Exploring the impact of the social network geography on the individual's activity space using structural equation models

Abstract

In travel demand, leisure travel plays an important and often overlooked role, which can negatively impact the functioning of the overall transport system. Also, leisure travel is primarily social travel as a small share of leisure is solitary leading to an increasing interest in understanding leisure travel demand and the influence of social needs and activities on travel decisions. Studying social network geographies can help understand how the geographic distribution of social networks impacts daily mobility patterns, opening new perspectives to transport modeling. One of the main differences between social travel and other types of travel is the motivation to maintain individual social connections. This motivation directly impacts the process of choosing a leisure activity and destination, as it involves not only personal preferences for the characteristics of the location but the preference and geographical location of the alters. Therefore, people with more extensive social networks tend to have higher heterogeneity in the type of locations visited and to perform more socially motivated travel.

This paper looks to contribute to understanding how social networks impact individual mobility patterns by analyzing the impact of the geography and structure of social networks on the number of regular leisure locations and the size of the activity space of the ego. To analyze this hypothesis, we use a structural equation model that includes three latent variables, social needs, relationship strength, and mobility demand. This model shows the relation between the unobservable variables described above by analyzing the covariances of observable variables that are associated with the unobservable variables.

The model used in this paper uses the social network geography as the independent latent variable, which explains the observable variables number of contacts and area of geographical distribution. The two dependent latent variables are relationship quality and mobility patterns. The first variable explains the observable variables social capital, meeting frequency, and trust level. The second explains the number of places regularly visited and area of activity space.

The results show that the latent variable “social needs” influences the three measured variables compared to the number of contacts. Individuals with more alters tend to visit more places for leisure and to have a higher leisure activity space. However, this mobility demand does not depend on the strength of the relationship with those alters. The results show a correlation between these two variables. The latent variable “social needs” explains the number of contacts in the ego’s social network and their distribution in space. At the same time, it also significantly impacts “mobility demand”. Also, we can see that “relationship strength” does not impact mobility demand.