MindEarth, 2502, Biel/Bienne, Switzerland.
University of Bristol, Bristol, 06010, UK.
Sci Rep. 2021 Dec 2;11(1):23289. doi: 10.1038/s41598-021-02743-9.
Human settlements on Earth are scattered in a multitude of shapes, sizes and spatial arrangements. These patterns are often not random but a result of complex geographical, cultural, economic and historical processes that have profound human and ecological impacts. However, little is known about the global distribution of these patterns and the spatial forces that creates them. This study analyses human settlements from high-resolution satellite imagery and provides a global classification of spatial patterns. We find two emerging classes, namely agglomeration and dispersion. In the former, settlements are fewer than expected based on the predictions of scaling theory, while an unexpectedly high number of settlements characterizes the latter. To explain the observed spatial patterns, we propose a model that combines two agglomeration forces and simulates human settlements' historical growth. Our results show that our model accurately matches the observed global classification (F1: 0.73), helps to understand and estimate the growth of human settlements and, in turn, the distribution and physical dynamics of all human settlements on Earth, from small villages to cities.
地球上的人类住区分布在多种形状、大小和空间排列中。这些模式通常不是随机的,而是复杂的地理、文化、经济和历史过程的结果,对人类和生态有深远的影响。然而,人们对这些模式的全球分布以及形成这些模式的空间力量知之甚少。本研究通过高分辨率卫星图像分析人类住区,并提供了一种全球空间模式分类。我们发现了两种新兴的模式,即集聚和离散。前者的住区数量低于基于标度理论预测的数量,而后者则呈现出出人意料的多数量的住区。为了解释观察到的空间模式,我们提出了一个结合两种集聚力并模拟人类住区历史增长的模型。我们的结果表明,我们的模型准确地匹配了观察到的全球分类(F1:0.73),有助于理解和估计人类住区的增长,进而有助于理解和估计地球上所有人类住区的分布和物理动态,从小村庄到城市。