Understanding Urban Movements through Big Data and Social Simulation

这项研究将通过大数据分析和尖端的计算机模拟相结合，从根本上改变我们对城市日常运动模式的理解。它将开发产生数据的新方法，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，这场革命有可能改变我们对城市日常动态的理解，并可能对科学家进行社会科学研究的方式产生重大影响。关于城市居民的大量新数据正在被收集。新的服务正在通过使用社交媒体、公共交通系统和移动电话来捕捉人们日常行为的信息，仅举几例。来自这些来源的数据，尽管嘈杂、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，这些方法可以理解这些数据，并获得关于人们在空间和时间上的日常运动的信息。然后，它提议开发一种全市日常城市运动的计算机模拟，将从人群来源的数据流中自动校准。这项研究之所以重要，是因为之前试图对详细的城市运动进行建模的项目一直受到阻碍，原因是缺乏高分辨率数据，以及难以对最终成为城市特征的人与人之间复杂的个人层面互动进行建模的方法。大量资料来源，如人口普查，捕捉的是人口的属性和特征，而不是他们的态度和行为。另一方面，试图捕捉这些行为信息的详细调查自然会受到其规模和范围的限制。相比之下，新的“大数据”公共数据流数量庞大，包含有关用户位置的信息，以及通常描述用户行为或行动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幅比我们以前更准确的城市动态图景。这幅新图景将有能力改变我们对关键社会现象的理解，这些社会现象取决于人们在一天中的不同时间身处何处，而不仅仅是他们住在哪里。它将使用模拟输出来生成对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。这项研究将基于对潜在受害者群体的估计来重新分析犯罪率，而不仅仅是人们居住的地方。然后，考虑到当时该地区可能成为受害者的人数，这将向我们显示犯罪率高于或低于预期的地方。这将对减少犯罪政策产生明显影响，该项目将与警察和减少犯罪专家合作，以最大限度地利用成果。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，来计算人们暴露在空气污染中的程度。通常，人们的住所位置被用来估计他们对空气污染的易感性，但这忽略了这样一个事实，即许多人在外出时(例如上班、去商店等)会暴露在空气污染中。通过更准确地估计人们的接触，该项目可能会对欧盟/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。总而言之，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走动。然后，它将应用这一新知识，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。

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.