HABITS will be using data from the GoSmarter travel app. This app is part of the EMPOWER H2020 project which incentivises walking and cycling. The GoSmarter app collects individual trip data including date, time, duration, mode, speed, geocoding, weather. To find out further details about the EMPOWER project, visit the EMPOWER website.
The HABITS project has received funding through the Big Data Phase 3 New and Emerging Forms of Data: Policy Demonstrator Projects. There are a number of other funded projects, including:
Social sensing of health and wellbeing impacts from pollen and air pollution
This project at the University of Exeter will evaluate a social sensing prototype focused on pollen, air pollution, asthma and hayfever. The prototype will be assessed for use by several partner organisations working on environment and public health issues: Met Office, Public Health England and AsthmaUK. An integrated ethical investigation will directly address the privacy and fairness concerns raised by social sensing. The project has three aims: (1) Create a prototype social sensing platform to track health and wellbeing impacts of pollen and air pollution; (2) Work with partners to evaluate the usefulness of social sensing in a variety of real-world scenarios; and (3) Investigate ethical concerns around social sensing, in particular, fairness and privacy. These aims will be delivered by achieving four specific objectives: (1) Engage end-users and stakeholders to co-design project goals; (2) Develop a prototype social sensing tool focused on pollen, air pollution, asthma and hayfever; (3) Evaluate the prototype in multiple real-world scenarios identified by partner organisations; and (4) Use academic literature, public engagement and surveys to assess ethical concerns around privacy and fairness.
Data Awareness for Sending Help (DASH)
This project at King’s College London explores integration of new and emerging data sources for potential impact on emergency response. In an emergency medical situation, ambulances must get to those in need as quickly as possible in order to provide care and, ultimately, to save lives. Decisions about which ambulance should respond to each incident need to be made rapidly. However, making such decisions is complicated: incidents can occur simultaneously or in short succession over a wide area; the locations of ambulances are constantly changing; and there are many environmental factors that can affect response times, such as traffic and weather conditions. In such a complex and dynamic environment, a form of automated decision support, known as computer assisted dispatch (CAD), is often installed to help staff make these decisions. This Policy Demonstrator Project aims to explore the potential of these “new data” sources for improving ambulance response times. The project builds on a new research collaboration between King’s College London (KCL) and the London Ambulance Service (LAS), which is evaluating novel methods for ambulance dispatch by simulating ambulance call-outs based on historical LAS system logs. DASH will lay the groundwork for extending this preliminary study in important new directions, by predicting changes in response times due to integration of additional data sources.
The food sentiment observatory: exploiting new forms of data to help inform policy on food safety and food crime risks
Social media and other forms of online content have enormous potential as a way to understand people’s opinions and attitudes, and as a means to observe emerging phenomena – such as disease outbreaks. How might policy makers use such new forms of data to better assess existing policies and help formulate new ones? This one year demonstrator project is a partnership between computer science academics at the University of Aberdeen and officers from Food Standards Scotland which aims to answer this question. The project will develop a software tool (the Food Sentiment Observatory) that will be used to explore the role of data from sources such as Twitter, Facebook, and TripAdvisor in three policy areas selected by Food Standards Scotland: (1) attitudes to the differing food hygiene information systems used in Scotland and the other UK nations; (2) study of an historical E.coli outbreak associated with venison products to understand effectiveness of monitoring and decision making protocols; (3) understanding the potential role of social media data in responding to new and emerging forms of food fraud.
Population247NRT: near real-time spatiotemporal population estimates for health, emergency response and national security
Decision-making and policy formulation in sectors such as health, emergency/crisis response and national security, ideally require accurate dynamic information on the number of people in specific places at specific times of the day, week, season or year. Traditional census data do not provide this level of detail but are often used for such policy and planning purposes. This project will combine new, emerging and existing datasets in order to produce enhanced time-specific population estimates for more informed decision-making and policy formulation in the health, emergency/crisis response and national security sectors. It is a collaborative project between University of Southampton, Public Health England (PHE), Health and Safety Executive (HSE) and Defence Science and Technology Laboratory (Dstl). The project will enhance existing methods and tools for harvesting, processing, integrating and calibrating new, emerging and existing data sources in order to produce time-specific population estimates.
Inclusive and healthy mobility: understanding trends in concessionary travel in the West Midlands
In this project, University College London will develop a Data Linkage and Analytics Framework that permits the systematic analysis of new, novel, rich and complex datasets routinely collected by transport authorities for geographically extensive areas. The objective is to better understand the pressing policy challenges of social exclusion in daily mobility. The project will use data on electronic ticketing, GPS-tracked vehicle movements and ancillary sources collected by Transport for West Midlands and linked to administrative, consumer and survey data collected by the Office for National Statistics, the Department for Transport and ESRC. The project will use the secure data and computing infrastructure available at the ESRC Consumer Data Research Centre (CDRC) to store, manage, link and process the data.
New and emerging forms of violence data for crisis response: a comparative analysis in Kenya
The project will produce the first robust evidence base on the opportunities and limitations of social media data on violence reporting to inform UK emergency and crisis response. These responses include targeted humanitarian support to vulnerable and conflict-affected populations, development of rapid conflict and risk assessments to inform policy and strategic action, and support to political and other reconciliation efforts in the medium-term (DfID, 2009, 2010). Effective UK Government crisis and emergency response increasingly depends on the availability of timely, reliable data on political violence, to determine the scale and dimensions of crises and tailor responses (UKAid, 2013). While social media reports of violence can inform the design, targeting, and geography of crisis response, there is limited robust research on their reliability and comprehensiveness. This project will test the reliability and comprehensiveness of social media data, against conventional media reporting of violence in a real-time context: the August 2017 Kenyan elections. It will identify opportunities new data provide for policy, and what limitations restrict usability, along three dimensions: 1) reporting timeliness; 2) targeting of crisis response; 3) geographies of violence risk.
Centre for cyberhate research and policy: real-time scalable methods and infrastructure for modelling the spread of cyberhate on social media
In partnership with the UK Head of the Cross-Government Hate Crime Programme at the Department for Communities and Local Government (DCLG), and the London Mayor’s Office for Policing and Crime’s (MOPAC) new Online Hate Crime Hub, the proposed project will co-produce evidence on how social media data, harnessed by new Social Data Science methods and scalable infrastructure, can inform policy decision making. We will achieve this by taking the social media reaction to the referendum on the UK’s future in the European Union as a demonstration study, and will co-develop with the Policy CI transformational New Forms of Data Capability contributions including: (i) semi-automated methods that monitor the production and spread of cyberhate around the case study and beyond; (ii) complementary methods to study and test the effectiveness of counter speech in reducing the propagation of cyberhate, and (iii) a technical system that can support real time analysis of hate and counter speech on social media at scale following ‘trigger events’, integrated into existing policy evidence-based decision-making processes. The system, by estimating the propagation of cyberhate interactions within social media using machine learning techniques and statistical models, will assist policymakers in identifying areas that require policy attention and better targeted interventions in the field of online hate and antagonistic content.