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

Defining spatial feature engineering

As we mentioned at the beginning of this chapter, feature engineering refers to the manipulation and transformation of raw data into features that are best suited to your analytical exercise. In data science, feature engineering can take many forms, including the following:

  • Filling missing values, leveraging expert intuition, or various machine learning-based approaches
  • Scaling and normalization, whereby the range and center of data are adjusted to help train models and allow easier interpretation later on
  • Feature encoding, whereby categorical data is converted to binary True or False representation across multiple columns

Spatial feature engineering is very similar to the approaches taken in more general data science. It is the process of creating, or engineering, new and additional information from raw data using geographic context and knowledge. Engineering new features can be done by connecting data from two or more datasets...