Understanding Urban Movements through Big Data and Social Simulation

通过大数据和社会模拟了解城市运动

• 批准号: ES/L009900/1
• 负责人: Nick Malleson
• 金额: $ 31.54万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FL009900%2F1
• 关键词: Understanding; Urban; Movements; through; Big

This research will fundamentally alter our understanding of daily urban movement patterns through a combination of 'big data' analysis and cutting-edge computer simulation. It will develop new methods to produce data that will help us to address key issues in crime and health.A big data "revolution" is underway that has the potential to transform our understanding of daily urban dynamics and could have big impacts on the ways that scientists conduct social science research. Vast quantities of new data are being gathered about people in cities. New services are capturing information about peoples' daily actions from their use of social media, public transport systems and mobile telephones, to name a few. Data from these sources, although noisy, messy and biased are unprecedented in their scope, scale and resolution. This research will first develop new geographical methods that can make sense of these data and derive information about peoples' daily movements in space and time. It then proposes to develop a computer simulation of city-wide daily urban movements that will be calibrated automatically from streams of crowd-sourced data. This research is important because previous projects that have attempted to model detailed urban movements have been hampered by a lack of high-resolution data and by methods that have difficulty in modelling the complex individual-level interactions of people that ultimately characterise cities. Large-volume sources, such as censuses, capture attributes and characteristics of the population, rather than their attitudes and behaviours. On the other hand, detailed surveys that attempt to capture this behavioural information are naturally limited by their size and scope. In contrast, new 'big' public data streams are voluminous and contain information about a user's location as well as a textual or multimedia component that often describes their behaviour or actions. The new simulation model will make use of these data to create a much more accurate picture of urban dynamics than we have had before now.This new picture will have the capacity to alter our understanding of key social phenomena that depend on where people are at different times of day, rather than simply where they live. It will use the simulation outputs to generate new estimates of where people are and apply these estimates to two empirical areas:1. Crime. The research will re-analyse crime rates based on estimates of where groups of potential victims are, rather than simply where people live. This will then show us where crime is higher or lower than expected, given the number of people who are in the area at the time and might be victimised. This will have obvious impacts for crime reduction policies and the project will work with the police and crime-reduction experts to make the best use of the results.2. Health. The second project will calculate peoples' exposure to air pollution based on where they actually spend their time, rather than where they live. Normally, peoples' home location is used to estimate how susceptible they are to air pollution, but this ignores the fact that many people will be exposed whilst away from home (e.g. going to work, travelling to the shops, etc.). By more accurately estimating peoples' exposure, this project could have substantial impacts on EU/UK air quality laws and lead to an overall improvement in national health.In summary, this project will make use of new 'big' data and advanced computer simulation to better understand how people move around cities. It will then apply this new knowledge to try to better understand rates of crime and to assess the impacts of air pollution on peoples' health.

这项研究将通过“大数据”分析和尖端计算机模拟相结合，从根本上改变我们对日常城市运动模式的理解。它将开发新的方法来产生数据，帮助我们解决犯罪和健康方面的关键问题。一场大数据“革命”正在进行中，它有可能改变我们对日常城市动态的理解，并可能对科学家进行社会科学研究的方式产生重大影响。人们正在收集有关城市居民的大量新数据。新的服务正在从人们使用社交媒体、公共交通系统和移动的电话等方面获取有关他们日常行动的信息。来自这些来源的数据虽然嘈杂、混乱和有偏见，但其范围、规模和分辨率都是前所未有的。这项研究将首先开发新的地理方法，使这些数据有意义，并获得有关人们在空间和时间上的日常活动的信息。然后，它建议开发全市日常城市运动的计算机模拟，该模拟将根据众包数据流自动校准。这项研究很重要，因为以前试图模拟详细的城市运动的项目受到了缺乏高分辨率数据的阻碍，并且难以模拟最终使城市繁荣的人们复杂的个人层面互动的方法。人口普查等大规模来源捕捉的是人口的属性和特点，而不是他们的态度和行为。另一方面，试图获取这种行为信息的详细调查自然受到其规模和范围的限制。相比之下，新的“大”公共数据流是海量的，包含有关用户位置的信息以及通常描述其行为或动作的文本或多媒体组件。新的模拟模型将利用这些数据来创建一个比我们以前拥有的更准确的城市动态图像。这个新图像将有能力改变我们对关键社会现象的理解，这些现象取决于人们在一天中不同时间的位置，而不仅仅是他们居住的地方。它将使用模拟输出来生成人们所在位置的新估计，并将这些估计应用于两个经验领域：1。犯罪这项研究将根据对潜在受害者群体所在地的估计，而不仅仅是人们居住的地方，重新分析犯罪率。这将告诉我们犯罪率高于或低于预期，考虑到当时在该地区的人数，可能会被取消。这将对减少犯罪政策产生明显的影响，该项目将与警方和减少犯罪专家合作，以最佳方式利用成果。健康第二个项目将根据人们实际花费时间的地方而不是他们居住的地方来计算人们暴露于空气污染的程度。通常情况下，人们的家庭位置被用来估计他们对空气污染的敏感程度，但这忽略了一个事实，即许多人在离家时（例如去上班，去商店等）会受到空气污染。通过更准确地估计人们的暴露量，该项目可能对欧盟/英国的空气质量法律产生重大影响，并导致国民健康的全面改善。总之，该项目将利用新的“大”数据和先进的计算机模拟来更好地了解人们如何在城市中移动。然后，它将应用这些新知识，试图更好地了解犯罪率，并评估空气污染对人们健康的影响。

项目成果

How big data and The Sims are helping us to build the cities of the future

大数据和模拟人生如何帮助我们建设未来的城市

• 发表时间: 2015
• 期刊: The Conversation
• 作者: A. Heppenstall

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

• DOI: 10.1016/j.simpat.2021.102386
• 发表时间: 2021-08-11
• 期刊: SIMULATION MODELLING PRACTICE AND THEORY
• 影响因子: 4.2
• 作者: Clay, Robert;Ward, Jonathan A.;Malleson, Nick
• 通讯作者: Malleson, Nick

Predicting Pedestrian Counts using Machine Learning

使用机器学习预测行人数量

• DOI: 10.5194/agile-giss-4-18-2023
• 发表时间: 2023
• 期刊: GIScience Series
• 作者: Asher M

Crime at Places and Spatial Concentrations: Exploring the Spatial Stability of Property Crime in Vancouver BC, 2003-2013

• DOI: 10.1007/s10940-016-9295-8
• 发表时间: 2017-06-01
• 期刊: JOURNAL OF QUANTITATIVE CRIMINOLOGY
• 影响因子: 3.6
• 作者: Andresen, Martin A.;Linning, Shannon J.;Malleson, Nick
• 通讯作者: Malleson, Nick

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