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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.
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.
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 ...
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]
The Spectral Variability Hypothesis (SVH) states that spatial variability in the reflectance of vegetated surfaces relates to plant species richness.It has been originally coined by Palmer et al. (2000) and states that "species richness will be positively related to any objective measure (e.g. standard deviation) of the variation in the spectral characteristics of a remotely sensed image". [1]
Spatial variability can be assessed using spatial descriptive statistics such as the range. Let us suppose that the Rev' z(x) is perfectly known at any point x within the field under study. Then the uncertainty about z(x) is reduced to zero, whereas its spatial variability still exists. Uncertainty is closely related to the amount of spatial ...
More precisely, each image represents two spatial dimensions, one of which is wavelength-coded. To acquire the spectrum of a given object point, scanning is needed. Spatio-spectral scanning combines some advantages of spatial and spectral scanning: Depending on the context of application, one can choose between a mobile and a stationary platform.