Neuware -This book is structured to encompass both the foundational and specialized aspects of quantitative analysis in geography. The basic content covers descriptive statistical analysis and correlation analysis of geographical data, while the professional content delves into more advanced topics like linear regression analysis, geographically weighted regression analysis, time series analysis, cluster analysis, principal component analysis, Markov chain analysis, and geographical network analysis. The methodologies span from widely utilized techniques to more recent developments, and the data primarily originates from reputable sources in China. The example code provided in the book can be executed using R packages available on the CRAN website.This book is an invaluable resource for undergraduate and graduate students, as well as researchers interested in learning and applying R for processing, visualizing, and analyzing geographic data. It serves as an introductory course in quantitative methods in geography for students in geography departments. Additionally, it is an ideal supplementary text for applied methods courses across various disciplines that involve geographic data, such as human and physical geography, geographic information science, ecology, public health, crime, and economics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 248 pp. Englisch.

Bestandsnummer des Verk�ufers 9789819740215

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Bibliografische Details

Titel: Geographic Data Analysis Using R
Verlag: Springer Nature Singapore, Springer Nature Singapore Aug 2024
Erscheinungsjahr: 2024
Sprache: Englisch
ISBN-10: 9819740215
ISBN-13: 9789819740215
Einband: Buch
Zustand: Neu

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This book is structured to encompass both the foundational and specialized aspects of quantitative analysis in geography. The basic content covers descriptive statistical analysis and correlation analysis of geographical data, while the professional content delves into more advanced topics like linear regression analysis, geographically weighted regression analysis, time series analysis, cluster analysis, principal component analysis, Markov chain analysis, and geographical network analysis. The methodologies span from widely utilized techniques to more recent developments, and the data primarily originates from reputable sources in China. The example code provided in the book can be executed using R packages available on the CRAN website.

This book is an invaluable resource for undergraduate and graduate students, as well as researchers interested in learning and applying R for processing, visualizing, and analyzing geographic data. It serves as an introductory course in quantitative methods in geography for students in geography departments. Additionally, it is an ideal supplementary text for applied methods courses across various disciplines that involve geographic data, such as human and physical geography, geographic information science, ecology, public health, crime, and economics.

�ber die Autorin bzw. den Autor

He Xindong, Associate Professor at the College of Geography and Planning, Chengdu University of Technology in China, has dedicated the past decade to teaching quantitative geography and GIS. His research expertise encompasses land use and change simulation analysis, applied spatial statistics, and spatial analysis in the realms of regional sustainability, spatial planning, and regional development. Over the last ten years, his focus has primarily been on geographic data and geocomputing in public policy- making support.

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