Applied Geospatial Data Science with Python: Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

Length: 308 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2023-02-28
ISBN-10: 1803238127
ISBN-13: 9781803238128
Sales Rank: #893574 (See Top 100 Books)

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.

Applied Geospatial Data Science with Python
Acknowledgments
Contributors
About the author
About the reviewer
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
    Download a free PDF copy of this book
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
    Summary
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
    Summary
Chapter 4: Exploring Geospatial Data Science Packages
    Technical requirements
    Packages for working with geospatial data
        GeoPandas
        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
    Summary
Part 2: Exploratory Spatial Data Analysis
Chapter 5: Exploratory Data Visualization
    Technical requirements
    The fundamentals of ESDA
        Example – New York City Airbnb listings
    Conducting EDA
        ESDA
    Summary
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
    Summary
Chapter 7: Spatial Feature Engineering
    Technical requirements
    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
    Summary
Part 3: Geospatial Modeling Case Studies
Chapter 8: Spatial Clustering and Regionalization
    Technical requirements
    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
    Summary
Chapter 9: Developing Spatial Regression Models
    Technical requirements
    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?
    Summary
Chapter 10: Developing Solutions for Spatial Optimization Problems
    Technical requirements
    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)
    Summary
Chapter 11: Advanced Topics in Spatial Data Science
    Technical requirements
    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
    Summary
Index
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