  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. However, this assumption
is not always valid. In fact, many things in the world lack a well-defined
mean, and therefore there are far more small things than large ones. 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. 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. I subsequently argue for a broad perspective on
spatial heterogeneity, and suggest it be formulated as a scaling law. I further
discuss the implications of Paretian thinking and the scaling law for better
understanding of geographic forms and processes, in particular while facing
massive amounts of social media data. In the spirit of Paretian thinking,
geospatial analysis should seek to simulate geographic events and phenomena
from the bottom up rather than correlations as guided by Gaussian thinking.
