldin score, and the Silhouette score, which are common mathematical measures of clustering performance. Even though the K-means-based model scored the best mathematically, it may not be the clustering model that makes the most sense for your use case. For...