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

Introduction to GWR models

GWR models vary from OLS-based models in that instead of fitting a set of global estimates, GWR examines the way in which the relationship between each predictor variable varies across space with respect to the dependent variable. GWR does this by iteratively fitting a localized regression within a search window or neighborhood around each observation. The observation for which the regression is being fit is known as the regression point. Observations that are closer to the regression point are weighted more heavily in the regression than observations that are further away.

Fitting a regression within these local neighborhoods is performed by using either a fixed kernel or an adaptive kernel. A fixed kernel uses an identical search area across all regression points, while an adaptive kernel’s search area can vary across space. Figure 9.11 shows a fixed kernel approach compared to an adaptive kernel approach.

Figure 9.11 – Fixed and adaptive kernels

Figure 9.11...