Isbn: 9781032713021 - an introduction to spatial data science with geoda: volume 2: clustering spatial data (23 resultados)

Condición: Usado.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. In English.

Condición: New. 2024. 1st Edition. hardcover. . . . . .

Hardback. Condición: New. 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.…

Condición: New. 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 Sci.

Condición: New. 2024. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condición: Brand New. 240 pages. 10.00x7.00x10.00 inches. In Stock.

Hardback. Condición: New. 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.…

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. PRINT ON DEMAND.

Hardcover. Condición: Brand New. 240 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand.

Buch. Condición: Neu. An Introduction to Spatial Data Science with GeoDa | Volume 2: Clustering Spatial Data | Luc Anselin | Buch | Einband - fest (Hardcover) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9781032713021 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 a major aspect of modern machine learning.…