Applied geospatial data science with Python : take control of implementing, analyzing, and visualizing geospatial and spatial data with geopandas and more / David Silas Jordan.

Format:

Book

Author/Creator:

Jordan, David Silas, author.

Language:

English

Subjects (All):

Geographic information systems--Data processing.

Geographic information systems.

Data mining.

Python (Computer program language).

Physical Description:

1 online resource (308 pages)

Edition:

1st ed.

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, [2023]

Biography/History:

Jordan David S. : David S. Jordan has made a career out of applying spatial thinking to tough problem spaces in the domains of real estate planning, disaster response, social equity, and climate change. He currently leads distribution and geospatial data science at JPMorgan Chase & Co. In addition to leading and building out geospatial data science teams, David is a patented inventor of new geospatial analytics processes, a winner of a Special Achievement in GIS (SAG) Award from Esri, and a conference speaker on topics including banking deserts and how great businesses leverage GIS.

Summary:

Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book Description 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. What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: The Essentials of Geospatial Data Science

Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science

What is GIS?

What is data science?

Mathematics

Computer science

Industry and domain knowledge

Soft skills

What is geospatial data science?

Summary

Chapter 2: What Is Geospatial Data and Where Can I Find It?

Static and dynamic geospatial data

Geospatial file formats

Vector data

Raster data

Introducing geospatial databases and storage

PostgreSQL and PostGIS

ArcGIS geodatabase

Exploring open geospatial data assets

Human geography

Physical geography

Country- and area-specific data

Chapter 3: Working with Geographic and Projected Coordinate Systems

Technical requirements

Exploring geographic coordinate systems

Understanding GCS versions

Understanding projected coordinate systems

Common types of projected coordinate systems

Working with GCS and PCS in Python

PyProj

GeoPandas

Chapter 4: Exploring Geospatial Data Science Packages

Packages for working with geospatial data

GDAL

Shapely

Fiona

Rasterio

Packages enabling spatial analysis and modeling

PySAL

Packages for producing production-quality spatial visualizations

ipyLeaflet

Folium

geoplot

GeoViews

Datashader

Reviewing foundational data science packages

pandas

scikit-learn

Part 2: Exploratory Spatial Data Analysis

Chapter 5: Exploratory Data Visualization

The fundamentals of ESDA

Example - New York City Airbnb listings

Conducting EDA

ESDA

Chapter 6: Hypothesis Testing and Spatial Randomness.

Technical requirements

Constructing a spatial hypothesis test

Understanding spatial weights and spatial lags

Global spatial autocorrelation

Local spatial autocorrelation

Point pattern analysis

Ripley's alphabet functions

Chapter 7: Spatial Feature Engineering

Defining spatial feature engineering

Performing a bit of geospatial magic

Engineering summary spatial features

Summary spatial features using one dataset

Summary spatial features using two datasets

Engineering proximity spatial features

Proximity spatial features - NYC attractions

Part 3: Geospatial Modeling Case Studies

Chapter 8: Spatial Clustering and Regionalization

Collecting geodemographic data for modeling

Extracting data using the Census API

Cleaning the extracted data

Conducting EDA and ESDA

Developing geodemographic clusters

K-means geodemographic clustering

Agglomerative hierarchical geodemographic clustering

Spatially constrained agglomerative hierarchical geodemographic clustering

Measuring model performance

Chapter 9: Developing Spatial Regression Models

A refresher on regression models

Constructing an initial regression model

Exploring unmodeled spatial relationships

Teaching the model to think spatially

Incorporating spatial fixed effects within the model

Introduction to GWR models

Fitting a GWR model to predict nightly Airbnb prices

Introduction to Multiscale Geographically Weighted Regression

Fitting an MGWR model to predict nightly Airbnb prices

How do I choose between these models?

Chapter 10: Developing Solutions for Spatial Optimization Problems

Exploring the Location Set Covering Problem (LSCP).

Understanding the math behind the LSCP

Solving LSCPs

Exploring route-based combinatorial optimization problems

Understanding the math behind the TSP

Setting up the Google Maps API

Solving the TSP

Exploring a single-vehicle Vehicle Routing Problem (VRP)

Exploring a Capacitated Vehicle Routing Problem (CVRP)

Chapter 11: Advanced Topics in Spatial Data Science

Efficient operations with spatial indexing

Implementing R-tree indexing in GeoPandas

Introducing the H3 spatial index

Estimating unknowns with spatial interpolation

Applying Inverse Distance Weighted (IDW) interpolation

Introduction to Kriging-based interpolation

Ethical spatial data science

Example 1 - Sharpiegate

Example 2 - Human mobility: The New York Times investigative report

Example 3 - COVID-19 contact tracing

Example 4 - United States Census Bureau disclosure avoidance system

Index

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Notes:

Includes index.

Description based on print version record.

ISBN:

9781803240343

1803240342

OCLC:

1369508190