ArticleScientific reports2021
The agglomeration and dispersion dichotomy of human settlements on Earth.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis.Scientific data · 2025Article
- Gardens reduce seasonal hunger gaps for farmland pollinators.Proceedings. Biological sciences · 2024Article
- Article
- Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.International journal of applied earth observation and geoinformation : ITC journal · 2023Article
- A new method for identifying industrial clustering using the standard deviational ellipse.Scientific reports · 2023Article
- Crime, inequality and public health: a survey of emerging trends in urban data science.Frontiers in big data · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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