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There exist two main types of spatial heterogeneity. The spatial local heterogeneity categorises the geographic phenomena whose its attributes' values are significantly similar within a directly local neighbourhood, but which significantly differ in the nearby surrounding-areas beyond this directly local neighbourhood (e.g. hot spots, cold spots).
Hybrid zones can form from secondary contact. A hybrid zone exists where the ranges of two interbreeding species or diverged intraspecific lineages meet and cross-fertilize. . Hybrid zones can form in situ due to the evolution of a new lineage [1] [page needed] but generally they result from secondary contact of the parental forms after a period of geographic isolation, which allowed their ...
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 .
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 composition describes the content of a landscape in terms of the number of different categories of elements existing in the landscape and their proportions. Most commonly the elements being measured are spatial patches of different types.
Although the term metapopulation had not yet been coined, the environmental factors of spatial heterogeneity and habitat patchiness would later describe the conditions of a metapopulation relating to how groups of spatially separated populations of species interact with one another. Huffaker's experiment is significant because it showed how ...
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 .
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 ...