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

Exploring Geospatial Data Science Packages

Toward the end of Chapter 3, Working with Geographic and Projected Coordinate Systems, we introduced you to the Python packages: PyProj, GeoPandas, and Matplotlib. You may recall that we used GeoPandas to read in a shapefile of state capitals, plotted them using Matplotlib, and then projected the capitals using PyProj. Reading in geospatial data, projecting the data, and then plotting it are common steps in numerous geospatial data science initiatives. However, we’ve just started to scratch the surface in terms of introducing you to the entire universe of geospatial data science packages and the powerful solutions that are just a few keystrokes away.

In this chapter, we’ll provide you with a deeper understanding of what PyProj, GeoPandas, and Matplotlib are capable of. We’ll also introduce you to a wide array of other geospatial data science packages that you’ll rely upon during your work. Some of these packages...