Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity
By Bin Jiang. 2014
134 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Epidemiology, Geography, Planning and Development
View Paper: https://www.semanticscholar.org/paper/8e5badf2a0e2bd9669db19999736dd132efdefc8
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Citations in the corpus
- Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, methodology): “Let us examine a working example to see how fractals are generated from tweets location data of the entire world (Jiang 2015a, Jiang & Miao 2015).”
- Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information (2016, methodology): “definition of fractals. A set or pattern is fractal if there are far more small things than large ones in it, or 13 the scaling pattern of far more small things than large ones recurs multiple times (Jiang 2015b, Jiang & Yin 2014). The new definition is in fact developed from head/tail breaks (J”
- New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings (2016, background): “Our data do not meet these conditions, which is unsurprising as spatially heterogeneous big data commonly violate the assumptions of Gaussian statistics (Jiang 2015).”