Geographic Space as a Living Structure for Predicting Human Activities Using Big Data
Bin Jiang, Zheng Ren

TL;DR
This paper develops a topological, multi-scale representation of geographic space inspired by Christopher Alexander's living structure concept, demonstrating its effectiveness in predicting human activities like tweet locations across scales.
Contribution
It advances the topological representation of geographic space as a multi-scale, living structure, improving prediction of human activities and addressing scale-related issues in traditional geographic models.
Findings
Living structures predict tweet locations effectively across scales.
Topological representation outperforms traditional single-scale models.
Living structure offers a new perspective on geographic space as a multi-scale, organic entity.
Abstract
Inspired by Christopher Alexanders conception of the world - space is not lifeless or neutral but a living structure involving far more small things than large ones a topological representation has been previously developed to characterize the living structure or the wholeness of geographic space. This paper further develops the topological representation and living structure for predicting human activities in geographic space. Based on millions of street nodes of the United Kingdom extracted from OpenStreetMap, we established living structures at different levels of scale in a nested manner. We found that tweet locations at different levels of scale, such as country and city, can be well predicted by the underlying living structure. The high predictability demonstrates that the living structure and the topological representation are efficient and effective for better understanding…
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