A SPATIAL TYPOLOGY OF CAR USAGE AND ITS LOCAL DETERMINANTS IN ENGLAND

This paper presents an initial classification of Middle-layer Super Output Areas (MSOAs) in England based on their car ownership, car usage and relevant local characteristics. Whilst a long lineage of widely used geodemographic classifications exist in the UK, none of these is sufficiently focused on travel behaviour and transport infrastructure to allow a useful placebased understanding of travel patterns alongside monitoring and evaluation of local transport interventions. The analysis uses a privileged dataset which includes the characteristics of every vehicle registered in the UK in 2011, the registered keeper type and location and the annual mileage derived from annual ‘MOT’ tests. We present initial results in the process of developing a typology of MSOAs using cluster analysis applied to the car and mileage data alongside variables selected from a long list of variables from additional sources including the Census and DfT Accessibility statistics. The most meaningful set of variables to use as clustering variables is derived from underpinning regression models to identify the strongest determinants of car ownership and use. A clustering procedure is tested to produce a stable and meaningful set of provisional local area transport-types. We present the methodology used to create the classification, a visual profile of each local transport-area-type identified and identify the next steps required to develop and address the methodological and conceptual challenges of identifying appropriate spatial units of analysis, and change over time. This initial classification has potential to be extended with other available datasets including, meteorological and topographical data as well as new local level measures of rail and bus provision, developed specifically for this project. We conclude with a brief discussion of how the identification of places that are physically, socially and behaviourally similar to each other in terms of their current car usage patterns and associated determinants allows for context appropriate policy planning, evaluation and knowledge sharing.