Geocomputation with Python (Chapman & Hall/CRC The Python Series)

Dorman, Michael; Graser, Anita; Nowosad, Jakub; Lovelace, Robin

ISBN 10: 1032458917 ISBN 13: 9781032458915

Editorial: Chapman and Hall/CRC, 2025

Idioma: Ingl�s

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T�tulo

Geocomputation with Python (Chapman & Hall/CRC The Python Series)

Editor

Chapman and Hall/CRC

Fecha de publicaci�n

2025

Idioma

Ingl�s

ISBN 10

1032458917

ISBN 13

9781032458915

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Encuadernaci�n de tapa dura

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New

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Sinopsis

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.

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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 M�nster. 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.

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