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 different types of spatial optimization that you can solve using Python. We kicked this chapter off by discussing LSCPs, where you found the optimal number of facilities needed to serve the emergency service demand of a community.

Then, we transitioned our focus to discussing route-based optimization problems, including TSP, VRP, and CVRP.

You explored three different case studies in this section using two different integer linear programming formulations (MTZ and DFJ), which help the optimization abide by the global constraint that a person or vehicle can only visit each stop once.

You were also introduced to a handful of new packages, including Spopt, which is PySAL’s spatial optimization library. You also learned about PuLP for solving integer optimization problems. Lastly, you set up a Google Maps API to gather real-world distances in the form of an O-D Cost Matrix from Google Map’s street network.

We covered...