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Spatial heterogeneity is a property generally ascribed to a landscape or to a population. It refers to the uneven distribution of various concentrations of each ...
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 ...
The possibility of spatial heterogeneity suggests that the estimated degree of autocorrelation may vary significantly across geographic space. Local spatial autocorrelation statistics provide estimates disaggregated to the level of the spatial analysis units, allowing assessment of the dependency relationships across space.
Spatial heterogeneity is the variation of an environment over space (e.g. differences between oranges and balls). Huffaker was expanding upon Gause's experiments by further introducing heterogeneity. Gause's experiments had found that predator and prey populations would become extinct regardless of initial population size.
In landscape ecology, spatial configuration describes the spatial pattern of patches in a landscape. Most traditional spatial configuration measurements take into account aspects of patches within the landscape, including patches' size, shape, density, connectivity and fractal dimension .
Most commonly the elements being measured are spatial patches of different types. Together with spatial configuration, spatial composition is a basic component of landscape heterogeneity indices. [ 1 ]
The first experiments with predation and spatial heterogeneity were conducted by G. F. Gause in the 1930s, based on the Lotka–Volterra equation, which was formulated in the mid-1920s, but no further application had been conducted. [3]
In GWR, regression coefficients (parameters) are estimated locally for each geographic location or point, allowing for the modeling of spatial heterogeneity. [6] Geographically Weighted Regression is a cornerstone of GIS and spatial analysis, and is built into ArcGIS, as a package for the R (programming language), and as a plugin for QGIS.