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

Point pattern analysis

Up until now, this chapter has solely focused on spatial autocorrelation. Spatial autocorrelation is just one spatial structure that can be tested. Another spatial hypothesis test falls within the domain of point pattern analysis. Point pattern analysis centers around the patterns present within point data instead of the attributes associated with the point data.

Studying the patterns present in point data is very common in the study of infectious diseases. As we discussed at the start of this section with respect to first- and second-order spatial effects, diseases are often clustered together around infected individuals or other infectious origins. One of the earliest uses of maps to identify the origin of an infectious disease was Dr. John Snow’s famous cholera map. While Dr. Snow didn’t have the statistics or technology that we have today, he was able to use maps and spatial data to identify that the infection originated from contaminated...