Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity
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- 作者：Bin Jiang
- 关键词：Big data ; Scaling of geographic space ; Head/tail breaks ; Power laws ; Heavy ; tailed distributions
- 刊名：GeoJournal
- 出版年：2015
- 出版时间：February 2015
- 年：2015
- 卷：80
- 期：1
- 页码：1-13
- 全文大小：1,790 KB
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- 刊物主题：Geography
Geography
Environmental Management
Hydrogeology
Ecology
Methodology of the Social Sciences- 出版者：Springer Netherlands
- ISSN：1572-9893
文摘
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.