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    Highlights Often, official commuting statistics are available only as geographical aggregates. Aggregate datasets are limited: intra-zone variation is masked. Spatial microsimulation can overcome this issue by modelling individuals. Methods of generating and analysing simulated microdata are illustrated. Policy-relevant results about intra-zone variability and inequalities are shown.

    Abstract The daily trip to work is ubiquitous, yet its characteristics differ widely from person to person and place to place. This is manifested in statistics on mode and distance of travel, which vary depending on a range of factors that operate at different scales. This heterogeneity is problematic for decision makers tasked with encouraging more sustainable commuter patterns. Numerical models, based on real commuting data, have great potential to aid the decision making process. However, we contend that new approaches are needed to advance knowledge about the social and geographical factors that relate to the diversity of commuter patterns, if policies targeted to specific individuals or places are to be effective. To this end, the paper presents a spatial microsimulation approach, which combines individual-level survey data with geographically aggregated census results to tackle the problem. This method overcomes the limitations imposed by the lack of available geocoded micro-data. Further, it allows a range of scales of analysis to be pursued in parallel and provides insights into both the types of area and individual that would benefit most from specific interventions.


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    Titel :

    A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels


    Beteiligte:
    Lovelace, Robin (Autor:in) / Ballas, Dimitris (Autor:in) / Watson, Matt (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2013-01-01


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch