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’ve introduced you to a number of Python packages that you’ll frequently leverage in your day-to-day work as a geospatial data scientist. In this chapter, you learned about a variety of packages, such as GeoPandas and Rasterio, for easily reading, manipulating, and writing geospatial vector and raster data structures. In the second part of the chapter, you learned about PySAL and its ecosystem of packages for exploring, modeling, and visualizing geospatial data. The last section of this chapter introduced you to five geospatial packages that enable you to produce production-quality static and interactive geospatial data visualizations.

With these packages, you can now interact with geospatial data and their underlying geometries. Interacting with data is one of the first steps in any data science pipeline. You also now have the skills to produce a variety of geospatial data visualizations, which will be important as you tell the story of...