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. 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.

There will be an immediate impact on the non-academic project partners through the challenging of established methods of using static residential-based population estimates (e.g. from the census) for policy and practice. While our Co-investigators and their immediate research teams are aware of the problems associated with such an approach, and indeed some have developed alternative data and methods to try to deal with these, raising awareness of these issues in the broader organisation will encourage more critical thinking related to accuracy and assumptions. Other departments within the partners' organisations also stand to benefit from the project. Some (e.g. Air Pollution and Climate Change and Extreme Events and Health Protection Groups at PHE), are already interested in the existing Population247 methods and may therefore be invited to the stakeholder workshop.

During the currency of the grant, key impacts for partners will be the shared development of methods and tools and new knowledge/skills gained from activities such as internal seminars. All partners will gain new insights into the variation in population distribution under normal conditions, and how this might be updated in near real-time during emergencies. The policy demonstrator case studies will explore how these estimates can be used in practical and policy contexts. PHE, for example will evaluate the relative merits of evacuation versus sheltering during an emergency, while HSE are keen to explore how Population247 and their own National Population Database (NPD) can be integrated in order to enhance both approaches. Via its stakeholder workshop and other engagement activities, the project will also provide a realistic appraisal of the opportunities for, and barriers to, using the outputs from the project in policy, planning and response more broadly.

The partners are key stakeholders in various consortia, contributing and sharing data, methods, tools and evidence for the development of policy and practice. Examples include the Natural Hazards Partnership (http://www.naturalhazardspartnership.org.uk/) and Resilience Direct (https://www.ordnancesurvey.co.uk/business-and-government/case-studies/resilience-direct.html), both of which are concerned with providing effective and efficient response to crises and disruptive events. The project's concerns with establishing patterns of 'normal' population distribution and providing near real-time updates are particularly well-aligned with these consortia's aims. Similarly, the findings from the project will be of interest to users in other sectors, where the mapping and modelling of population is important, such as environmental management (e.g. Environment Agency), resource allocation (Clinical Commissioning Groups), planning (local authorities), as well as non-public sector users such as retail (Demographic Users Group).

In the mid- to longer-term, this project will contribute to better decision-making and policy formulation in a range of sectors by providing enhanced data, methods, tools and evidence for modelling population distribution in space and time. Ultimately, the key users who stand to benefit from this project are members of the public. The non-academic partners all have national remits within the health, emergency response and national security sectors. Fundamentally, we aim to save lives and money by enabling the partners to carry out their remits more effectively and efficiently. The project can be scaled up by applying the methods and tools to more new and emerging datasets, to other geographical areas (in order to achieve the full spatial coverage required in a policy context) and to other years (to extend the evidence-base). It can also be applied to other sectors which share the same need to model the population distribution more accurately. Finally, it can also be applied in other countries with suitable data infrastructures.