Social Interaction Potential and the Spatial Distribution of Face-to-Face Social Interactions

This paper investigates the spatial distribution of social activity locations. The research makes use of a social interaction potential (SIP) metric to estimate the potential for an individual to participate in a face-to-face social activity at any particular location in the city. The metric is shown to constitute a contact probability field that is sensitive to time-geographic constraints such as home locations, workplaces, and travel times. Empirical case studies drawn from samples in Ghent, Belgium and Concepción, Chile are used to evaluate the effectiveness of the SIP metric in assigning high potential scores to observed social activity episodes. Moreover, a regression model is used to estimate the marginal benefit of using successive levels of constraint detail. The results illustrate both positive and negative aspects of the SIP metric. The metric behaves very well in general; 75% of the time an observed activity location received a score in the 25th percentile. However, lower valued scores were more common in cases when the time-geographic constraints were not very strong (ie, when the commute duration was short), or when activities took place in the homes of the respondents. In the end, the results are a step towards validating the regional scale SIP metric and indicate that it may be useful in microsimulation models of daily travel and activity participation.

[1]  Eric J. Miller,et al.  Exploring the propensity to perform social activities: a social network approach , 2006 .

[2]  Khandker Nurul Habib,et al.  Social Context of Activity Scheduling , 2008 .

[3]  Matthew J. Roorda,et al.  Validation of TASHA: A 24-Hour Activity Scheduling Microsimulation Model , 2008 .

[4]  Tijs Neutens,et al.  Dealing with Timing and Synchronization in Opportunities for Joint Activity Participation: Opportunities for Joint Activity Participation , 2010 .

[5]  Darren M. Scott,et al.  Modeling constrained destination choice for shopping: a GIS-based, time-geographic approach , 2012 .

[6]  Jonathan Bishop,et al.  Networked: The New Social Operating System , 2013, Int. J. E Politics.

[7]  Tijs Neutens,et al.  Spatial variation in the potential for social interaction: A case study in Flanders (Belgium) , 2013, Comput. Environ. Urban Syst..

[8]  T. Arentze,et al.  An Analysis of Context and Constraints-dependent Shopping Behaviour Using Qualitative Decision Principles , 2005 .

[9]  Yali Chen,et al.  Feasibility of using time–space prism to represent available opportunities and choice sets for destination choice models in the context of dynamic urban environments , 2012 .

[10]  B. Wellman,et al.  How Far and with Whom Do People Socialize? , 2008 .

[11]  Craig R. Rindt,et al.  The Activity-Based Approach , 2008 .

[12]  K. Axhausen,et al.  Structures of Leisure Travel: Temporal and Spatial Variability , 2004 .

[13]  M. O'Kelly,et al.  Disaggregate Journey-to-Work Data: Implications for Excess Commuting and Jobs–Housing Balance , 2005 .

[14]  Juan Antonio Carrasco,et al.  Affective Personal Networks versus Daily Contacts: Analyzing Different Name Generators in a Social Activity-Travel Behavior Context , 2013 .

[15]  Matthew J. Roorda,et al.  Prototype Model of Household Activity-Travel Scheduling , 2003 .

[16]  Eric J Miller,et al.  Collecting Social Network Data to Study Social Activity-Travel Behavior: An Egocentric Approach , 2008 .

[17]  T. Arentze,et al.  Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation , 2008 .

[18]  Eric G. Moore Some Spatial Properties of Urban Contact Fields , 2010 .

[19]  S. Srinivasan,et al.  Modeling household interactions in daily in-home and out-of-home maintenance activity participation , 2005 .

[20]  Wei Tu,et al.  A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities , 2011 .

[21]  Martin Dijst,et al.  Face-to-face and electronic communications in maintaining social networks: the influence of geographical and relational distance and of information content , 2010, New Media Soc..

[22]  Tijs Neutens,et al.  Minimum commuting distance as a spatial characteristic in a non‐monocentric urban system: The case of Flanders , 2011 .

[23]  Hongbo Yu,et al.  Potential effects of ICT on face-to-face meeting opportunities: a GIS-based time-geographic approach , 2011 .

[24]  Ta Theo Arentze,et al.  Modeling social interactions between individuals for joint activity scheduling , 2012 .

[25]  Jean-Claude Thill,et al.  Choice set formation for destination choice modelling , 1992 .

[26]  Harry Timmermans,et al.  Factors Influencing the Planning of Social Activities , 2010 .

[27]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[28]  Sean T. Doherty,et al.  How Far in Advance Are Activities Planned? , 2005 .

[29]  Tijs Neutens,et al.  My space or your space? Towards a measure of joint accessibility , 2008, Comput. Environ. Urban Syst..

[30]  Tijs Neutens,et al.  Arranging place and time: A GIS toolkit to assess person-based accessibility of urban opportunities , 2010 .

[31]  Tijs Neutens,et al.  The Social Interaction Potential of Metropolitan Regions: A Time-Geographic Measurement Approach Using Joint Accessibility , 2013 .

[32]  Lawrence A. Brown,et al.  Urban Acquaintance Fields: An Evaluation of a Spatial Model , 1970 .

[33]  Kay W. Axhausen,et al.  Collective Location Choice Model , 2011 .

[34]  J. Prashker,et al.  The Effect of Temporal Constraints on Household Travel Behavior , 1981 .

[35]  Harry Timmermans,et al.  ALBATROSS: Multiagent, Rule-Based Model of Activity Pattern Decisions , 2000 .

[36]  Mei-Po Kwan,et al.  Network-based constraints-oriented choice set formation using GIS , 1998 .