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


By the time this book has been published, the world will have just formally exited a global pandemic, and society as a whole will be trying to grapple with the new normal in the post-COVID era. During the depths of the pandemic, spatial analysis was featured in prime time through the great work of Johns Hopkins University (JHU)’s COVID-19 dashboard, which can be found at The JHU dashboard monitored the spread of the virus across the globe in near real time, and this map was likely the first time that the masses were exposed to the power of spatial analysis, spatial data visualization, and spatial data science. However, spatial analysis has long been used to analyze the spread of diseases. In fact, way back in 1854, John Snow produced a map of cholera deaths in London, which allowed him to show that cholera was spread through germs in water wells and not through miasma in the air, as many thought during that time.

Reeling from this global pandemic is not the only problem that our modern society faces. Today, supply chain issues that face economies across the globe are driving inflation to heights not seen in several decades. In addition to this, climate change is causing major rivers across the globe to dry up, including the Colorado and Mississippi rivers in the United States, the Yangtze in China, the Rhine in Germany, and the Danube in Romania. Climate change is also leading to more extreme weather events, yielding devastating flooding in areas such as Florida in the United States and Pakistan in South Asia.

We are also living through a time in which more and more people are willing to stand up for equity and call out inequities when they see them. In the United States and across the world, teams of people are researching high-profile inequities in terms of the global food supply, healthcare access, and financial services. Others are looking into lesser-known inequities, such as urban heat islands and lack of shade. Collectively, teams of this kind are working hard to ensure that future generations won’t face the inequities of their forefathers.

We now have the data, tools, and technology to begin to do something about each of these problems. Spatial analysis and data science have the potential to provide enormous value in helping us find solutions, perform resiliency planning, and better educate ourselves and those around us. However, while performing spatial analysis and producing compelling visualizations is now easier than ever, it is not without risks. By nature, maps and spatial data are representations of real-world processes and are often incomplete or can easily be manipulated and thus the truth can be distorted. One recent example of map manipulation happened in an event that has since been dubbed “Sharpie-gate,” in which then-President Donald Trump altered an NOAA hurricane path map with a Sharpie in defiance of the scientific community. While this example may seem comical, there are many nuances to spatial analysis, data science, and cartography that you’ll need to be aware of as a burgeoning spatial data scientist.

This book is written for data scientists seeking to incorporate geospatial analysis into their work and for geographic information system (GIS) professionals seeking to incorporate data science methods into their work. Our goal is that this text will help these communities to develop a common understanding and shared vernacular, enabling them to properly incorporate geographic context into modeling, analysis, and visualization.

This book will begin with the fundamentals of GIS and data science before moving into detailed examples of spatial data science workflows built upon practical applications of geospatial data science that are industry agnostic. We will begin by teaching you the fundamentals of sourcing and working with geospatial data. Building upon this, we will teach you how to integrate spatial data and spatial thinking into your data science processes to hopefully improve model performance and develop a more accurate representation of the world around us.

We hope that you, as a member of the next generation of spatial data scientists, are empowered to leverage spatial thinking and analysis, which may help us find solutions to the problems currently facing our society and better prepare for the future ahead.