Geospatial Data Science with R and Python
The first part of this book provides an introduction to the foundational concepts of geospatial data science, including geographic representation, map projections, vector and raster data structures, essential software tools, data attributes, and the concept of data cubes. In this initial section, both R and Python are primarily utilized to generate textual descriptions and visual outputs.
Some of the included code, particularly in sections on data cubes, serves primarily illustrative purposes, demonstrating visualizations generated in R and Python rather than addressing core analytical content directly. Nevertheless, all code segments relevant to geospatial data analysis have been structured to be accessible and intuitive, even for readers with foundational knowledge equivalent to resources such as R for Data Science (r4ds?) and Geopandas Documentation for Python.
Detailed discussions and practical applications using R and Python for solving geospatial data science problems commence in the second part of Geospatial Data Science with R and Python. An introductory explanation of essential R and Python data structures is provided in this first section, laying the groundwork for more sophisticated and advanced techniques explored in subsequent chapters. Similarly, core geospatial concepts introduced here are revisited and expanded upon with greater complexity as the book progresses.