I subsequently argue for a broad perspective on spatial heterogeneity, and suggest it be formulated as a scaling law. I review two properties of spatial dependence and spatial heterogeneity, and point out that the notion of spatial heterogeneity in current spatial statistics is only used to characterize local variance of spatial dependence. This paper attempts to argue that geospatial analysis requires a different way of thinking - a Paretian way of thinking that underlies skewed distribution such as power laws, Pareto and lognormal distributions. In fact, many things in the world lack a well-defined mean, and therefore there are far more small things than large ones. However, this assumption is not always valid. Geospatial analysis is very much dominated by a Gaussian way of thinking, which assumes that things in the world can be characterized by a well-defined mean, i.e., things are more or less similar in size.
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