Spatial Patterns
Spatial patterns refer to the arrangement of data points in space. In data science, spatial patterns are analyzed using various techniques such as spatial autocorrelation, spatial interpolation, and spatial clustering. Spatial autocorrelation measures the degree to which data points near each other are similar, while spatial interpolation is used to estimate values at unsampled locations based on the values of nearby sampled locations. Spatial clustering identifies groups of data points that are close to each other in space and have similar characteristics. Spatial patterns are commonly used in fields such as geography, ecology, and epidemiology to understand the distribution of phenomena across space and to identify potential relationships between variables.
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