Population247NRT: Near real-time spatiotemporal population estimates for health, emergency response and national security

Population247NRT：针对健康、应急响应和国家安全的近实时时空人口估计

• 批准号: ES/P010768/1
• 负责人: Samantha Cockings
• 金额: $ 23.58万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FP010768%2F1
• 关键词: Population247NRT; Near; real; time; spatiotemporal

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.

在卫生、应急/危机应对和国家安全等部门的决策和政策制定方面，理想情况下需要关于特定地点在一天、一周、一季或一年的特定时间的人口数量的准确动态信息。传统的人口普查数据不提供这种程度的详细信息，但经常用于此类政策和规划目的。ESRC资助的人口247研究方案(Martin等人，2015年)开发了一个框架、方法和软件工具(SurfaceBuilder247)，用于整合不同的当代数据来源，为小的地理区域提供改进的、具体时间的人口估计。自那以后，通过与HR Wallingford和英国公共卫生组织的合作，它在洪水和辐射应急反应/规划方面的有效性已经得到证明。这些模式主要涉及整合居住地、工作地点、教育和保健等活动的开放行政数据。现在，新的和新兴的数据形式，如传感器数据、通过互联网提供的实时和静态数据馈送，以及以前没有的各种商业数据集，为加强这些人口估计提供了令人兴奋的机会。这些新的和新兴的数据集是有用的，因为它们提供了关于零售、休闲和运输等特别活跃和以前难以建模的部门的人口活动的近乎实时的信息。然而，从这些来源中提取有用的情报，并将其与现有的数据来源进行整合和校准，对于寻求利用它们来创建特定时间的人口估计的研究人员和从业者来说是一个巨大的挑战。该项目将结合新的、新兴的和现有的数据集，以产生增强的特定时间的人口估计，以便在卫生、紧急/危机应对和国家安全部门进行更知情的决策和政策制定。这是英国南安普顿大学(PHE)、卫生与安全主管(HSE)和国防科学与技术实验室(DSTL)合作的项目。该项目将加强现有的方法和工具，以收集、处理、整合和校准新的、新出现的和现有的数据源，以产生特定时间的人口估计数。它将与项目伙伴一起提供两个实质性的政策示范案例研究。第一个案例研究将展示利用特定时间的人口估计在紧急情况下作出近实时反应的潜力；第二个案例研究将探讨它们在模拟“正常”人口分布在空间和时间上的变化方面的有用性，以便为较长期的规划和政策制定提供信息。重要的是，该项目还将通过联合设计和实施案例研究、内部研讨会和联合举办的利益攸关方研讨会，鼓励学术界和公共部门之间分享知识和专业知识。研讨会的受邀者将是伙伴部门内外的政策和实践方面的主要利益攸关方。讲习班将展示该项目开发的数据、方法和工具，讨论在决策和政策制定中实施这些方法所涉及的机会和挑战，并确定如何切实在这些部门内推广这些方法。归根结底，该项目的目的是通过提供更好的具体时间人口估计数，帮助公共卫生部门、卫生与公众服务部和卫生与公众服务部等合作伙伴更有效、更高效地执行任务。