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Geocomputation with Python
ISBN/GTIN

Geocomputation with Python

BuchGebunden
Verkaufsrang63287inPsychologie
CHF228.00

Beschreibung

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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Details

ISBN/GTIN978-1-032-45891-5
ProduktartBuch
EinbandGebunden
Erscheinungsdatum14.02.2025
Auflage1. A.
ReiheChapman & Hall/CRC The Python Series
Seiten344 Seiten
SpracheEnglisch
MasseBreite 156 mm, Höhe 234 mm
IllustrationenFarb., s/w. Abb.
Artikel-Nr.52093682
KatalogBuchzentrum
Datenquelle-Nr.47573309
WarengruppePsychologie
Weitere Details

Reihe

Über den/die AutorIn

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 Poznä 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.