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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).
Geographically weighted regression (GWR) is a local version of spatial regression that generates parameters disaggregated by the spatial units of analysis. [54] This allows assessment of the spatial heterogeneity in the estimated relationships between the independent and dependent variables.
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 ...
This notion of far more small things than large ones is also called spatial heterogeneity, which has been formulated as scaling law. [22] Cartographic generalization or any mapping practices in general is essentially to retain the underlying scaling of numerous smallest, a very few largest, and some in between the smallest and largest. [ 23 ]
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 .
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]
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.
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 ]