Assessing Daily Urban Systems: A Heterogeneous Commuting Network Approach

Abstract

Daily Urban Systems (DUSs) are not only an attractive concept for planning locations for jobs, housing, schools and retail, but also for managing services such as public transportation and health care. If we can match geographically demand and supply of goods and services, higher levels of spatial efficiency can be reached. Since 50 years most of the research delineating DUSs uses thresholds of commuting levels, thus identifying labor markets polarized towards central cities. Few research grasps the more recent complex interactions within metropolitan areas due to growth and decentralization of activities. In this paper, we use techniques of complex network theory, namely community detection, on nearly 4,500,000 Belgian commuting links to define DUSs. Secondly, we explore differences for DUSs by gender and by income group. The results confirm the usefulness of community detection techniques for delineating Daily Urban Systems. Commuting patterns of females and low and very low income commuters are geographically more restricted than those of male and high and very high income commuters.

Regionalization for infection control: An algorithm for delineating containment zones considering the regularity of human mobility

“…Last but not least, although most people move regularly, different demographic groups may have different movement behaviors. For example, the movements of women are usually shorter than those of men ( Verhetsel, Beckers, & De Meyere, 2018 ), and low-income people seldom engage in long-distance travel due to the unaffordability of travel fees or costs ( Casado-Díaz, 2000 ). Previous studies have shown that different social-demographic groups have functional regions of different sizes ( Farmer & Fotheringham, 2011 ).…”

Containment and Connectivity in Dutch Urban Systems: A Network‐Analytical Operationalisation of the Three‐Systems Model

“…These stabilisations are then represented as regions within which interactions are most cohesive (e.g. Verhetsel et al 2018). However, this method makes it difficult to establish causes of this cohesiveness.…”

Belgian urban regions

“…La forte dispersion en Belgique de la population et de l'emploi explique ces chiffres. Pour obtenir une délimitation des zones complexes des mouvements pendulaires, une autre approche est nécessaire : non une orientation vers une agglomération centrale mais une analyse de réseaux (Verhetsel et al, 2018).…”