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

A refresher on regression models

It is best if we start with a brief refresher on regression models in general to ensure a common understanding. Let’s begin with the following regression equation:

Let's break down the notation in this equation:

  • Y is the dependent variable, representing the process you are trying to explain or predict.
  • is the intercept, which is the value of the dependent variable if all of the independent variables are 0.
  • , known as beta, represent the coefficients applied to the independent variables. These are computed by the regression algorithm and represent the strength and direction of the relationship between the independent and dependent variables.
  • are the independent or explanatory variables used to explain or predict the dependent variable.
  • is the error term.

Now that we’ve aligned on a common understanding of the regression equation and terms, let’s shift our focus to...