Agent-Based Modelling and Dynamic Data Assimilation for Modelling Urban Dynamics

从学术和实践的角度来看，确定鼓励和阻碍城市空间出勤的因素至关重要。对于政策制定者来说，了解是什么吸引人们来到市中心，又是什么让他们望而却步，对城市规划和应急管理至关重要。从学术角度来看，了解客流量的驱动因素对于回答流动性、包容性和机会可及性等问题至关重要。简单地量化客流量是极具挑战性的，更不用说剖析潜在的驱动因素了。虽然出现了一系列不同的“大”数据集，可以帮助估计客流量，但没有一个单独提供全面的情况。此外，没有标准的方法适合于吸收动态的、多样的、嘈杂的和有偏差的数据源来创建一个完整的图像。为了解决我们对城市动态理解中的这些差距，该项目将开展一项雄心勃勃的方法开发和实证数据分析计划。它将采用计算机科学（如机器学习和人工智能）、大气建模（如数据同化和集成建模）和地理（如GIS和空间分析）等领域的相关方法来创建一个稳健的人流量模型。支持分析的指示性数据源包括：利兹市议会闭路电视摄像机收集的人流量数据；人口普查工作日人口估计；动态天气资料；地理定位Twitter数据；公众活动及假日时段；营业时间；Wi-Fi传感器足迹数据；以及其他可用的资源。利用这些数据作为一种解释工具，了解是什么推动了城市中心的上座率，并作为一种预测工具，预测不同情况下的未来客流量，这是非常有潜力的。利兹市议会积极参与设计研究问题，确定数据来源，并将共同监督研究。该项目将纳入CDRC职权范围内的工作，提供基线和时间上细微差别的城市人口，并解释其驱动因素。它将被纳入利兹市议会的现实城市管理系统，以及更广泛的城市和未来城市文献。最后，它将作为未来应急规划和每日人口流动工作以及城市可达性长期预测工作的基础。

Identifying the factors that encourage and discourage attendance in urban spaces is vital from both academic and practitioner perspectives. For policy makers, an understanding of what attracts people to city centres, and what discourages them, is vital for urban planning and emergency management. From an academic perspective, understanding the drivers of footfall is essential in order to answer questions of mobility, inclusivity, and accessibility of opportunities.Simply quantifying footfall, let alone dissecting the underlying drivers, is extremely challenging. Although there are a series of diverse 'big' datasets that are emerging which can contribute to footfall estimates, none in isolation provide a comprehensive picture. In addition, there are no standard methods that are appropriate for assimilating dynamic, diverse, noisy, and biased data sources to create a complete picture.To address these gaps in our understanding of urban dynamics, this project will embark on an ambitious programme of methodological development and empirical data analysis. It will adapt relevant methods from fields such as computer science (e.g. machine learning and artificial intelligence), atmospheric modelling (e.g. data assimilation and ensemble modelling), and geography (e.g. GIS and spatial analysis) to create a robust model of footfall. Indicative data source that will underpin the analysis include: footfall data collected by Leeds City Council CCTV cameras; Census workday population estimates; dynamic weather data; geo-located Twitter data; times of public events and holidays; business opening hours; Wi-Fi sensor footfall data; and others as they become available. There is great potential to leverage these data as both an explanatory tool for understanding what has been driving city-centre attendance and as a predictive tool for forecasting future footfall under different scenarios. Leeds City Council are actively involved in designing the research questions, identifying data sources, and will jointly supervise the research. This project will feed into work across the CDRC remit, providing baseline and temporally nuanced urban populations, along with an explanation of their drivers. It will feed into real-world urban management systems at Leeds City Council, as well as broader cities and future cities literature. Finally, it will act as a foundation for future work in emergency planning and diurnal population movements, as well as longer-term predictions of city accessibility.