An Introduction to Spatial Data Science with GeoDa: Volume 2: Clustering Spatial Data
Anselin, Luc
- 出版商: CRC
- 出版日期: 2024-05-29
- 售價: $3,510
- 貴賓價: 9.5 折 $3,335
- 語言: 英文
- 頁數: 210
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 103271302X
- ISBN-13: 9781032713021
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相關分類:
Data Science
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商品描述
This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning.
The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user's guide for these methods as implemented in the GeoDa open source software for spatial analysis.
It is organized into three major parts, dealing with dimension reduction (principal components, multidimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties.
The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.
商品描述(中文翻譯)
這本書是一系列兩冊的第二冊,介紹了空間數據科學領域。它超越了純粹的數據探索,將觀察結果組織成有意義的群組,即空間聚類。這是所謂的無監督學習的重要組成部分,也是現代機器學習的一個主要方面。
這本書的獨特之處在於通過連接的地圖和圖表探索將經典的聚類方法空間化的方式,以及在聚類算法中明確引入空間鄰近性約束。通過大量的真實世界實證示例,讀者將瞭解主要概念和技術以及它們的相對優缺點。該書還是GeoDa開源軟件空間分析方法的權威用戶指南。
該書分為三個主要部分,涉及降維(主成分分析、多維尺度分析、隨機網絡嵌入)、經典聚類方法(階層聚類、k-means、k-medians、k-medoids和譜聚類)以及空間約束聚類方法(包括階層和分區)。最後對空間和非空間聚類特性進行評估。
本書適合對地理數據的簡單映射不滿足,希望瞭解表達在空間觀察群集中的有趣模式的讀者。假設讀者對第一冊的內容熟悉,特別是對局部空間自相關性分析和全面的可視化方法的分析。
作者簡介
Luc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also Stein-Freiler Distinguished Service Professor of Sociology and the College, as well as a member of the Committee on Data Science. He is the creator of the GeoDa software and an active contributor to the PySAL Python open source software library for spatial analysis. He has written widely on topics dealing with the methodology of spatial data analysis, including his classic 1988 text on Spatial Econometrics. His work has been recognized by many awards, such as his election to the U.S. National Academy of Science and the American Academy of Arts and Science.
作者簡介(中文翻譯)
Luc Anselin是芝加哥大學空間數據科學中心的創始主任,同時也是芝加哥大學社會學和學院的Stein-Freiler傑出服務教授,以及數據科學委員會的成員。他是GeoDa軟體的創作者,也是PySAL Python開源軟體庫的積極貢獻者,用於空間分析。他廣泛撰寫有關空間數據分析方法論的文章,包括他1988年的經典著作《空間計量經濟學》。他的工作獲得了許多獎項的認可,例如當選為美國國家科學院和美國藝術與科學院的成員。