Explainer
What story does this visualisation tell?
This image is taken from an interactive data visualisation mapping tool published by the National Audit Office (NAO). It shows journey times to good and outstanding acute hospital trusts in England (as of 2017 – the most recent data available) and highlights the difference in time taken to reach hospital services across the country. Good quality services are not evenly spread across the country; long public transport journey times may compound this unevenness, putting better services out of reach of some users. For the first time, our analysis considered service quality alongside travel time to the service, giving decision-makers more information on equity so they can decide how best to improve outcomes for users and, therefore, overall value for money.
Why was it created?
The NAO has a unique position and is independent from government. We produced this image and our wider visualisation tool to demonstrate new insights available to government through innovative analysis, and to facilitate discussion across government about the links between local transport and the equitable access, quality, and value for money of public services.
What data and technique/approach did you use to create it and why?
We used journey time modelling and analysis to calculate journey times, by public transport, from thousands of Output Area centroids across England to public service locations. In the case of this image the service locations are acute hospital trusts in England. Our journey time calculations were based on modelling undertaken by the Department for Transport, using the most recent data they make available (journey times as of 2017).
We also included public service quality data from the Care Quality Commission (CQC) which enabled us to calculate journey times to good and outstanding services only. The CQC provided us with ratings to best represent the situation in 2017 to match the time period of the journey time data. We recognise that some ratings of locations may now be different to the ones used in our analysis.
We then compared our calculated journey times with the English Index of Multiple Deprivation (IMD) and the Rural-Urban Classification to explore relationships between journey times, deprivation and rurality.
Who was the intended audience? What other audiences might be interested?
We see several different ways the insights presented in our published outputs could be used across central and local government – for example, in planning decisions, funding allocation and service delivery. Our insights could also provide an evidence base to inform select committee inquires and help MPs understand the local contexts of their constituencies. We also want to build a community of interest in the work beyond government to include academia, research bodies, charities and relevant third sector groups.
Our work also provides a case study for the Department for Transport to understand the utility, relevance and usability of the journey time data the Department makes publicly available.
Why did you choose to present the data in this way over other approaches?
The image above is taken from our interactive data visualisation mapping tool. The interactivity of our tool allows a user to customise the data and analysis they view depending on their interests. Through the different filters and options provided, users can view our journey time analysis to multiple or individual services, choose to see the locations and metadata of the services and select different geographic boundaries to overlay. This flexibility makes our tool accessible and useful for multiple types of users and has proved to be more engaging than a series of static map images.
What impact has the visualisation had in research, policy or other contexts?
The analysis included in our data visualisation aims to generate and facilitate discussions around provision of local transport services and equity in access to local services. Our analysis cuts across government departmental boundaries so it can be used to support cross-government working and improve the understanding of outcomes associated with spending in different parts of government. The complete findings from our analysis can be found in our insights document.
Our analysis has also helped the Department for Transport understand the utility of the journey time data they publish, and we are feeding our experience into their consultation on how they might develop their journey time statistics for future use.
How have people engaged with the visualisation – what has it enabled people to do?
As well as providing new insights for central government departments, we are excited by the possibility our analysis and tool presents for local authorities, who often have control of and deliver public transport services in local areas. Local authorities have told us the tool offers an opportunity for supporting evidence-based decision making in the allocation of funds. Visualising public transport accessibility to local services in specific local contexts enables local authorities to better identify possible areas of need and target funding in such a way as to deliver higher value for money. By identifying communities or areas which may be less well served, funds can be allocated to specific services or improvements to help increase the equity of service accessibility in an area as a whole.
How else might this approach or data be used? How can the visualisation be taken a step further?
Our visualisation demonstrates the potential and opportunity for new insights to be derived from innovative use of the datasets that government already collects and publishes. We encourage government and others to do more with the data already available and to combine and share datasets to produce useful information to inform decision making. For our visualisation, additional service locations (for example, libraries or job centres) could be included, journey times could be population weighted by service users or consideration of the financial cost of a journey could be incorporated.
Try it yourself
For anyone wishing to replicate or build on the NAO's analysis, they have published a comprehensive technical guide detailing their methodology and would be happy to speak to anyone interested in finding out more. A good starting point would be to familiarise yourself with the different approaches used to build journey time models and explore the different types of geolocated data required to build these models. The NAO analysis and interactive tool were created and published in R shiny, which is an open access programming language which anyone can learn to use.
About the creator
Dr Helen Roberts is a Senior Analyst on the Transport Value for Money team at the National Audit Office. She also leads the NAO’s analytical mapping discipline. Through both these roles, Helen has extensive experience of journey time analysis and examining its utility and application in supporting decision making across government.
You can find out more about Helen’s work in her Geography Directions post.