An integrated spatio-temporal GIS toolkit for exploring intra-household interactions

Kang, Hejun; Scott, Darren M.

doi:10.1007/s11116-007-9146-4

Cited by 43 publications

(36 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the activity purposes reported by the involved individuals were the same (type A episode), or if one or more of the involved individuals reported the activity purpose of participation as "accompanying another individual" with the other involved individuals reporting the same activity purpose (type B episode), the tagged episode was designated as a joint activity episode. The activity purpose for the type A episode is straightforward, while the activity purpose for the type B episode corresponds to the activity purpose of the individual(s) who reported a purpose other than "accompanying another individual" (Kang and Scott, 2008 use a similar method to identify joint activities). Finally, the durations of episodes were aggregated by purpose and participating individuals to obtain the weekday durations, and served as the dependent variables of the MDCEV model.…”

Section: Determination Of Joint Activity Participation and Associatedmentioning

confidence: 99%

A household-level activity pattern generation model with an application for Southern California

et al. 2013

View full text Add to dashboard Cite

This paper develops and estimates a Multiple Discrete Continuous Extreme Value (MDCEV) model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the Post Census Regional Household Travel Survey conducted by the South California Association of Governments (SCAG) in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.Keywords: Intrahousehold interactions, joint activity participation, multiple-discreteness, activity-based travel demand modeling 1 INTRODUCTIONThe emphasis of the activity-based approach to travel modeling is on activity participation and scheduling over a specified time period (usually a weekday in the U.S.), with travel being viewed as a derivative of out-of-home activity participation and scheduling decisions. While the detailed structures of activity-based models (ABMs) vary substantially, it is typical for ABMs to model "mandatory" activity decisions such as out-of-home work-related decisions (employed or not, duration of work, location of work, and timing of work) and education-related decisions (student or not, duration of study, location of study, and timing of study) as precursors to the generation of out-of-home non-work activity participations and the overall activity-travel schedules of individuals (including the scheduling of work and non-work episodes). Within the context of the generation of out-of-home non-work activity participation, while early activity-based travel studies ignored the interactions between individuals within a household (see, for example, Mannering et al., 1994, Lu andPas, 1999), more recent studies and models have emphasized the need to explicitly consider such interactions and model joint activity participations within a household. This is motivated by several considerations. First, individuals within a household usually do not make their activity engagement decisions in isolation. As articulated by Gliebe and Koppelman (2002) and Kapur and Bhat (2007), an individual's activity participation decisions are likely to be dependent on other members of the household because of the possible sharing of household maintenance responsibilities, joint activity participat...

show abstract

Section: Determination Of Joint Activity Participation and Associatedmentioning

confidence: 99%

A household-level activity pattern generation model with an application for Southern California

et al. 2013

View full text Add to dashboard Cite

show abstract

“…First, compared to people in the work force, most seniors have a more flexible schedule, which is relatively easier for them to schedule activities with people due to much less constraints [29][30][31][32]. This, however, is inconsistent to the fact that seniors showed lower social contacts as observed.…”

Section: Conclusion and Discussionmentioning

confidence: 93%

Social integration: How is it related to self-rated health?

Kang¹,

Michael²

2013

AAR

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent accomplishments in this field include the implementation of 5 toolkits for querying and representing the opportunities for joint activity participation and intrahousehold interaction (Kang & Scott, 2008;Kwan & Lee, 2011;Neutens, et al, 2010b;Yu & Shaw, 2008), the theoretical formulation of necessary conditions for physical and virtual interaction (Miller, 2005b) and the development of an analytical framework for measuring joint accessibility (Fang, et al, 2011;Neutens, et al, 2010a;Neutens, et al, 2008).…”

Section: [Insert Figure 1 Here]mentioning

confidence: 99%

Spatial variation in the potential for social interaction: A case study in Flanders (Belgium)

Neutens

Farber

Delafontaine

et al. 2013

Computers, Environment and Urban Systems

View full text Add to dashboard Cite

Abstract.Empirical evidence is mounting that good urban design fosters the formation of social fabric.Existing evidence is however limited in at least two respects. First, empirical studies have focused largely on social interactions taking place within the residential neighborhood, while leaving social encounters near the workplace unconsidered. Second, while various studies have examined the impact of the built environment on realized social behavior, there is as yet no empirical research on the potential for having social contact. A deeper understanding of the geography of social interaction potential is nonetheless important, for it is individuals' social opportunities rather than their preferences and actual choices that are most directly amenable to policy intervention. This paper seeks to address both issues in an empirical case study in Flanders and Brussels (Belgium). An exploratory spatial analysis is conducted to uncover spatial trends in the potential for social interaction in order to better understand the role of urban spatial structure in the production of social interaction potential.

show abstract

An integrated spatio-temporal GIS toolkit for exploring intra-household interactions

Abstract: 3D, Activity analysis, Geovisualization, GIS, Intra-household interaction, Joint activity, Space-time path, Spatio-temporal GIS, Travel behavior,

Cited by 43 publications

References 23 publications

A household-level activity pattern generation model with an application for Southern California

A household-level activity pattern generation model with an application for Southern California

Social integration: How is it related to self-rated health?

Spatial variation in the potential for social interaction: A case study in Flanders (Belgium)

Contact Info

Product

Resources

About