Representativeness of mobile phone location data for accurate measurement of population movement

手机位置数据的代表性，精准测算人口流动

• 批准号: 2573226
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2573226
• 关键词: Representativeness; mobile; phone; location; data

Human travel is used as a proxy for a range of social behaviors including migration, economic engagement, social connections, and the transmission of infectious disease. The widespread adoption of mobile phones has greatly enhanced scientists' ability to collect information on patterns of travel. While mobile phone location data provides undeniably useful information on empirical patterns of movement, this data may be collected from a sample of individuals which is not representative of the broader population which it is intended to represent. This research will address whether the sample of individuals included in mobile phone location data is biased with respect to spatial, behavioral, or demographic characteristics, and whether the movement captured in these datasets is an accurate proxy of specific social behaviors. This research will focus in particular on the use of mobile phone location data for predicting infectious disease transmission. Research Areas: human geography, demography, sociologyAims of the research:1. The principal aim is to understand how geographic, social, and economic factors influence movementbehaviour during the COVID-19 pandemic.2. I will explore the representativeness of movement data, and whether certain demographic groups ormovement types are under or overrepresented. Importantly, this will inform methods for generalising themovement of a sample of individuals to the behaviours of an entire population.3. I will examine the prediction of movement behaviour during emergencies and how these predictionscan be improved with information on the socio-demographic characteristics of populations.4. I will investigate how privacy preserving aggregation techniques (spatial and temporal) impactmovement indicators and the effect these indicators have on different socio-demographic groups.Detailed Research Questions: I will use movement data to quantify the varied geographic, social, andeconomic determinants of movement behaviour in the UK. As in my previous work, this research will bespecifically applied to understanding patterns of movement during public health emergencies, includingthe COVID-19 pandemic and associated interventions. The research will also make predictions ofmovement patterns and determine how demographic factors improve the performance of predictions ofhuman movements. Finally, the research will determine the influence of privacy preserving aggregationand censoring methods on the interpretation of human movement indicators.

人类旅行被用来衡量一系列社会行为，包括移民、经济参与、社会联系和传染病传播。移动电话的广泛采用极大地增强了科学家收集旅行模式信息的能力。虽然移动电话位置数据无可否认地提供了有关经验运动模式的有用信息，但该数据可能是从个体样本中收集的，而该样本并不代表其旨在代表的更广泛的人群。这项研究将解决手机位置数据中包含的个人样本在空间、行为或人口特征方面是否存在偏差，以及这些数据集中捕获的运动是否能够准确代表特定的社会行为。这项研究将特别关注使用手机位置数据来预测传染病的传播。研究领域：人文地理学、人口学、社会学 研究目的： 1．主要目的是了解地理、社会和经济因素如何影响 COVID-19 大流行期间的运动行为。2。我将探讨运动数据的代表性，以及某些人口群体或运动类型是否代表性不足或过多。重要的是，这将为将个体样本的运动推广到整个群体的行为提供方法。3。我将研究紧急情况期间运动行为的预测，以及如何利用有关人口社会人口特征的信息来改进这些预测。4。我将研究隐私保护聚合技术（空间和时间）如何影响运动指标以及这些指标对不同社会人口群体的影响。详细研究问题：我将使用运动数据来量化英国运动行为的各种地理、社会和经济决定因素。与我之前的工作一样，这项研究将专门应用于了解突发公共卫生事件期间的运动模式，包括 COVID-19 大流行和相关干预措施。该研究还将对运动模式进行预测，并确定人口因素如何提高人类运动预测的性能。最后，研究将确定隐私保护聚合和审查方法对人类运动指标解释的影响。