Geographic Data Analysis Using R

2024
ISBN: 978-981-97-4021-5
Verlag: Springer Nature Singapore

Buch, Englisch, 225 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 586 g

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. 

Zielgruppe

Research

Autoren/Hrsg.

He, Xindong

Fachgebiete

• Geowissenschaften Geographie | Raumplanung Geographie: Allgemeines, Karten & Atlanten
• Geowissenschaften Geologie GIS, Geoinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung

Inhaltsverzeichnis

Introduction to Geographic Data and R.- Descriptive Analysis of Geographic Data.- Correlation Analysis.- Linear Regression Analysis.- Geographically Weighted Regression Analysis.- Time Series Analysis.- Cluster Analysis.- Principal Component Analysis (PCA).- Markov Chain Analysis.- Geographic Network Analysis.- Spatial Interpolation.

Über Autor(innen)

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.