Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures

• Main Authors: G.-Fivos Sargentis, Theano Iliopoulou, Stavroula Sigourou, Panayiotis Dimitriadis, Demetris Koutsoyiannis
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Sustainability
• Subjects: clustering evolution; natural clustering; social clustering; spatiotemporal clustering; scale development; stochastic analysis
• Online Access: https://www.mdpi.com/2071-1050/12/19/7972

Clustering structures appearing from small to large scales are ubiquitous in the physical world. Interestingly, clustering structures are omnipresent in human history too, ranging from the mere organization of life in societies (e.g., urbanization) to the development of large-scale infrastructure and policies for meeting organizational needs. Indeed, in its struggle for survival and progress, mankind has perpetually sought the benefits of unions. At the same time, it is acknowledged that as the scale of the projects grows, the cost of the delivered products is reduced while their quantities are maximized. Thus, large-scale infrastructures and policies are considered advantageous and are constantly being pursued at even great scales. This work develops a general method to quantify the temporal evolution of clustering, using a stochastic computational tool called 2D-C, which is applicable for the study of both natural and human social spatial structures. As case studies, the evolution of the structure of the universe, of ecosystems and of human clustering structures such as urbanization, are investigated using novel sources of spatial information. Results suggest the clear existence both of periods of clustering and declustering in the natural world and in the human social structures; yet clustering is the general trend. In view of the ongoing COVID-19 pandemic, societal challenges arising from large-scale clustering structures are discussed.