Spatial Heterogeneity, Scale, Data Character, and Sustainable Transport in the Big Data Era

TL;DR

This paper advocates a paradigm shift in spatial analysis and transportation modeling, emphasizing fractal geometry and scaling laws enabled by big data to better understand and develop sustainable transport systems.

Contribution

It introduces a new conceptual framework replacing traditional Euclidean and Gaussian models with fractal and scaling law approaches for spatial heterogeneity and scale in the big data era.

Findings

Fractal geometry better captures spatial heterogeneity than Euclidean geometry.

Scaling laws reveal the importance of small-scale patterns in transportation data.

Big data enables more accurate modeling of complex, living spatial structures.

Abstract

In light of the emergence of big data, I have advocated and argued for a paradigm shift from Tobler's law to scaling law, from Euclidean geometry to fractal geometry, from Gaussian statistics to Paretian statistics, and - more importantly - from Descartes' mechanistic thinking to Alexander's organic thinking. Fractal geometry falls under the third definition of fractal - that is, a set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times (Jiang and Yin 2014) - rather than under the second definition of fractal, which requires a power law between scales and details (Mandelbrot 1982). The new fractal geometry is more towards living geometry that "follows the rules, constraints, and contingent conditions that are, inevitably, encountered in the real world" (Alexander et al. 2012, p. 395), not only for understanding complexity, but also for creating complex or living structure (Alexander 2002-2005). This editorial attempts to clarify why the paradigm shift is essential and to elaborate on several concepts, including spatial heterogeneity (scaling law), scale (or the fourth meaning of scale), data character (in contrast to data quality), and sustainable transport in the big data era.