Beyond Activity Space: Detecting Communities in Ecological Networks

Abstract

Emerging research suggests that the extent to which activity spaces -the collection of an individual's routine activity locations -overlap provides important information about the functioning of a city and its neighborhoods. To study patterns of overlapping activity spaces, we draw on the notion of an ecological network, a type of two-mode network with the two modes being individuals and the geographic locations where individuals perform routine activities. We describe a method for detecting "ecological communities" within these networks based on shared activity locations among individuals. Specifically, we identify latent activity pattern profiles, which, for each community, summarize its members' probability distribution of going to each location, and community assignment vectors, which, for each individual, summarize his/her probability distribution of belonging to each community. Using data from the Adolescent Health and Development in Context (AHDC) Study, we employ latent Dirichlet allocation (LDA) to identify activity pattern profiles and communities. We then explore differences across neighborhoods in the strength, and within-neighborhood consistency of community assignment.We hypothesize that these aspects of the neighborhood structure of ecological community membership capture meaningful dimensions of neighborhood functioning likely to co-vary with economic and racial composition. We discuss the implications of a focus on ecological communities for the conduct of "neighborhood effects" research more broadly.

Neighborhoods, Activity Spaces, and the Span of Adolescent Exposures

“…A focus on the aggregate pattern of shared activity space locations also highlights the potential relevance of contextual units rooted in overlapping routines rather than traditional neighborhood boundaries. For instance, clusters of people that intersect at higher rates within an ecological network—or ecological communities (Xi, Calder, and Browning 2020) — may independently influence youth outcomes (Browning and Soller 2014). In this sense, a focus on systematic, aggregate patterns of mobility yields potentially new insight into previously neglected structural sources of contextual influence.…”

Analysis of Spatial Networks From Bipartite Projections Using the R Backbone Package

“…Bipartite projections offer a way to indirectly measure spatial networks using data that is often relatively easy to obtain. For this reason, bipartite projections are now among the most common ways to measure the world city network (Taylor & Derudder, 2016), and are frequently used to measure other spatial networks at the global (e.g., Hafner-Burton et al, 2009;Heemskerk et al, 2016;Straka et al, 2017) and local (Browning et al, 2017;Xi et al, 2020) scales, as well as to study the structure of geography as a discipline (Peris et al, 2018). It is often helpful to focus on the backbone of bipartite projections, which preserve only the most important connections between nodes.…”

“…However, it is common in sociological research, where the focus is on social networks emerging from spatial interactions. For example, Browning et al (2017) and Xi, Calder, and Browning (2020) use this approach to measure and study the social network among households in Los Angeles: households (the agents) are connected to the extent that they visit the same routine activity locations (e.g., school, work; the artifacts). This rests on the logic that places offer opportunities for casual encounters which lead to the formation of social bonds, and therefore when two households frequent the same places, they are more likely to interact with each other (Jacobs, 1961).…”

Analysis of spatial networks from bipartite projections using the R backbone package

“…Spatial networks are difficult to measure, but bipartite projections offer a way to indirectly measure them using data that is easier to obtain. For this reason, bipartite projections are now among the most common ways to measure the world city network (Taylor & Derudder, 2016), and are frequently used to measure other spatial networks at the global (e.g., Hafner-Burton et al, 2009;Heemskerk et al, 2016;Straka et al, 2017) and local (Browning et al, 2017;Xi et al, 2020) scales, as well as to study the structure of geography as a discipline (Peris et al, 2018). It is often helpful to focus on the backbone of bipartite projections, which preserve only the most important connections between nodes.…”

“…However, it is common in sociological research, where the focus is on social networks emerging from spatial interactions. For example, Browning et al (2017) and Xi, Calder, and Browning (2020) use this approach to measure and study the social network among households in Los Angeles: households (the agents) are connected to the extent that they visit the same nonhome routine activity locations (e.g., school, work; the artifacts). This rests on the logic that places offer opportunities for causal encounters that lead to the formation of social bonds, and therefore that when two households frequent the same places, they are more likely to interact with each other (Jacobs, 1961).…”