Pew Pauline van den Berg

Size and Composition of Ego-Centered Social Networks and Their Effect on Geographic Distance and Contact Frequency

Social activities are responsible for a substantial proportion of trips by individuals and households. Therefore, travel demand is determined primarily by the size and the spatial distribution of peoples social networks. In the field of transportation, little is known about social networks in relation to trip generation. However, research interest in social networks is rapidly emerging. This paper describes the instrument used to collect data on ego-centered social networks in a survey in the Netherlands and the analysis of these data in relation to travel demand. Five successive regression models were used to analyze and predict the size of the egos social network, distribution of network members across seven social categories, geographic distance, and contact frequency (by means of information and communication technology and face-to-face) with each social network member based on socio-demographic characteristics of the ego. The results indicate that, even though significant relationships exist, the relationships between socio-demographic variables and social network size, the choice of social category, geographic distance, and (mediated) contact frequency are not strong. A better model fit is found for predicting face-to-face contact frequency.

Modeling social networks in geographic space: approach and empirical application

Social activities are responsible for a large proportion of travel demands of individuals. Modeling of the social network of a studied population offers a basis to predict social travel in a more comprehensive way than currently is possible. In this paper we develop a method to generate a whole social network for a given population focusing on friendship relationships. The core of the method is a friendship-formation model that predicts for any two given persons from a population the probability that a friendship relationship exists between the persons. The model takes into account the degree of similarity in attributes, geographic distance between the persons as well as threshold values representing the persons opportunities and base preferences for engagement in a friendship relationship. We show how the model can be estimated on the basis of observed personal social networks from a sample of individuals. We estimate and test the model using data collected in a recent survey in Eindhoven, The Netherlands. The results indicate that the model is able to generate networks that display the same structural properties as we find in the sample data. A synthetic network generated in this way can be used to microsimulate social interactions in a population in geographic space

A multilevel path analysis of contact frequency between social network members

Recently, there has been an increasing interest in the role of social networks in spatial-choice and travel behavior. It has been acknowledged that social activities and the travel for these activities can emerge from individuals’ social networks and that social activities are responsible for an important portion of travel demand. The influence of information and communication technologies (ICT’s) is also important in this respect. The purpose of the paper is to examine the effects of characteristics of egos and ego-alter relationships on the frequency of social interaction by different communication modes, using multilevel path analysis. The analyses are based on social network data collected in 2008 in the Eindhoven region in the Netherlands among 116 respondents. The results indicate a complementary relationship between contact frequencies by different modes. The contact frequencies of the different modes, especially face-to-face and telephone, can also be largely explained by the ego’s personal characteristics and the type of relationship and the distance between ego and alter.

New ICTs and social interaction: Modelling communication frequency and communication mode choice:

New information and communication technologies (ICTs) have provided new ways of communicating and maintaining social networks. However, relatively little is known on the effect of ICT-use on social interaction. Therefore, this article aims to explore the factors influencing individuals’ communication frequency and choice of communication mode. The analyses are based on social interaction diary data gathered in 2008 in the Eindhoven region in the Netherlands among 747 respondents. Using these data two models are estimated analysing the number of social interactions in two days and the choice of a communication mode used for the social interaction. Many significant effects of personal and household characteristics were found. In addition, the results for communication mode choice show the importance of including characteristics of the contacted person(s) to explain communication mode choice. The findings allow us to reconstruct the generation of social activities and the relationship between face-to-face and ICT-mediated communication.

Location-type choice for face-to-face social activities and its effect on travel behavior

Face-to-face social activities, such as joint activities with friends or visiting relatives, involve meeting with other persons at a certain time and location. This (social) spatial-choice behavior is critical for successful urban planning. Recently there has been a rapid emergence of research into social interactions in the field of urban planning and transport. This study contributes to our understanding of the relationships between face-to-face social interaction, location-type choice, and travel by presenting analyses based on social-contact diary data gathered for this study in a large-scale survey conducted in the Netherlands. The analysis framework consists of a set of linked linear regression and discrete choice models to predict the location-type choice for a given face-to-face social interaction, the distance to that location, and the transport mode. Furthermore, the model predicts outcomes of a chain of higher-level decisions including the number of face-to-face interactions and the purpose of the interaction. As independent variables, the model system includes sociodemographic and spatial variables. The results indicate that, to a moderate extent the number of face-to-face social interactions, the purpose of the interactions, and the travel distance can be explained by peoples personal and residential variables. These variables have a strong impact on the type of location for the interaction and the transport mode used to get there.

Social interaction location choice : a latent class modelling approach

Social contacts are an important aspect of an individuals quality of life. Social contacts take place at a certain time and location: Geography matters, for instance, at home or a work location or at different types of (local) facilities such as schools, shops, sports, and catering facilities. For urban planners, it is essential to know which locations provide opportunities for social interaction. As this knowledge is currently largely lacking, more empirical research is needed. The purpose of this study, therefore, is to analyze the diversity of participation in social activities at different locations and the relationship among social interaction location, sociodemographic characteristics, and characteristics of the residential environment. The analyses are based on two-day social interaction diary data that were collected in 2008 among 747 respondents living in the Eindhoven region in The Netherlands. A latent class multinomial logit model is used to segment respondents in terms of their social activity location choices. The article reports findings of several descriptive analyses and the latent class model. Four latent classes are identified, showing different patterns in choices for social activity locations. Latent class membership can be explained by household and personal time-use characteristics (e.g., gender, age, household type, number of face-to-face social interactions, frequency of contact with neighbors), as well as characteristics of the residential environment (e.g., urban density, distance to several facilities, and satisfaction with local facilities). The findings could provide useful information for local governments and planners regarding the importance of public facilities for social interaction of various segments of the population to support individual well-being and neighborhood livability.