Core Themes
Data Science Campus work focuses on a number of core themes:
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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.
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Transformation in the Ghana Statistical Service
Using data science and automation in the production of price statistics
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Enabling Data Access through Privacy Preserving Synthetic Data
Synthetic data are artificially generated data that are made to resemble real-world, often sensitive, data.
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Synthesising the linked 2011 Census and deaths dataset while preserving its confidentiality
This report offers a thorough look into how we created a synthetic version of a large, linked, and confidential dataset while adhering to a formal privacy framework
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Comparing international transport performance in urban centres: upcoming work
We are planning to follow up on previous work we published earlier this year looking at public transport availability across the UK, by providing metrics for urban centres across the UK, as well as their international counterparts.
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Guest blog: Enhancing open-access data analysis: introducing the Journey Time Statistics R and Python packages
In this guest blog, 10DS data science fellows Federico and Robin, talk about working with the Campus to create packages that import, process and visualise DfT’s journey time statistics data.
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The use of microdata for firm-level analysis of preference tariff utilisation in the UK: technical report
We go behind our analysis on the use of microdata for the examination of preference tariff utilisation and take a deep dive into challenges of drawing together new administrative data sources to answer relevant policy questions.
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Using new shipping data to improve government understanding of trade flows
We show how shipping instructions can be used to map the trade routes of critical goods. This will help understand our reliance on global ports for accessing specific products, and draw insights on the impact of important events such as strikes.
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Using open data to understand hyperlocal differences in UK public transport availability
We have published data to help researchers and local planners understand how public transport access varies across the UK, using open data and open-source software.
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Using Sentinel-2 images to measure the change in tree coverage in eastern Uganda: what does it mean for the Mbale Trees Programme?
We used machine learning to develop a model to identify areas of trees from satellite images in eastern Uganda, where the Mbale Trees Programme has been running since 2010.
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Using data science to help inform regional broadband investment
The Campus has delivered a suite of tools and code to North of Tyne Combined Authority (NTCA) to support their work to prioritise broadband infrastructure improvements in the region.
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Identifying different roles in the social care sector using online job advertisements
In this guest blog, data science apprentice Evie Brown from the Social Care Analysis team at the Office for National Statistics (ONS) presents work on grouping online job adverts by social care role. This project was a significant part of the final year of the Level Six Data Science Apprenticeship.
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Predicting the Energy Performance Certificates (EPC) of properties
The Department for Business, Energy and Industrial Strategy (BEIS) asked the Campus to see if machine learning techniques could be used to predict the energy efficiency of properties that do not have an official score. Read more about our findings on this blog post.
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Visualising rail schedules using open data
Rail services can be affected by temporary factors such as unseasonably hot weather, industrial action and engineering works. We produced maps from open data showing service levels at every station in Great Britain over a 21-day period.
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Evaluating synthetic data using SynthGauge
Data synthesis is an active area of research for many organisations, including the Office for National Statistics (ONS). SynthGauge is a Python library that provides decision-makers with a range of metrics and visualisations for evaluating synthetic data.
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Case study: responding to the coronavirus pandemic using aggregated BT mobility data
To support the national fight against coronavirus (COVID-19) in March 2020, BT made aggregate, anonymised mobility data available to the UK Government. We quickly turned this into daily updates, with only one day’s delay between activity and the reporting of it.
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Use of hybrid data to understand the community-level influences on coronavirus (COVID-19) incidence
Understanding and monitoring the major influences on COVID-19 infection numbers in communities is essential to inform policy making and evaluate the impact of non-pharmaceutical interventions. We have developed a community-level analysis by assembling a large set of static and dynamic data for England.
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Technical report: Predicting cattle camp locations in South Sudan from Sentinel 2 satellite imagery
Cattle are central to many people’s livelihoods in South Sudan, but there are very few recent data on the number of animals and their geographic distribution. Building on previous work, this blog explores new methods, based on convolutional neural networks, that can better distinguish cattle camps from similar landscape features.
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The Data Science Campus – five years of data science for public good
On 27 March 2017, with an audience of UK and international data science leaders from across the public, private and academic sectors, a team of 8, some brilliant presentations, and a lot of excitement, the Data Science Campus was launched. 5 years later, Louisa Nolan shares what have we learned along the way.
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Employing data science to analyse the use of preferential tariffs in free trade agreements
Preference utilisation rates (PURs) measure the extent to which UK businesses make use of the zero or reduced tariffs available via free trade agreements (FTAs). In this work, we study the take-up of preferential tariffs by UK businesses between 2009 and 2019 and examine their trends and patterns.
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Technical Report: Project Mertz—novel use of historical RAF flight safety records
Part of our mission at the Data Science Campus (DSC) is to build data science capability across the public sector. In this project, which grew out of our Data Science Accelerator programme, we worked with the Royal Air Force (RAF) to help upskill their staff in the Python programming language and natural language processing (NLP) methods.
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Estimating vehicle and pedestrian activity from town and city traffic cameras
We have released a new version of our open codebase to enable the Traffic Cameras project to run on a single, stand-alone laptop as well as on Google Cloud Platform, making it more accessible to users without cloud infrastructure.
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Making an international impact with data science mentoring
Our FCDO hub’s mentorship model has helped countries, including Rwanda and Vanuatu, deliver in-house data science projects with significant impact, while also creating a flourishing sustainable ecosystem of data scientists.
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Automated coding of Standard Industrial and Occupational Classifications (SIC/SOC)
This report summarises our work to meet widespread demand for better automated and higher quality solutions to this problem, by using machine learning (ML) methods to improve the match rate and accuracy of automatic classification of SIC and SOC classifications.
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Using Mobile Phone Data for Enhancing International Passenger Survey Traveller Statistics
In this guest blog, Robyn Hunt from the ONS travel and tourism review team looks at using mobility data to model estimates from the International Passenger Survey.
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Detecting Trucks in East Africa
Many countries in the developing world lack regular estimates of road traffic activity. To address this, we applied machine learning techniques to open-source satellite data to generate estimates of traffic volume in Kenya, East Africa.
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Using data science to explore changes in behaviour and well-being during the coronavirus (COVID-19) pandemic
In this report, we talk about our use of novel machine and deep learning techniques to explore changes in population behaviour and well-being during the coronavirus pandemic.
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Mapping HIV risk in Côte d’Ivoire, West Africa
We have been exploring if machine learning methods and publicly available spatial datasets can be used to map and understand HIV risk in Cote d’Ivoire, West Africa.
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Using Facebook data to understand changing mobility patterns
We have been exploring Facebook data to understand changing patterns of mobility and the impact of lockdown restrictions.
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Understanding mobility during the COVID-19 pandemic
This blog outlines some of the work the Campus has been doing, bringing our skills together with new data sources to help inform responses to the coronavirus (COVID-19) pandemic.
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Quality and methodology: Extent and change of surface water statistics
A report on the quality and methodology of data used to produce extent and change of surface water statistics for reporting on Sustainable Development Goals indicator 6.6.1 for the UK.
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Using satellite imagery to report changes to water bodies for SDG 6.6.1
In this blog, we describe how we have assessed the quality of the novel Global Surface Water Explorer (GSWE) dataset to better understand its value and fitness-for-purpose, producing data to report the UK’s position on indicator 6.6.1.
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Extracting text data from business website COVID-19 notices
We used text data from over 500,000 business websites to inform survey response-chasing efforts and gain insights into the impact of COVID-19.
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Counting cows in South Sudan
On World Statistics Day 2020, our ONS FCDO Data Science Hub talk about exploring the use of satellite imagery to conduct a cattle census in South Sudan.
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Payments Data for Public Good
The latest update on our work with Barclays exploring if aggregated and anonymised card transactions data could provide significantly quicker, more granular insight into UK consumer spending.
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pyGrams: An open source tool for discovering emerging terminology in large text datasets
Large volumes of unstructured, free-text data such as patent applications, contain potentially valuable information for policymakers. Here we describe the pipeline used to process such data sources.
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Discovering emerging important terminology in large text datasets using pyGrams: a comparison between net growth and e-score methods
In this report, we focus primarily on how the pyGrams tool can be used to analyse terms through time using the time stamps in document metadata.
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Extracting, visualising and identifying emerging important terminology from patent collections
We introduce pyGrams – a new Python tool for extracting, visualising and identifying emerging terms in large document collections, such as patents.
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Supporting the response to coronavirus (COVID-19)
We are exploring new data sources such as Google’s Community Mobility Reports to strengthen the information that we have through surveys and other sources.
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Can machine learning be used to predict energy performance scores?
Our latest project investigates the use of machine learning techniques to predict missing energy performance scores. It also attempts to create a complete picture of the energy efficiency profile for domestic properties in Wales.
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Using machine learning to predict energy efficiency
Faster indicators of UK economic activity: more timely and relevant shipping indicators
This article summarises further development we have carried out to improve the timeliness and relevance of the shipping indicators, in response to feedback from our users.
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Data science for sustainable development
How data science is helping to address the challenge of measuring the Sustainable Development goals in the UK.
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Understanding the characteristics of high growth companies using non-traditional data sources
What can internet use tell us about our society and the economy?
Extracting social-economic signals from internet traffic data.
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Green spaces in residential gardens
Improving the current approach to identifying the proportion of vegetation for urban residential gardens in Great Britain.
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Faster indicators of UK economic activity: shipping
Faster indicators of UK economic activity: Value Added Tax returns
Faster indicators of UK economic activity: road traffic data for England
The month that was – June 2018
So, June was notable for (i) more uninterrupted sunshine than I can remember in the UK; (ii) the short-lived excitement…
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The month that was – May 2018
Where do the days go? It’s already time for my latest update – providing an overview of what happened in the…
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The months that were – March and April 2018
Here’s the latest “monthly” update for the Data Science Campus, covering both March and April 2018. We took a break…
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The month that was – February 2018 (plus a bit)
Here’s our latest monthly update for the Data Science Campus, covering February 2018. We’ve taken the liberty of stretching the…
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Evaluating Calorie Intake
1. Executive Summary The Evaluating Calorie Intake for Population Statistical Estimates (ECLIPSE) project was carried out by the Data Science…
Read more on Evaluating Calorie Intake The months that were – December 2017 and January 2018
Following a brief midwinter break, here is our bumper two-month update, covering December 2017 and January 2018. The Campus continues…
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The month that was – November 2017
Here’s our update for November, the month when the Campus won an award and had the privilege of welcoming numerous…
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Using doubly labelled water to improve our understanding of the UK’s calorie intake. The problem We’re not very good at…
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The month that was – October 2017
This is our monthly update for October 2017 – one of a series of regular snapshots that highlight some of…
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The month that was – September 2017
This is our monthly update for September – one of a series of regular snapshots on what we’ve been doing…
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The month that was – August 2017
Welcome to “the month that was”, providing you with a snapshot of what we’ve been doing this month to support…
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The month that was – July 2017
Welcome to the first of our monthly updates – a regular snapshot on what we’ve been doing to support data…
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The first 100 days
It is 100 days since the Data Science Campus officially opened our doors to help the Office for National Statistics…
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Building and stocking the survey question library
The Office for National Statistics (ONS) is on a fantastic journey to better serve the UK by improving the quality…
Read more on Building and stocking the survey question libraryCollaborating with Statistics Netherlands towards a data driven society
The Office for National Statistics (ONS) Data Science Campus is keen to collaborate with partner organisations both in the UK…
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Mapping the urban forest
Urban forest refers to the trees and vegetation present in the streets, parks, gardens, balconies and even green roofs within…
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We are officially open for business
The Data Science Campus celebrated a significant achievement on Monday 27 March, as it opened its doors for the first…
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When the team met Turing
On 5 December 2016, the Office for National Statistics and The Alan Turing Institute signed a Memorandum of Understanding signalling their shared…
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