Page | 1 Long-time series, small-area statistics for sustainable transport Morgan M*1, and Philips I†1 1Institute for Transport Studies, University of Leeds, UK GISRUK 2023 Summary Transport policy often suffers from a paucity of data on which to build a robust evidence base. Long- time series, small-area transport statistics over a large geographical area can provide insights into the effects of a broad range of transport policies. Unfortunately there are important gaps in the UK’s small-area statistics that limit their usability and effectiveness for answering key policy questions. This paper reviews the state of data in the UK and reports early progress on filling data gaps. KEYWORDS: transport, land use, zonal statistics, spatiotemporal data, geodemographic 1. Introduction Car ownership and use are at the heart of many of the transport sustainability crises which require urgent action including carbon emissions, NOx and particulates, road death, car dependence and social exclusion. Understanding the dynamic changes in patterns of car ownership and use is fundamental to addressing these crises. Strangely, whilst the UK government (and likely many other governments in other countries) have a constantly updating complete census of vehicle ownership and an annual census of use (UK MOT road worthiness tests), transport analysis relies upon much more data sparse methods, such as modelling and natural experiments. One challenge of implementing rapid change to the transport system is a lack of evidence of what works and where. Transport is part of and interacts with a complex socio-technical system, and so effective solutions need to account for a wide range of variables, making it unclear if a solution that worked in one place and time is transferable to another context. This can be especially true if new solutions must be implemented against resistance from sceptical politicians and the public. Unfortunately for researchers, studying transport interventions in a controlled environment is usually impossible. Academics rarely have the power or resources to build infrastructure or change regulations for research. Thus, much transport research utilises observational studies such as natural experiments, modelling, or qualitative work. While these methods are a well-established in many disciplines, the lack of control limits how generalisable their conclusions can be. Large sample sizes can boost confidence in findings, but in practice, only so many changes occur each year and not every change is studied. Thus, many transport policies are supported by an evidence base of only a handful of case studies. Given the limited opportunities for natural experiments, a missed opportunity is especially disappointing and often occurs when an ex-anti study was either not conducted or was inadequate. For example, data gathered as part of the transport planning process is often insufficiently robust for academic research. So researchers must be embedded early in the planning process to conduct successful research. Fortunately, recent improvements in the publication of administrative data may provide a low-cost way to study many natural experiments, including historical events previously considered missed opportunities. * M.Morgan1@leeds.ac.uk † I.Philips@leeds.ac.uk