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

Developing geodemographic clusters

To begin your clustering exercise, it is helpful to talk about a few types of clustering algorithms that you’ll be leveraging within this section. Your first model will be developed using a K-means clustering algorithm. The K-means clustering algorithm aims to split your observations into a predefined number of clusters that minimizes within-cluster variance. Within-cluster variance measures the similarity of observations that are grouped together in the same cluster. Later on in this section, we’ll discuss how to develop clustering models using an agglomerative hierarchical clustering (AHC) algorithm. Agglomerative clustering begins with each observation in its own cluster and recursively merges pairs of clusters together based on a linkage metric. Similar to K-means, AHC aims to minimize within-cluster variance while maximizing between-cluster variance. There are many other types of clustering algorithms out there, such as density...