ABSTRACT

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.

Key Features:

●       Showcases the excellent data science environment in Python.

●       Provides examples for readers to replicate, adapt, extend, and improve.

●       Covers the crucial knowledge needed by geographic data scientists.

It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.

Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

part I|102 pages

Building Blocks

chapter Chapter 1|10 pages

Geographic Thinking for Data Scientists

chapter Chapter 2|22 pages

Computational Tools for Geographic Data Science

chapter Chapter 3|32 pages

Spatial Data

chapter Chapter 4|36 pages

Spatial Weights

part II|118 pages

Spatial Data Analysis

chapter Chapter 5|28 pages

Choropleth Mapping

chapter Chapter 6|24 pages

Global Spatial Autocorrelation

chapter Chapter 7|28 pages

Local Spatial Autocorrelation

chapter Chapter 8|36 pages

Point Pattern Analysis

part III|146 pages

Advanced Topics

chapter Chapter 9|28 pages

Spatial Inequality Dynamics

chapter Chapter 10|32 pages

Clustering and Regionalization

chapter Chapter 11|42 pages

Spatial Regression

chapter Chapter 12|42 pages

Spatial Feature Engineering