ESRC ADR UK No.10 Data Science Fellowship 2021

Lead Research Organisation: University of Leeds
Department Name: Institute for Transport Studies

Abstract

Collaborating with 10 Downing Street's data science team (10DS) and the Office for National Statistics (ONS), the fellow will utilise linked administrative data created by government and public bodies across the UK to enable vital research to support better informed policy decisions and more effective public services. Analytical projects will be co-designed during a three-month inception phase, followed by a 12-month placement with 10DS and ONS where fellows will produce analysis to inform policy in priority policy areas, using a range of administrative, survey and newly created linked datasets in the access-restricted ONS Secure Research Service funded by ADR UK. Alongside undertaking specified research projects throughout the year, fellows will engage with stakeholders to champion data science across central government and support wider knowledge exchange with researchers on effective policy collaboration and data analysis and will spend a further period of up to three months to enable approved knowledge exchange, impact and publication activity.

Publications

10 25 50
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Beecham R (2023) Supplemental Material - Connected bike ability in London: Which localities are better connected by bike and does this matter?

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Beecham R (2023) Connected bikeability in London: Which localities are better connected by bike and does this matter? in Environment and Planning B: Urban Analytics and City Science

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Beecham R (2022) A Framework for Inserting Visually Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research in Geographical Analysis

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Beecham R (2022) Connected bikeability in London: which localities are better connected by bike and does this matter?

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Lovelace R (2022) EXPLORING JITTERING AND ROUTING OPTIONS FOR CONVERTING ORIGIN-DESTINATION DATA INTO ROUTE NETWORKS: TOWARDS ACCURATE ESTIMATES OF MOVEMENT AT THE STREET LEVEL in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

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Lovelace R (2022) ClockBoard: A zoning system for urban analysis in Journal of Spatial Information Science

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Lovelace R (2022) Jittering: A Computationally Efficient Method for Generating Realistic Route Networks from Origin-Destination Data in Findings

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Tait C (2023) Contraflows and cycling safety: Evidence from 22 years of data involving 508 one-way streets. in Accident; analysis and prevention

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Tait C (2022) Is cycling infrastructure in London safe and equitable? Evidence from the cycling infrastructure database in Journal of Transport & Health

 
Description As part of this 10DS Fellowship, I found new ways to apply data science techniques to policy relevant questions. Specifically, I developed the new 'jittering' method for processing and adding value to origin-destination data. In collaboration with researchers at the Alan Turing Institute I am now using the approach to map walking, wheeling, and cycling to school. The results will inform millions of pounds worth of investment in active travel schemes. More broadly, the project led to a new data science team being set-up in government, after I became the Director of Data and Analysis in Active Travel England on secondment from the data science team in No. 10 (10DS). I also discovered how to set-up data science capacity inside government after applying for the Head of Data Science role in Active Travel England which I am doing on a part time basis since January 2023 alongside my University of Leeds role.
Exploitation Route The outcomes create a strong basis for further work using data science to inform policy. Specifically the outcomes allow people to add value to origin-destination data to inform investment in sustainable transport infrastructure. It has led to a £250k project funded by Transport for Scotland to develop a Network Planning Tool, which will transform the process of active travel network planning in Scotland, benefitting hundreds of thousands of people.
Sectors Energy

Environment

Government

Democracy and Justice

Transport
URL https://findingspress.org/article/33873-jittering-a-computationally-efficient-method-for-generating-realistic-route-networks-from-origin-destination-data
 
Description The award has had a substantial impact on how data science is used in government. The project led to a new data science team being set-up in government, and the 10DS Fellowship was the basis of my work creating and recruiting for the Data and Digital team in Active Travel England, now an established part of the data science ecosystem in government, influencing how part of the Department for Transport family uses data. Through the SchoolRoutes and jtstats projects, the project provided new evidence on mobility and transport inequalities that are being used in practice. More widely, the project changed the narrative on how open source software is used in government and encouraged government departments to publish more code and data in the open. It led to deep collaboration and a new job and a new data science team in government was set-up as a result of this project.
First Year Of Impact 2022
Sector Education,Energy,Environment,Government, Democracy and Justice,Transport