000			04574cam a2200361 i 4500
001			23524934
003			OSt
005			20250128154537.0
008			240119s2024 flub b 001 0 eng
010			_a 2023048617
020			_a9781032713021 (hbk.)
040			_cJKRC
082	0	0	_a519.53 _223 _bANS.L
100	1		_aAnselin, Luc, _d1953- _eauthor. _930209
245	1	3	_aAn introduction to spatial data science with GeoDa / _cby Luc Anselin. _hEnglish _bclustering spatial data Vol-002
250			_a1st ed.
260			_aBoca Raton, FL : _bCRC Press, Taylor & Francis Group, _c2024.
300			_axix, 217 p. _bmaps ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aVolume 1. Exploring spatial data -- Volume 2. Clustering spatial data.
520			_a"This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration, to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods, by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa"-- _cProvided by publisher.
520			_a"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, multi-dimensional 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"-- _cProvided by publisher.
630	0	0	_aGeoDa (Computer file) _930210
650		0	_aSpatial analysis (Statistics)
650		0	_aSpatial analysis (Statistics) _xData processing. _930211
653			_aSpatial data science _aIntroduction to spatial data science with geoda
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _c1 _e23 _n0
999			_c428643 _d428643