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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).
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.
A major problem already noted by Palmer (2002) is the fact that different habitats support different species numbers so the relationship between habitat heterogeneity and species numbers differs depending on which habitats are involved. [2] This means that spatial heterogeneity in reflectance does not show a generalizable link to species richness.
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 was first proposed as "a possible candidate" of the second law of geography by Michael F. Goodchild, who attributes this law to David Harvey. [5] Chinese geographers often cite it simply as the "second law of geography".
A landscape with structure and pattern implies that it has spatial heterogeneity, or the uneven distribution of objects across the landscape. [6] Heterogeneity is a key element of landscape ecology that separates this discipline from other branches of ecology. Landscape heterogeneity is able to quantify with agent-based methods as well. [37]
Mei-Po Kwan, a prominent scholar in human geography, highlighted the importance of accounting for spatial processes and interactions within neighborhoods in a 2018 paper. [2] She argued that the analysis's neighborhood effect averaging problem arises from disregarding spatial dependence and spatial heterogeneity , and is credited with the ...
Openshaw (1993) and Hewitson et al. (1994) started investigating the applications of the a-spatial/classic NNs to geographic phenomena. [4] [5] They observed that a-spatial/classic NNs outperform the other extensively applied a-spatial/classic statistical models (e.g. regression models, clustering algorithms, maximum likelihood classifications) in geography, especially when there exist non ...