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Waldo Tobler in front of the Newberry Library. Chicago, November 2007. The First Law of Geography, according to Waldo Tobler, is "everything is related to everything else, but near things are more related than distant things." [1] This first law is the foundation of the fundamental concepts of spatial dependence and spatial autocorrelation and is utilized specifically for the inverse distance ...
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.
MAUP can be used as an analytical tool to help understand spatial heterogeneity and spatial autocorrelation. This topic is of particular importance because in some cases data aggregation can obscure a strong correlation between variables, making the relationship appear weak or even negative. Conversely, MAUP can cause random variables to appear ...
GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling. It runs on different versions of Windows, Mac OS, and Linux. The package was initially developed by the Spatial Analysis Laboratory of the University of Illinois at Urbana-Champaign under the direction of Luc ...
Geary's C is a measure of spatial autocorrelation that attempts to determine if observations of the same variable are spatially autocorrelated globally (rather than at the neighborhood level). Spatial autocorrelation is more complex than autocorrelation because the correlation is multi-dimensional and bi-directional.
In spatial analysis, four major problems interfere with an accurate estimation of the statistical parameter: the boundary problem, scale problem, pattern problem (or spatial autocorrelation), and modifiable areal unit problem. [1] The boundary problem occurs because of the loss of neighbours in analyses that depend on the values of the neighbours.
[1] [2] Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional (i.e. 2 or 3 dimensions of space) and multi-directional.
This concept has been formalized as spatial dependence or spatial autocorrelation, which underlies the method of geostatistics. [16] A parallel concept that has received less publicity, but has underlain geographic theory since at least Alexander von Humboldt is spatial association, which describes how phenomena are similarly distributed. [17]