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Measuring Spatial Autocorrelation of Vectors
Author(s) -
Liu Yu,
Tong Daoqin,
Liu Xi
Publication year - 2015
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12069
Subject(s) - spatial analysis , autocorrelation , scalar (mathematics) , contrast (vision) , statistics , econometrics , mathematics , computer science , data mining , artificial intelligence , geometry
This article introduces measures to quantify spatial autocorrelation for vectors. In contrast to scalar variables, spatial autocorrelation for vectors involves an assessment of both direction and magnitude in space. Extending conventional approaches, measures of global and local spatial associations for vectors are proposed, and the associated statistical properties and significance testing are discussed. The new measures are applied to study the spatial association of taxi movements in the city of Shanghai. Complications due to the edge effect are also examined.