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

What this book covers

Chapter 1, Introducing Geographic Information Systems and Geospatial Data Science, lays the foundations for the book by introducing you to GIS and its commonalities with and differences from geospatial data science. In this chapter, we also walk through the data science pipeline that you’ll follow throughout the book.

Chapter 2, What Is Geospatial Data and Where Can I Find It?, introduces you to common geospatial data types and formats that you’ll work with throughout your geospatial data science workflows. In this chapter, we’ll also introduce various categories of geospatial data, ranging from human geography to country- and area-specific data.

Chapter 3, Working with Geographic and Projected Coordinate Systems, will introduce you to geographic and projected coordinate systems and help you avoid some of the most common pitfalls of working with geospatial data.

Chapter 4, Exploring Geospatial Data Science Packages, covers a wide variety of Python geospatial data science packages that allow you to perform spatial data processing, analysis, visualization, and modeling.

Chapter 5, Exploratory Data Visualization, shows you how to harness the power of spatial data to create compelling static and dynamic mapping applications.

Chapter 6, Hypothesis Testing and Spatial Randomness, introduces you to the topic of complete spatial randomness and a variety of statistical tests to better understand whether your data reflects patterns across space.

Chapter 7, Spatial Feature Engineering, will walk you through how to derive new spatial-based features known as summary spatial features and proximity spatial features from both tabular and geo-enabled data assets.

Chapter 8, Spatial Clustering and Regionalization, introduces you to a class of unsupervised machine learning models known as clustering models, through which you’ll create spatial clusters and regions from your data.

Chapter 9, Developing Spatial Regression Models, will open your eyes to the power that spatial data can bring to regression models through the incorporation of spatial effects.

Chapter 10, Developing Solutions to Spatial Optimization Problems, will show you how to use linear programming in combination with spatial data to solve problems such as the Vehicle Routing Problem and the Location Set Covering Problem.

Chapter 11, Advanced Topics in Spatial Data Science, covers more advanced topics in spatial feature engineering, spatial modeling, and spatial ethics.