Geocomputation with Python: Chapman & Hall/CRC The Python Series
Autor Michael Dorman, Anita Graser, Jakub Nowosad, Robin Lovelaceen Limba Engleză Paperback – 14 feb 2025
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.
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.
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Specificații
ISBN-13: 9781032460659
ISBN-10: 1032460652
Pagini: 344
Ilustrații: 292
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC The Python Series
ISBN-10: 1032460652
Pagini: 344
Ilustrații: 292
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC The Python Series
Public țintă
Postgraduate and Undergraduate AdvancedCuprins
Preface 1. Geographic data in Python 2. Attribute data operations 3. Spatial data operations 4. Geometry operations 5. Raster-vector interactions 6. Reprojecting geographic data 7. Geographic data I/O 8. Making maps with Python References
Notă biografică
Michael Dorman, Ph.D. is a programmer and lecturer at The Department of Environmental, Geoinformatics and Urban Planning Sciences, Ben-Gurion University of the Negev. He is working with researchers and students to develop computational workflows for spatial analysis, mostly through programming in Python, R, and JavaScript, as well as teaching those subjects.
Anita Graser, Ph.D. is a Senior Scientist at the Austrian Institute of Technology (AIT), QGIS PSC member and lead developer of MovingPandas. Anita has published several books about QGIS, including “Learning QGIS” and “QGIS Map Design”, teaches Python for QGIS, and writes a popular spatial data science blog.
Jakub Nowosad, Ph.D. is an Associate Professor at Adam Mickiewicz University in Poznań and a visiting scientist at the University of Münster. Specializing in spatial pattern analysis in environmental studies, he combines research with a dedication to education and open science principles. Dr. Nowosad is committed to developing scientific software and fostering accessible knowledge through teaching and open-source contributions.
Robin Lovelace, Ph.D. is a Professor of Transport Data Science at the University of Leeds., He is the developer of high impact applications for more data-driven transport planning and policy. He has a decade’s experience researching and teaching data science with geographic data and has developed numerous tools to support more data-driven policies, including the award-winning Propensity to Cycle Tool which has transformed the practice of strategic active travel network planning in the UK.
Anita Graser, Ph.D. is a Senior Scientist at the Austrian Institute of Technology (AIT), QGIS PSC member and lead developer of MovingPandas. Anita has published several books about QGIS, including “Learning QGIS” and “QGIS Map Design”, teaches Python for QGIS, and writes a popular spatial data science blog.
Jakub Nowosad, Ph.D. is an Associate Professor at Adam Mickiewicz University in Poznań and a visiting scientist at the University of Münster. Specializing in spatial pattern analysis in environmental studies, he combines research with a dedication to education and open science principles. Dr. Nowosad is committed to developing scientific software and fostering accessible knowledge through teaching and open-source contributions.
Robin Lovelace, Ph.D. is a Professor of Transport Data Science at the University of Leeds., He is the developer of high impact applications for more data-driven transport planning and policy. He has a decade’s experience researching and teaching data science with geographic data and has developed numerous tools to support more data-driven policies, including the award-winning Propensity to Cycle Tool which has transformed the practice of strategic active travel network planning in the UK.
Descriere
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 many examples covering a range of GIS operations.