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

Teaching the model to think spatially

We kicked this chapter off with a brief disclaimer that it is important to consider spatial structures and incorporate them into the regression modeling process. This is especially important if the underlying data is generated via a geospatial process. Thankfully, there are numerous methods by which you can accomplish this. In this section, we will build spatial structures into our models in two ways. First, we’ll incorporate some of the spatially engineered variables that were constructed in Chapter 7, Spatial Feature Engineering. The second way we will build space into the model is by exploring spatial fixed effects, and we’ll talk more about this later on.

To begin, let’s go ahead and bring the spatially engineered variables into the equation. In the following first step, you’ll rerun the feature engineering process previously conducted to bring in the distance to some common NYC attractions:

  1. Recreate spatially...