, Volume 80, Issue 1, pp 1–13 | Cite as

Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity

  • Bin Jiang


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.


Big data Scaling of geographic space Head/tail breaks Power laws Heavy-tailed distributions 



The author would like to thank the anonymous referees and the editor Daniel Z. Sui for their valuable comments. However, any shortcoming remains the responsibility of the author.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Division of Geomatics, Department of Technology and Built EnvironmentUniversity of GävleGävleSweden

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