Modularity maximization and tree clustering: Novel ways to determine effective geographic borders

TL;DR

This paper explores new methods for determining effective geographic borders by analyzing human mobility data using modularity maximization and tree clustering techniques.

Contribution

It introduces novel applications of optimization techniques to human mobility proxies for identifying logical geographic divisions.

Findings

Effective borders can be derived from mobility data.

Modularity maximization reveals community structures in geographic regions.

Tree clustering provides hierarchical insights into territorial divisions.

Abstract

Territorial subdivisions and geographic borders are essential for understanding phenomena in sociology, political science, history, and economics. They influence the interregional flow of information and cross-border trade and affect the diffusion of innovation and technology. However, most existing administrative borders were determined by a variety of historic and political circumstances along with some degree of arbitrariness. Societies have changed drastically, and it is doubtful that currently existing borders reflect the most logical divisions. Fortunately, at this point in history we are in a position to actually measure some aspects of the geographic structure of society through human mobility. Large-scale transportation systems such as trains and airlines provide data about the number of people traveling between geographic locations, and many promising human mobility proxies are being discovered, such as cell phones, bank notes, and various online social networks. In this chapter we apply two optimization techniques to a human mobility proxy (bank note circulation) to investigate the effective geographic borders that emerge from a direct analysis of human mobility.