Abstract
This article explores the stability of local vehicle ownership rates in Great Britain using the technique of spatial Markov chain analysis. Non-spatial Markov chain processes describe the transition of neighbourhoods through levels of ownership with no regard to their neighbourhood context. In reality however, how a neighbourhood transitions to different levels of ownership could be influenced by its neighbourhood context. A spatial Markov chain accounts for this context by estimating transition properties that are conditioned on the surrounding neighbourhood. These spatial Markov chain properties are estimated using a long run census time series from 1971 to 2011 of household vehicle ownership rates in Great Britain. These processes show that there is different behaviour in how neighbourhoods transition between levels of ownership depending on the context of their surrounding neighbours. The general finding is that the spatial Markov process will lead to a greater homogeneity in levels of ownership in each locality, with neighbourhoods surrounded by relatively low ownership neighbourhoods taking longer than a non-spatial Markov process would suggest to transition to higher levels, whilst neighbourhoods of high ownership surrounded by high ownership neighbourhoods take longer to transition to lower levels. This work corroborates Tobler's first law of geography “Everything is related to everything else, but near things are more related than distant things” but also provides practical guidance. Firstly, in modelling ownership, spatial effects need to be tested and when present, accounted for in the model formulation. Secondly, in a policy context, the surrounding neighbourhood situation is important, with neighbourhoods having a tendency towards homogeneity of ownership levels. This allows for the effective planning of transport provision for local services. Thirdly, vehicle ownership is often used as a proxy for the social and aspirational nature of an area and these results suggest that these properties will persist for a prolonged period, possibly perpetuating and exacerbating differentials in society.
Highlights
This study on vehicle ownership is set in the context of the island of Great Britain, which is part of the United Kingdom
Whilst the classic discrete Markov chain is a flexible framework for modelling transitional dynamics, it has some potential limitations when applied in a spatial context
The key findings and contributions of this study are that: (i) a comparison of the steady state probabilities and transition times in the non-spatial and spatial cases clearly demonstrates that neighbourhood context influences the transition of neighbourhoods through the levels of vehicle ownership; (iii) the duration to transition between the extreme levels of ownership is seen to be long when the neighbourhood context is different to that at the end of the transition; and (ii) the incorporation of spatial effects into models of behaviour are likely to produce substantially different estimates and conclusions
Summary
This study on vehicle ownership is set in the context of the island of Great Britain, which is part of the United Kingdom. After previous rapid increases in vehicle ownership, the 21st century shows a more stable pattern, with one vehicle households accounting for around 45% of all households and two or more vehicle owning household accounting for a further 30% The causes of this transition are well studied in the literature. The UK's adoption of the recommendations of the Intergovernmental Panel on Climate Change in its Climate Change Act has necessitated policy interventions to try and mitigate the environmental impacts of vehicle use (Marsden and Rye, 2010). Having set the policy and historic context of vehicle ownership in Great Britain, the research question for this study is concerned with whether there is a spatial dimension to this changing pattern of ownership in Great Britain.
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