http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
APPLIED GEOSPATIAL DATA SCIENCE WITH PYTHON
leverage geospatial data analysis and modeling to find unique solutions to environmental problems
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.
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 -- 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