Social
About
The Data Science Campus supports ONS social priorities by identifying the role new data sources and methods can play in modernising approaches to demography. We also work with other government departments to deliver insights in support of social policy making.
Creating Transformational Change
“The Office for National Statistics (ONS) is developing a future population and migration statistics system for England and Wales, making use of the best available data sources, using robust and innovative methods.” ONS
The Data Science Campus are delivering work establishing how novel data, and machine learning methods can be used to support the ONS proposal to transform the production of population and migration statistics.
Recent examples include:
- Collaborating with ONS’s Small Area Estimation team to develop a proof of concept for using geospatial data sets (including earth observation data, and mobile phone data) and machine learning methods to derive household population estimates for lower layer super output areas in England and Wales.
- Developing methods for updating travel to work matrices between census years, by integrating data that is updated at a higher frequency.
We also work to support other government departments with their decision-making; for instance we recently worked with the Department for Environment, Food and Rural Affairs (DEFRA), to use Strava Metro data to estimate engagement with natural spaces in support of their 25 Year Environment Plan.
Publications
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ParliAI – using AI tools to monitor parliamentary coverage of the ONS across the UK
Introduction As part of understanding user need, monitoring parliamentary coverage is an important way to understand how people use Office…
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Transport Performance: A reproducible and reusable toolkit for measuring transport network performances internationally
We have built a new, open-source toolkit for measuring the performance of urban centre transport networks
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Bus reliability – analysing the morning travel peak across England
We have explored the feasibility of using open data to analyse local transport performance. In this publication we demonstrate how to produce hyperlocal bus service coverage and punctuality metrics.