Название: Geocomputation with PythonАвтор: Michael Dorman, Anita Graser, Jakub Nowosad, Robin Lovelace
Издательство: CRC Press
Серия: The Python Series
Год: 2025
Страниц: 309
Язык: английский
Формат: pdf (true), epub
Размер: 37.4 MB
Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.
Key features:
Showcases the integration of vector and raster datasets operations.
Provides explanation of each line of code in the book to minimize surprises.
Includes example datasets and meaningful operations to illustrate the applied nature of geographic research.
Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community.
The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for Data Science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, Machine Learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.
Prerequisites:
We assume that the reader is:
• familiar with the Python language,
• is capable of running Python code and install Python packages, and
• is familiar with the numpy and pandas packages for working with data in Python.
From that starting point on, the book introduces the topic of working with spatial data in Python, through dedicated third-party packages—most importantly geopandas and rasterio.
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