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Geographic Data Science with Python

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Preface

  • Table of Contents

Part I - Building Blocks

  • Overview
  • Geographic thinking for data scientists
  • Computational Tools for Geographic Data Science
  • Spatial Data
  • Spatial Weights

Part II - Spatial Data Analysis

  • Overview
  • Choropleth Mapping
  • Global Spatial Autocorrelation
  • Local Spatial Autocorrelation
  • Point Pattern Analysis

Part III - Advanced Topics

  • Overview
  • Spatial Inequality Dynamics
  • Clustering & Regionalization
  • Spatial Regression
  • Spatial Feature Engineering

Endmatter

  • References

Datasets

  • Overview
  • AirBnb
  • Airports
  • Brexit
  • Countries
  • GHSL
  • H3 Grid
  • Mexico
  • NASA DEM
  • San Diego Tracts
  • Texas
  • Tokyo Photographs
  • US County Income 1969-2017
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Contents
  • Part I: Building Blocks
  • Part II: Spatial Data Analysis
  • Part III: Advanced Topics
  • Endmatter

Table of Contents

Contents

  • Part I: Building Blocks
  • Part II: Spatial Data Analysis
  • Part III: Advanced Topics
  • Endmatter

Table of Contents¶

  • Prologue

Part I: Building Blocks¶

  • Geographic Thinking for Data Scientists

  • Geospatial Computational Environment

  • Spatial data

  • Spatial weights

Part II: Spatial Data Analysis¶

  • Choropleth Mapping

  • Spatial Autocorrelation

  • Local Spatial Autocorrelation

  • Point Pattern Analysis

Part III: Advanced Topics¶

  • Spatial Inequality

  • Clustering and Regionalization

  • Spatial Regression

  • Spatial Feature Engineering

Endmatter¶

  • References

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By Sergio J. Rey, Dani Arribas-Bel, Levi J. Wolf
© Copyright 2020.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.