Bringing the Social City to the Smart City

将社会城市带入智慧城市

• 批准号: ES/R007918/1
• 负责人: Alison Heppenstall
• 金额: $ 29.68万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FR007918%2F1
• 关键词: Bringing; Social; City; Smart

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.

技术发展，如支持GPS的设备和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，为我们连接和共享信息的方式带来了社会变革(Croitoru等人，2014)。这一新一代移动技术作为单独的传感器工作，捕捉以前隐藏的各种人类行为的数据。这些数据包括个人运动、偏好和意见的数据。如果我们要创建一种联合的方法来模拟城市的呼吸和发展，理解这些行为是至关重要的。然而，在适应和开发机器学习的新技术以提取可以嵌入到尖端建模技术中的行为方面，需要做大量的工作。在代表“真实”世界的“大数据”和生成现实的替代版本的模拟之间建立这种桥梁，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来说都是有价值的。要做到这一点，我们需要了解“社会城市”内的因素(个人行动和决策的影响)在“智能城市”中每天是如何发挥作用的(从固定传感器收集的数据，例如，交通流量、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，一周特定时间段的交通、能源需求或24小时内的污染水平)将被复制到未来，缺乏这两者的适应性(如果城市正在发生重大事件，这将如何改变？)以及预测力(如果禁止所有汽油车和柴油车，对健康有何影响？)智能城市和社交城市之间的这种脱节意味着，政策制定者无法获得复杂的相互关联的问题的答案，例如：促进健康行为和减少城市碳足迹的最佳交通基础设施是什么？随着城市面临与快速增长的城市人口压力相关的重大挑战，能够回答这些问题变得越来越重要。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，以及提供足够的住房、能源、保健、教育和就业。未来城市面临的这些压力导致智慧城市议程主导了许多政府举措，政府和政策制定者希望通过新形式的(大数据)微数据为这些挑战提供创新的解决方案。尽管已经开发了许多模型来预测未来的交通、住房或医疗保健举措，但大多数用途纯粹是经验的：它们缺乏对生成数据的个人背后的社会过程或他们的行动和决定的影响的任何考虑。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，例如关于个人如何移动和使用城市空间的数据。除了基于代理的模型的附加方法，它允许智能和社交城市数据容易地结合在一起，这套方法将通过观察(I)空气污染对个人的影响和(Ii)城市机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到智慧城市数据的分析中。

项目成果

Agent-Based Modelling and Geographical Information Systems: A Practical Primer

基于代理的建模和地理信息系统：实用入门

• 发表时间: 2018
• 作者: Crooks Andrew

What do you want theory for? - A pragmatic analysis of the roles of "theory" in agent-based modelling

• DOI: 10.1016/j.envsoft.2023.105802
• 发表时间: 2023-10
• 期刊: Environ. Model. Softw.
• 作者: Patrycja Antosz;Daniel Birks;B. Edmonds;A. Heppenstall;R. Meyer;J. Gareth Polhill;D. O'Sullivan;Nanda Wijermans

Adjustment for time-invariant and time-varying confounders in 'unexplained residuals' models for longitudinal data within a causal framework and associated challenges.

在因果关系框架和相关挑战中，调整了“无法解释的残差”模型中的时间不变和时变的混杂因素。

• DOI: 10.1177/0962280218756158
• 发表时间: 2019-05
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Arnold KF;Ellison G;Gadd SC;Textor J;Tennant P;Heppenstall A;Gilthorpe MS
• 通讯作者: Gilthorpe MS

Developing an Individual-level Geodemographic Classification

• DOI: 10.1007/s12061-017-9233-7
• 发表时间: 2018-09-01
• 期刊: APPLIED SPATIAL ANALYSIS AND POLICY
• 影响因子: 1.9
• 作者: Burns, Luke;See, Linda;Birkin, Mark
• 通讯作者: Birkin, Mark

Modeling agent decision and behavior in the light of data science and artificial intelligence

根据数据科学和人工智能对代理决策和行为进行建模

• DOI: 10.1016/j.envsoft.2023.105713
• 发表时间: 2023
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: An L