Project overview
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. The ESRC-funded Population247 programme of research (Martin et al, 2015) developed a framework, methodology and software tool (SurfaceBuilder247) for integrating diverse contemporary data sources to produce enhanced time-specific population estimates for small geographical areas. Its usefulness has since been demonstrated for flooding and radiation emergency response/planning, through collaborations with HR Wallingford and Public Health England. These models have primarily involved the integration of open administrative data for activities such as place of residence, work, education and health. Now, new and emerging forms of data, such as sensor data, live and static data feeds provided via the internet, and various commercial datasets which were not previously available, provide exciting opportunities to enhance these population estimates. Such new and emerging datasets are useful because they provide near real-time information on population activity in sectors which are particularly dynamic and have previously been difficult to model, such as retail, leisure and transport. However, extracting useful intelligence from these sources, and integrating and calibrating them with existing data sources, poses significant challenges for researchers and practitioners seeking to employ them in the creation of time-specific population estimates. 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. It will deliver two substantive policy demonstrator case studies with the project partners. The first case study will demonstrate the potential for using time-specific population estimates for near real-time response in emergencies; the second will explore their usefulness for modelling variation in 'normal' population distributions through space and time in order to inform longer-term planning and policy formulation. Importantly, the project will also encourage the sharing of knowledge and expertise between academia and the public sector through joint design and implementation of the case studies, internal seminars and a jointly organised stakeholder workshop. Invitees to the workshop will be key stakeholders in policy and practice from within and beyond the partners' sectors. The workshop will showcase the data, methods and tools developed by the project, discuss the opportunities and challenges involved in implementing these for decision-making and policy formulation, and identify how such methods might realistically be scaled up within these sectors. Ultimately, the aim of the project is to help partners such as PHE, HSE and Dstl carry out their remits more effectively and efficiently through the provision of better time-specific population estimates.
