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

博士项目的主题是“是什么让一个地方忙碌？使用一系列数据源表征周围人群的时空要素”由伦敦大学学院地理和消费者数据研究中心（www.cdrc.ac.uk）提供。主要导师是詹姆斯柴郡教授和theseconstructionone是斯蒂芬博士罗。这项研究将与GHD集团运动战略，一个领先的人员流动咨询公司，提供流动数据集进行分析，在这个项目（https：//www.ghd.com/en/about-us/ghd-movement-strategies.aspx）。拟议的博士学位旨在联合收割机个人为基础的数据，从移动的设备（基于GPS）和基于地点的数据测量客流量（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、交通枢纽和城市镇中心，以生成为城市政策提供信息的证据。通过该项目，我将获得数据科学和机器学习方面的技能，用于分析移动和图像数据集，并开发新的验证技术。这将有助于在不同尺度上描述不同地方的人口时空分布特征。提供利用和建模感兴趣的数据集的框架将有助于规划，设计和运营拥挤的地方，使其更安全，最大限度地提高容量，增强游客体验和增加盈利能力。例如，在地方一级，数据可用于监测移动密度，并检测交通枢纽和大规模事件中的风险和异常情况（体育场、节日、音乐会）和/或在全国范围内，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，我将把流动性问题与城市地理和城市规划问题联系起来，其目的是创造对城市政策、智慧城市解决方案以及未来研究具有创新性和影响力的成果。因此，公共和私营部门都将受益于该项目的调查结果。

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.