Big data: a new perspective on cities

R. Gallotti, Thomas Louail, Rémi Louf, M. Barthelemy
{"title":"Big data: a new perspective on cities","authors":"R. Gallotti, Thomas Louail, Rémi Louf, M. Barthelemy","doi":"10.1017/CBO9781316162750.010","DOIUrl":null,"url":null,"abstract":"The recent availability of large amounts of data for urban systems opens the exciting possibility of a new science of cities. These datasets can roughly be divided into three large categories according to their time scale. We will illustrate each category by an example on a particular aspect of cities. At small time scales (of order a day or less), mobility data provided by cell phones and GPS reveal urban mobility patterns but also provide information about the spatial organization of urban systems. At very large scales, the digitalization of historical maps allows us to study the evolution of infrastructure such as road networks, and permits us to distinguish on a quantitative basis self-organized growth from top-down central planning. Finally at intermediate time scales, we will show how socio-economical series provide a nice test for modeling and identifying fundamental mechanisms governing the structure and evolution of urban systems. All these examples illustrate, at various degrees, how the empirical analysis of data can help in constructing a theoretically solid approach to urban systems, and to understand the elementary mechanisms that govern urbanization leaving out specific historical, geographical, social, or cultural factors. At this period of human history that experiences rapid urban expansion, such a scientific approach appears more important than ever in order to understand the impact of current urban planning decisions on the future evolution of cities. Big data and urban systems A common trait shared by all complex systems – including cities – is the existence of a large variety of processes occurring over awide range of time and spatial scales.The main obstacle to the understanding of these systems therefore resides at least in uncovering the hierarchy of processes and in singling out the few that govern their dynamics. Albeit difficult, the hierarchization of processes is of prime importance. A failure to do so leads either to modelswhich are too complex to give any real insight into the phenomenon or to be validated, or too simple to provide a satisfactory framework which can be built upon. As a matter of fact, despite numerous attempts [1–6], a theoretical understanding of many observed empirical regularities in cities is still missing. This situation is, however, changing with the recent availability of an unprecedented amount of data about cities and their inhabitants.","PeriodicalId":415319,"journal":{"name":"Big Data over Networks","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data over Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/CBO9781316162750.010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The recent availability of large amounts of data for urban systems opens the exciting possibility of a new science of cities. These datasets can roughly be divided into three large categories according to their time scale. We will illustrate each category by an example on a particular aspect of cities. At small time scales (of order a day or less), mobility data provided by cell phones and GPS reveal urban mobility patterns but also provide information about the spatial organization of urban systems. At very large scales, the digitalization of historical maps allows us to study the evolution of infrastructure such as road networks, and permits us to distinguish on a quantitative basis self-organized growth from top-down central planning. Finally at intermediate time scales, we will show how socio-economical series provide a nice test for modeling and identifying fundamental mechanisms governing the structure and evolution of urban systems. All these examples illustrate, at various degrees, how the empirical analysis of data can help in constructing a theoretically solid approach to urban systems, and to understand the elementary mechanisms that govern urbanization leaving out specific historical, geographical, social, or cultural factors. At this period of human history that experiences rapid urban expansion, such a scientific approach appears more important than ever in order to understand the impact of current urban planning decisions on the future evolution of cities. Big data and urban systems A common trait shared by all complex systems – including cities – is the existence of a large variety of processes occurring over awide range of time and spatial scales.The main obstacle to the understanding of these systems therefore resides at least in uncovering the hierarchy of processes and in singling out the few that govern their dynamics. Albeit difficult, the hierarchization of processes is of prime importance. A failure to do so leads either to modelswhich are too complex to give any real insight into the phenomenon or to be validated, or too simple to provide a satisfactory framework which can be built upon. As a matter of fact, despite numerous attempts [1–6], a theoretical understanding of many observed empirical regularities in cities is still missing. This situation is, however, changing with the recent availability of an unprecedented amount of data about cities and their inhabitants.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
大数据:城市的新视角
最近,大量城市系统数据的出现,为一门新的城市科学开辟了令人兴奋的可能性。这些数据集根据其时间尺度大致可以分为三大类。我们将通过一个城市特定方面的例子来说明每个类别。在小时间尺度(一天或更短),手机和GPS提供的移动数据揭示了城市移动模式,但也提供了关于城市系统空间组织的信息。在非常大的尺度上,历史地图的数字化使我们能够研究道路网络等基础设施的演变,并使我们能够在定量的基础上区分自组织增长和自上而下的中央计划。最后,在中间时间尺度上,我们将展示社会经济序列如何为建模和识别控制城市系统结构和演变的基本机制提供一个很好的测试。所有这些例子都在不同程度上说明了数据的实证分析如何有助于构建一个理论上可靠的方法来研究城市系统,并帮助理解在忽略特定历史、地理、社会或文化因素的情况下控制城市化的基本机制。在这个经历城市快速扩张的人类历史时期,为了了解当前城市规划决策对城市未来演变的影响,这种科学方法显得比以往任何时候都更加重要。包括城市在内的所有复杂系统的一个共同特征是,存在着在广泛的时间和空间尺度上发生的各种各样的过程。因此,理解这些系统的主要障碍至少在于揭示过程的层次结构,并挑出少数控制其动态的过程。尽管困难重重,但过程的分层是最重要的。如果做不到这一点,要么会导致模型过于复杂,无法提供任何对现象的真正洞察或验证,要么过于简单,无法提供一个可以构建的令人满意的框架。事实上,尽管进行了许多尝试[1-6],但对城市中观察到的许多经验规律的理论理解仍然缺失。然而,随着最近有关城市及其居民的大量数据的出现,这种情况正在发生变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Sparsity-aware distributed learning Big data processing for smart grid security Tensor models: solution methods and applications Inference of gene networks associated with the host response to infectious disease A unified distributed algorithm for non-cooperative games
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1