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

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

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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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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.
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.
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 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.
Sector Education,Energy,Environment,Government, Democracy and Justice,Transport