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Nevertheless, Mousavi recommends this book as an "introductory text on spatial information science" aimed at practitioners, and commends its use of QR codes and word clouds. [1] Stein praises the book's attempt to bridge mathematics and geography, and its potential use as a first step towards that bridge for practitioners. [2]
In his widely cited book, Statistics for Spatial Data, [4] Cressie established a general spatial model that unified statistics for geostatistical data, regular and irregular lattice data, point patterns, and random sets, building on earlier research of his and many others on statistical theory, methodology, and applications for spatial data.
Getis published more than 100 peer-reviewed journals and book chapters during his career that have been cited over 25,000 times, giving him an h-index of 53. [5] [16] Their most influential, and highly cited, was "Analysis of spatial association by use of distance statistics" which lead to the creation of the Getis-Ord family of statistics.
Spatial statistics is a field of applied statistics dealing with spatial data. It involves stochastic processes ( random fields , point processes ), sampling , smoothing and interpolation , regional ( areal unit ) and lattice ( gridded ) data, point patterns , as well as image analysis and stereology .
Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets.Developed originally to predict probability distributions of ore grades for mining operations, [1] it is currently applied in diverse disciplines including petroleum geology, hydrogeology, hydrology, meteorology, oceanography, geochemistry, geometallurgy, geography, forestry, environmental control, landscape ...
Local spatial autocorrelation statistics provide estimates disaggregated to the level of the spatial analysis units, allowing assessment of the dependency relationships across space. G {\displaystyle G} statistics compare neighborhoods to a global average and identify local regions of strong autocorrelation.