The time geography of segregation during working hours

Abstract

While segregation is usually evaluated at the residential level, the recent influx of large streams of data describing urbanites' movement across the city allows to generate detailed descriptions of spatio-temporal segregation patterns across the activity space of individuals. For instance, segregation across the activity space is usually thought to be lower compared with residential segregation given the importance of social complementarity, among other factors, shaping the economies of cities. However, these new dynamic approaches to segregation convey important methodological challenges. This paper proposes a methodological framework to investigate segregation during working hours. Our approach combines three well-known mathematical tools: community detection algorithms, segregation metrics and random walk analysis. Using Santiago (Chile) as our model system, we build a detailed home-work commuting network from a large dataset of mobile phone pings and spatially partition the city into several communities. We then evaluate the probability that two persons at their work location will come from the same community. Finally, a randomization analysis of commuting distances and angles corroborates the strong segregation description for Santiago provided by the sociological literature. While our findings highlights the benefit of developing new approaches to understand dynamic processes in the urban environment, unveiling counterintuitive patterns such as segregation at our workplace also shows a specific example in which the exposure dimension of segregation is successfully studied using the growingly available streams of highly detailed anonymized mobile phone registries.

Keywords: community detection; network analysis; segregation; urban dynamics.

Figure 1.

Santiago, Chile. (a) Spatial distribution of socioeconomic level. (b) Spatial distribution of detected communities using Louvain’s algorithm. Arrows represent the mode of angles in H-W commuting trajectories for each community. The length and angle (theta) of arrows are proportional to the distance and direction of H-W commuting, and (c) socioeconomic level composition of each detected community.

Figure 2.

Probability distribution of commuting distance among individuals affiliated to each of the six communities detected in Santiago, Chile.

Figure 3.

Probability distribution of H-W commuting angles across individuals affiliated to each of the six communities detected in Santiago, Chile. Colour bar shows the probability density estimation in the direction angles (theta) of the polar plot.

Figure 4.

Isolation index value of detected communities. Real isolation index (RII) values are shown as red lines, while values obtained from simulations (SII) are depicted by black boxplots. Blue lines show isolation index values in the ‘well-mixed limit’, as explained in §2.