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

Static and dynamic geospatial data

Geospatial data can typically be viewed as static or dynamic. Static geospatial data is data that does not change over a short-term time period. This data can include things such as the epicenter of an earthquake, the location of a store, or the number of college-educated adults. Dynamic geospatial data, in contrast, can change in real time. This data can include the location of a shopper within a shopping mall, the position of a bike courier delivering food, or the spread of an infectious disease such as the SARS-CoV-2 virus that caused the COVID-19 pandemic. Dynamic geospatial data is often referred to as spatiotemporal data, or data relating to both space and time. Static and dynamic geospatial data comes in two formats: vector and raster. We’ll discuss them in detail in the following section.