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
- 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
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 Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Download a free PDF copy of this book
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