Bringing the Social City to the Smart City

技术发展，如GPS设备和Web 2.0技术的兴起，通过智能手机和社交媒体平台的大规模普及，在我们如何连接和共享信息方面创造了社会变革（Croitoru等人，2014）。这种新一代的移动的技术作为单个传感器工作，捕获以前隐藏的各种人类行为的数据。这些数据包括个人行动、偏好和意见。了解这些行为是至关重要的，如果我们要创建一个联合起来的方法来模拟城市如何呼吸和成长。然而，在适应和开发机器学习的新技术方面需要做大量工作，以提取可以嵌入尖端建模技术的行为。在代表"真实的“世界的”大“数据和产生现实替代版本的模拟之间建立这座桥梁，对于寻求为当今城市面临的许多挑战开发新解决方案的学者和政策制定者都具有价值。要做到这一点，我们需要了解“社会城市”中的因素（个人活动和决策的影响）每天在“智能城市”中的作用（从固定传感器收集的数据，例如交通量，空气污染或人口流动）。然而，标准的“智能”城市理解假设以前的流量（例如，一周中特定时间的交通，24小时内的能源需求或污染水平）将在未来复制，缺乏适应性（如果城市发生重大事件，这将如何改变？）和预测能力（如果禁止所有汽油和柴油车辆，对健康有什么影响？）。智能城市与社会城市之间的脱节意味着决策者无法获得复杂的相互关联的问题的答案，例如：什么是促进健康行为和减少城市碳足迹的最佳交通基础设施？能够回答这些问题越来越重要，因为城市正面临着与快速增长的城市人口压力相关的重大挑战。这些措施包括改善水和交通基础设施、空气污染和废物管理，以及提供适当的住房、能源、保健、教育和就业。未来城市面临的这些压力使智慧城市议程成为许多政府举措的主导，政府和政策制定者正在寻求新形式的（大）微观数据，为这些挑战提供创新的解决方案。虽然已经开发了许多模型来预测未来的交通、住房或医疗保健计划，但大多数用途都是纯粹的经验性的：它们缺乏对生成数据的个人背后的社会过程或其行动和决策的影响的任何考虑。该奖学金将探索如何使用机器学习启发的工具来识别微观数据源中的这种新兴模式和过程，例如个人如何移动和使用城市空间的数据。沿着基于智能体的模型的附加方法，它允许智能和社会城市数据很容易地结合起来，这套方法将通过研究（i）空气污染对个人的影响和（ii）城市流动性的案例研究来探索。这项工作将从根本上改变我们的能力，展示如何识别和理解智慧城市的社会元素，以及如何将“生活体验”带入智慧城市数据分析。

Technological developments, such as the rise in GPS enabled devices and Web 2.0 technologies have created social transformations in how we connect and share information through the mass uptake of smart phones and social media platforms (Croitoru et al, 2014). This new generation of mobile technologies work as individual sensors capturing data on a wide range of human behaviours that have been previously hidden. These include data on individual movement, preferences and opinions. Understanding these behaviours is crucial if we are to create a joined up approach to simulating how cities breath and grow. However, considerable work is required in adapting and developing new technologies from machine learning to extract behaviours which can be embedded into cutting-edge modelling techniques. Creating this bridge between 'big' data representing the 'real' world, and simulations producing alternative versions of reality is of value to both academics and policymakers looking to develop new solutions to many of the challenges that today's cities face. To do this we need to understand how factors within the "Social City" (the impact of individual movements and decisions) play out every day in the "Smart City" (data collected from fixed sensors on for example, traffic counts, air pollution or movements of populations). However, standard "Smart" City understanding assumes previous flows (e.g. traffic at a specific time of the week, energy requirements or pollution levels over a 24-hour period) will be replicated into the future, lacking both adaptability (how does this alter if a major event in the city is happening?) and predictive power (what is the impact on health if all petrol and diesel vehicles are banned?). This disconnection between the Smart and Social city means policymakers are unable to obtain answers to complex interrelated questions such as: what is the optimal transport infrastructure to promote healthy behaviours and reduce the City's carbon footprint?Being able to answer these questions is increasingly important as cities are facing significant challenges associated with the pressures from rapidly increasing urban populations. These include improving water and transportation infrastructure, air pollution and waste management as well as provision of adequate housing, energy, health care, education and employment. These pressures on future cities has brought the Smart City agenda to dominate many government initiatives with governments and policymakers looking to new forms of (big) micro data to provide innovative solutions to these challenges. While many models have been developed to forecast future transport, housing or healthcare initiatives, most uses are purely empirical: they lack any consideration of the social processes behind the individual generating the data or the impact of their actions and decisions. This Fellowship will explore how machine learning inspired tools can be used to recognise such emergent patterns and processes within micro-level data sources such as data on how individuals move and use city spaces. Along with the additional methodology of Agent Based Models, which allows smart and social city data to be readily combined, this suite of methods will be explored through looking at the case-studies of (i) the impact of air pollution on individuals and (ii) urban mobility. This work will fundamentally transform our ability to show how the social elements of the Smart City can be recognised and understood, and how to bring 'lived experience' to the analysis of Smart City data.