What makes a place busy? Characterising the spatiotemporal elements of the ambient population using a range of data sources

由伦敦大学学院地理学部和消费者数据研究中心(www.cdrc.ac.uk)提供的关于“什么让一个地方繁忙？使用一系列数据源表征周围人口的时空要素”这一主题的博士项目。首席导师是詹姆士·切希尔教授，副校长是罗致光博士。这项研究将与领先的人员流动咨询公司GHD Group Mobile Strategy联合进行，该公司提供将在本次project(https://www.ghd.com/en/about-us/ghd-movement-strategies.aspx).The中分析的流动性数据集。拟议的PHD旨在结合来自移动设备的基于个人的数据(基于GPS)和基于地点的数据测量足迹(通过闭路电视)，以加强城市地区周围人口的量化。这将使我更好地了解密集的城市情况，如大型体育场、交通枢纽和城市城镇中心，以产生为城市政策提供信息的证据。通过该项目，我将获得分析机动性和图像数据集的数据科学和机器学习技能，并开发新的技术来验证它们。这将有助于描述不同尺度上的地点的空间和时间人口概况。为利用和模拟感兴趣的数据集提供框架将有助于规划、设计和运营拥挤的场所，目标是使它们更安全、最大限度地增加容量、增强游客体验和增加盈利能力。例如，在地方层面，数据可用于监测交通枢纽和大型活动(体育场、节日、音乐会)和/或国家层面的移动密度和风险和异常，表征城市零售中心的健康状况。此外，通过博士论文和学术论文，我将把移动数据来源与城市地理和城市规划问题联系起来，目的是创造出对城市政策、智慧城市解决方案以及未来研究具有创新和影响的产出。因此，公共部门和私营部门都将从这一项目的结果中受益。

The PhD project on the topic "What makes a place busy? Characterising the spatiotemporal elements of the ambient population using a range of data sources" is offeredby the UCL Department of Geography and Consumer Data Research Centre(www.cdrc.ac.uk). The primary supervisor is Professor James Cheshire and thesecondary one is Dr Stephen Law. The research will be carried out in association withGHD Group Movement Strategies, a leading people movement consultancy, whichsupplies mobility datasets to be analysed in this project(https://www.ghd.com/en/about-us/ghd-movement-strategies.aspx).The proposed PhD aims to combine individual-based data from mobile devices (GPSbased) and place-based data measuring footfall (via CCTV) to enhance thequantification of ambient populations in urban areas. This will provide a betterunderstanding of dense urban situations such as large stadia, transport hubs, andurban town centres to generate evidence that informs urban policies.Through the programme, I will acquire skills in data science and machine learning foranalysing mobility and imagery datasets as well as develop novel techniques for theirvalidation. This will help to characterize the spatial and temporal population profiles ofplaces at different scales. Providing the framework for utilising and modelling thedatasets of interest will help in planning, designing, and operating crowded places withthe objectives of making them safer, maximising capacity, enhancing the visitorexperience, and increasing profitability. For instance, at the local scale, data may beused to monitor movement density and to detect risks and anomalies in transport hubsand large-scale events (stadia, festivals, concerts) and/or at the national scale,characterising the health of urban retail centres (volume, retail turn-over).Furthermore, through the PhD thesis and academic papers, I will link the mobility datasources to urban geography and urban planning issues, with the aim of creating outputthat is innovative and influential on urban policy, smart city solutions as well as futureresearch. Thus, both the public and private sectors would benefit from the findings ofthis project.