Download Hi-Res Image 下载高分辨率图片Download to MS-PowerPoint
下载至 MS-PowerPointCite This: 引用此文：Environ. Sci. Technol. 环境科学与技术 2024, 58, 10, 4617-4626
下载至 MS-PowerPointCite This: 引用此文：Environ. Sci. Technol. 环境科学与技术 2024, 58, 10, 4617-4626
Your access to this publication has been provided by Learn More
Sustainable Systems
可持续系统 2024 年 2 月 28 日
可持续系统 2024 年 2 月 28 日
Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation
通过整合强降水期间的人类移动大数据来指导城市洪水风险适应Click to copy article link
点击复制文章链接Article link copied!SWJTU A简介SCI升级版 环境科学与生态学1区SCI Q1IF 10.8
点击复制文章链接Article link copied!
- Jiacong Cai 蔡家聪Jiacong CaiState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaMore by Jiacong Cai
- Jianxun Yang* 杨建勋*Jianxun Yang 杨建勋*Email: yangjx@nju.edu.cnState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
污染控制与资源化研究国家重点实验室，环境学院，南京大学，南京 210023，中国Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
江苏大气环境与装备技术协同创新中心，南京信息工程大学，江苏南京 210044
*电子邮箱：yangjx@nju.edu.cnMore by Jianxun Yang 更多来自杨建勋的文章 - Miaomiao Liu* 刘淼淼*Miaomiao Liu 刘淼淼*Email: liumm@nju.edu.cn *电子邮箱：liumm@nju.edu.cnState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
污染控制与资源化研究国家重点实验室，环境学院，南京大学，南京 210023，中国Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
江苏大气环境与装备技术协同创新中心，南京信息工程大学，江苏南京 210044More by Miaomiao Liu 更多作品 由 刘淼淼 - Wen Fang 文芳Wen FangState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaMore by Wen Fang
- Zongwei Ma 马宗伟Zongwei MaState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaMore by Zongwei Ma
- Jun Bi 俊比Jun BiState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, ChinaJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaMore by Jun Bi
Abstract 摘要
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between −3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.
理解强降水对人类移动性的影响对于精细化城市洪水风险评估及实现可持续发展目标#11，即建设韧性安全城市至关重要。本研究利用 2018 年夏季在中国江苏省收集的约 260 万条手机信号数据，提出了一种新颖框架，以高空间粒度（500 米网格单元）评估降雨事件期间人类移动性的变化。精细尺度的移动性图谱识别出异常聚集或人类活动减少的空间热点。在市级层面汇总结果显示，人类移动性变化范围在-3.6%至 8.9%之间，揭示了城市间不同的内部移动模式。分段结构方程模型分析进一步表明，城市规模、交通系统和拥挤程度直接影响移动性响应，而经济状况则通过多条间接路径影响移动性。当叠加历史城市洪水地图时，我们发现这种人类移动性变化有助于 23 个城市减少覆盖 0.45 百万人口的 2.6%洪水风险，但在 12 个城市中平均增加了覆盖 0.21 百万人口的 1.64%洪水风险。 研究结果有助于深化我们对强降水事件后城市居民流动模式的认识，并通过支持更高效的小规模灾害管理来促进城市适应性。
This publication is licensed under the terms of your
institutional subscription.
Request reuse permissions.
本出版物根据您所在机构的订阅条款获得许可。请求重用权限。
Copyright © 2024 American Chemical Society
版权 © 2024 美国化学学会
版权 © 2024 美国化学学会
Special Issue 特刊
Published as part of Environmental Science & Technology virtual special issue “Accelerating Environmental Research to Achieve Sustainable Development Goals.”
作为《环境科学与技术》虚拟特刊“加速环境研究以实现可持续发展目标”的一部分发表。
Synopsis 概要
This study proposes a framework to assess fine-scale changes in human mobility and urban flood risks during heavy rainfall events by integrating mobile phone big data.
本研究提出一个框架，通过整合手机大数据来评估强降雨事件期间人类流动性与城市洪水风险的细粒度变化。
1. Introduction 1. 引言
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
Cities around the globe have witnessed a growing number of flooding events and their catastrophic impacts in the past few decades. (1,2) Urban flood inundation happens when rainfall overwhelms the capacity of the drainage system. (3) Floods result in drastic damage to urban infrastructure including the water supply system, energy transmission and distribution facilities, and the transport system. (4) Between 1980 and 2013, the global direct economic losses due to floods exceeded $1 trillion and 220 000 people lost their lives. (5) It is estimated that average global flood losses will increase to over $60 billion per year in 2050 under projected socioeconomic and climate change pathways. (6) Without adaptive strategies, the frequency and intensity of urban floods are expected to continuously increase. (7−9)
过去几十年间，全球各地城市见证了越来越多的洪灾事件及其灾难性影响。（1,2）当降雨量超过排水系统的承载能力时，城市洪涝便随之发生。（3）洪水对城市基础设施造成严重破坏，包括供水系统、能源输送与分配设施以及交通系统。（4）1980 年至 2013 年间，全球因洪水导致的直接经济损失超过 1 万亿美元，并有 22 万人丧生。（5）据估计，在预期的社会经济和气候变化路径下，到 2050 年，全球平均每年洪水损失将增至 600 亿美元以上。（6）若无适应性策略，城市洪水的频率和强度预计将持续上升。（7−9）
过去几十年间，全球各地城市见证了越来越多的洪灾事件及其灾难性影响。（1,2）当降雨量超过排水系统的承载能力时，城市洪涝便随之发生。（3）洪水对城市基础设施造成严重破坏，包括供水系统、能源输送与分配设施以及交通系统。（4）1980 年至 2013 年间，全球因洪水导致的直接经济损失超过 1 万亿美元，并有 22 万人丧生。（5）据估计，在预期的社会经济和气候变化路径下，到 2050 年，全球平均每年洪水损失将增至 600 亿美元以上。（6）若无适应性策略，城市洪水的频率和强度预计将持续上升。（7−9）
Sustainable development goal (SDG) #11 (building sustainable cities and communities), introduced in 2015 alongside the other 16 goals by the United Nations, is grounded in addressing critical global issues. (10) These goals serve as a collective blueprint for promoting peace and prosperity for people and the planet, both presently and in the future. (11) SDG #11 focuses on creating cities and human settlements that are inclusive, safe, resilient, and sustainable. This goal places substantial emphasis on improving the resilience of cities to natural and climate change-induced disasters. (12) Effective adaptation strategies are urgently needed to manage urban floods and buffer their impacts, thereby advancing the objectives of SDG #11. (13,14)
联合国于 2015 年提出的可持续发展目标（SDG）第 11 项（建设可持续城市与社区），与其他 16 项目标并列，旨在应对全球性关键问题。这些目标共同构成了促进人类与地球当前及未来和平与繁荣的集体蓝图。SDG 第 11 项着重于打造包容、安全、有抵御能力且可持续的城市及人类住区，特别强调提升城市对自然及气候变化引发灾害的抵御能力。为实现 SDG 第 11 项目标，亟需采取有效的适应策略来管理城市洪涝，减轻其影响。
联合国于 2015 年提出的可持续发展目标（SDG）第 11 项（建设可持续城市与社区），与其他 16 项目标并列，旨在应对全球性关键问题。这些目标共同构成了促进人类与地球当前及未来和平与繁荣的集体蓝图。SDG 第 11 项着重于打造包容、安全、有抵御能力且可持续的城市及人类住区，特别强调提升城市对自然及气候变化引发灾害的抵御能力。为实现 SDG 第 11 项目标，亟需采取有效的适应策略来管理城市洪涝，减轻其影响。
Various interventions can be explored for urban flood adaptation, such as waterproof infrastructure, early warning systems, nature-based solutions, and risk financing schemes. Fine-scale mapping of flood risk is a fundamental prerequisite for performing all these adaptive measures in a targeted way. (15,16) It helps identify hotspots and drivers of inundation and supports high-efficiency resource allocation. Previous studies have proposed indicator frameworks to map urban flood risks, measured as the product of flood hazard, vulnerability, and exposure. (17,18) Despite these efforts, data availability is always an obstacle for further applying index-based approaches at finer spatial scales. (19) Moreover, setting indices and weights is a subjective process. Existing studies focus heavily on infrastructural and socioeconomic factors that may affect vulnerability. Influencing factors of exposure are often overlooked, failing to reflect the situation in the real world. Human mobility, for example, a variable that shapes dynamic urban flood exposure of residents, has not been comprehensively examined in existing flood risk mapping frameworks. (20,21)
城市洪水适应性措施可探索多种干预手段，如防水基础设施、预警系统、基于自然的解决方案及风险融资计划。精细尺度的洪水风险图是实施这些针对性适应措施的基本前提。（15,16）它有助于识别淹没热点与驱动因素，并支持高效资源配置。先前研究已提出指标框架来绘制城市洪水风险图，该风险被定义为洪水危险性、脆弱性和暴露度的乘积。（17,18）尽管如此，数据可获取性始终是进一步在更细空间尺度应用基于指数方法的障碍。（19）此外，设定指标及其权重过程具有主观性。现有研究侧重于可能影响脆弱性的基础设施与社会经济因素，而暴露度的影响因素常被忽视，未能真实反映现实情况。例如，塑造居民动态城市洪水暴露度的人类流动性变量，在现有洪水风险制图框架中未得到全面审视。（20,21）
城市洪水适应性措施可探索多种干预手段，如防水基础设施、预警系统、基于自然的解决方案及风险融资计划。精细尺度的洪水风险图是实施这些针对性适应措施的基本前提。（15,16）它有助于识别淹没热点与驱动因素，并支持高效资源配置。先前研究已提出指标框架来绘制城市洪水风险图，该风险被定义为洪水危险性、脆弱性和暴露度的乘积。（17,18）尽管如此，数据可获取性始终是进一步在更细空间尺度应用基于指数方法的障碍。（19）此外，设定指标及其权重过程具有主观性。现有研究侧重于可能影响脆弱性的基础设施与社会经济因素，而暴露度的影响因素常被忽视，未能真实反映现实情况。例如，塑造居民动态城市洪水暴露度的人类流动性变量，在现有洪水风险制图框架中未得到全面审视。（20,21）
Urban human mobility reflects the interaction process between individuals and physical settings in the city, offering a new perspective on urban flood risk mapping and adaptation. (22,23) Heavy rain events alter human activity patterns. When rainfall hits, urban residents may change their routine, shift to more usage of public transport, seek shelters and take refuge in concrete buildings, cancel their trips, or just stay in their cars due to road closures. (20) Such spontaneous intracity movement, and consequently uneven population distribution, may reshape the spatial patterns of flooding exposure within the urban area. When a large number of people gather in risk-prone places such as low buildings and crowded roads, the proportion of the population exposed to floods can potentially increase as a result. (24) Therefore, tracking human mobility when heavy rainfall and flood events occur is critical to understanding dynamic mappings of urban flood risks.
城市人类移动性反映了个人与城市物理环境之间的互动过程，为城市洪水风险制图与适应提供了新的视角。（22,23）强降雨事件会改变人类活动模式。当降雨来袭时，城市居民可能会改变日常行为，增加公共交通的使用，寻找避难所并躲入坚固建筑中，取消出行计划，或因道路封闭而滞留车内。（20）这种自发的城市内部移动及其导致的非均匀人口分布，可能会重塑城市区域内洪水暴露的空间格局。当大量人群聚集在低矮建筑和拥挤道路等高风险地点时，遭受洪水威胁的人口比例可能会因此上升。（24）因此，在强降雨和洪水事件发生时追踪人类移动性，对于理解城市洪水风险的动态映射至关重要。
城市人类移动性反映了个人与城市物理环境之间的互动过程，为城市洪水风险制图与适应提供了新的视角。（22,23）强降雨事件会改变人类活动模式。当降雨来袭时，城市居民可能会改变日常行为，增加公共交通的使用，寻找避难所并躲入坚固建筑中，取消出行计划，或因道路封闭而滞留车内。（20）这种自发的城市内部移动及其导致的非均匀人口分布，可能会重塑城市区域内洪水暴露的空间格局。当大量人群聚集在低矮建筑和拥挤道路等高风险地点时，遭受洪水威胁的人口比例可能会因此上升。（24）因此，在强降雨和洪水事件发生时追踪人类移动性，对于理解城市洪水风险的动态映射至关重要。
Current index-based assessment frameworks make it difficult to measure the effect of human mobility in response to heavy rainfall on urban flood risks. Most studies chose proxy variables to represent a city’s capacity to tolerate flooding and maintain large-volume population mobility, such as access to public transport infrastructure, road densities, or percent population owing vehicles. (25,26) These measures do not capture trajectories of urban population during extreme weather events and reflect an overall status over a long period. To identify flood risk hotspots induced by intracity population movement, it is necessary to trace human mobility patterns during the time span of heavy precipitation events.
当前基于指标的评估框架难以衡量人类流动性在应对强降雨时对城市洪水风险的影响。多数研究选择代理变量来代表城市承受洪灾并维持大规模人口流动的能力，如公共交通基础设施的可达性、道路密度或拥有车辆的人口比例。（25,26）这些指标未能捕捉极端天气事件期间城市人口流动的轨迹，而是反映了一个较长时期内的整体状况。为了识别由城市内部人口移动引发的洪水风险热点，有必要在强降水事件的时间跨度内追踪人类流动性模式。
当前基于指标的评估框架难以衡量人类流动性在应对强降雨时对城市洪水风险的影响。多数研究选择代理变量来代表城市承受洪灾并维持大规模人口流动的能力，如公共交通基础设施的可达性、道路密度或拥有车辆的人口比例。（25,26）这些指标未能捕捉极端天气事件期间城市人口流动的轨迹，而是反映了一个较长时期内的整体状况。为了识别由城市内部人口移动引发的洪水风险热点，有必要在强降水事件的时间跨度内追踪人类流动性模式。
In recent years, increasing accessibility to real-time geolocated big data, such as mobile phone records, check-ins of online social media, and GPS traces of vehicles, has enabled researchers to model human mobility patterns at an unprecedented spatial and temporal resolution. (27,28) Utilizing massive human trajectory data emerges as a research frontier in the field of disaster risk management. (29) These data sets are powerful to track and explain human behaviors after emergent events or disasters. For instance, geolocated big data have been employed to monitor migration, recovery of small businesses, and public emotional responses after natural hazards. (30,31) Regarding the effect of heavy precipitations and urban floods on the citizens’ mobility, several studies attempted to quantify the magnitude of impacts or the ability to recover from the perturbation. (32) Preliminary conclusions drawn from these studies suggest that rainfalls may have a reducing effect on the citizen’s trip flows, distance, and duration. However, very few of them take a step further to estimate how human mobility leads to highly heterogeneous population distribution and, consequently, results in dynamic urban flood risk exposure. Another major gap in the literature is that these studies mostly focus on a single city or a few case cities, which makes it hard to explain variations across cities and recommend practical and effective adaptive interventions for other regions. (33,34)
近年来，随着实时地理位置大数据（如手机记录、在线社交媒体签到和车辆 GPS 轨迹）的日益普及，研究人员得以以前所未有的时空分辨率模拟人类流动模式。(27,28) 利用海量人类轨迹数据成为灾害风险管理领域的一个研究前沿。(29) 这些数据集在追踪和解释紧急事件或灾害后的人类行为方面具有强大能力。例如，地理位置大数据已被用于监测自然灾害后的迁移、小企业恢复及公众情绪反应。(30,31) 针对强降水和城市洪水对市民流动性的影响，多项研究尝试量化其影响程度或恢复能力。(32) 这些研究的初步结论表明，降雨可能减少市民的出行流量、距离和时长。 然而，鲜有研究进一步估算人类流动如何导致高度异质性的人口分布，进而引发动态的城市洪水风险暴露。文献中的另一个主要缺口在于，这些研究大多聚焦于单一城市或少数案例城市，这使得解释城市间的差异并为其他地区推荐切实有效的适应性干预措施变得困难。 (33,34)
近年来，随着实时地理位置大数据（如手机记录、在线社交媒体签到和车辆 GPS 轨迹）的日益普及，研究人员得以以前所未有的时空分辨率模拟人类流动模式。(27,28) 利用海量人类轨迹数据成为灾害风险管理领域的一个研究前沿。(29) 这些数据集在追踪和解释紧急事件或灾害后的人类行为方面具有强大能力。例如，地理位置大数据已被用于监测自然灾害后的迁移、小企业恢复及公众情绪反应。(30,31) 针对强降水和城市洪水对市民流动性的影响，多项研究尝试量化其影响程度或恢复能力。(32) 这些研究的初步结论表明，降雨可能减少市民的出行流量、距离和时长。 然而，鲜有研究进一步估算人类流动如何导致高度异质性的人口分布，进而引发动态的城市洪水风险暴露。文献中的另一个主要缺口在于，这些研究大多聚焦于单一城市或少数案例城市，这使得解释城市间的差异并为其他地区推荐切实有效的适应性干预措施变得困难。 (33,34)
To close the research gaps in urban flood risk mapping and inform adaptation at finer scales, this study developed a novel detrending framework and pictured rainfall-induced mobility perturbation at a high spatial resolution (500 m grid cell), leveraging a data set of ∼2.6 million mobile phone records obtained during 2018 summer in Jiangsu Province, China. Emerging hot–cold spots of mobility variation were identified, revealing the shocks in citizens’ daily routines facing heavy precipitation events. We then aggregated grid estimates on the urban scale and employed piecewise SEM to elucidate the variations in population mobility changes across cities. Finally, we superimposed a historical flood inundation map onto the mobility map to reveal how mobility changes reshape urban flood risk exposure. It is anticipated that our findings will help decision-makers better understand the mobility pattern of urban citizens impacted by heavy rainfall events, which can support more efficient adaptation resource allocation and accelerate progress in achieving SDGs #11.
为了填补城市洪水风险制图的研究空白并指导更精细尺度的适应策略，本研究开发了一种新颖的去趋势化框架，并利用 2018 年夏季江苏省约 260 万条手机记录数据集，以高空间分辨率（500 米网格单元）描绘了降雨引发的移动性扰动。识别出移动性变化的“热点”与“冷点”，揭示了市民在面对强降水事件时日常活动的冲击。随后，我们将网格估计值聚合到城市尺度，并采用分段结构方程模型（SEM）阐明了城市间人口移动变化的模式。最后，将历史洪水淹没图叠加到移动性图上，揭示了移动性变化如何重塑城市洪水风险暴露。预期我们的发现将帮助决策者更好地理解受强降雨事件影响的市民移动模式，从而支持更有效的适应资源配置，并加速实现可持续发展目标（SDG）第 11 项的进程。
为了填补城市洪水风险制图的研究空白并指导更精细尺度的适应策略，本研究开发了一种新颖的去趋势化框架，并利用 2018 年夏季江苏省约 260 万条手机记录数据集，以高空间分辨率（500 米网格单元）描绘了降雨引发的移动性扰动。识别出移动性变化的“热点”与“冷点”，揭示了市民在面对强降水事件时日常活动的冲击。随后，我们将网格估计值聚合到城市尺度，并采用分段结构方程模型（SEM）阐明了城市间人口移动变化的模式。最后，将历史洪水淹没图叠加到移动性图上，揭示了移动性变化如何重塑城市洪水风险暴露。预期我们的发现将帮助决策者更好地理解受强降雨事件影响的市民移动模式，从而支持更有效的适应资源配置，并加速实现可持续发展目标（SDG）第 11 项的进程。
2. Literature Review 2. 文献综述
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
Geolocated big data have radically changed the ways through which human societies manage natural disasters due to their varied possibilities in pattern mining and trend prediction. To track advances in related fields and disclose research gaps, we briefly review the applications of geolocated big data (especially mobile phone data) in tracking human mobility and improving natural hazard adaptation. (30−32)
基于地理位置的大数据因其模式挖掘与趋势预测的多样可能性，从根本上改变了人类社会应对自然灾害的方式。为追踪相关领域的进展并揭示研究空白，本文简要回顾了地理位置大数据（尤其是手机数据）在追踪人类流动与提升自然灾害适应性方面的应用。（30−32）
基于地理位置的大数据因其模式挖掘与趋势预测的多样可能性，从根本上改变了人类社会应对自然灾害的方式。为追踪相关领域的进展并揭示研究空白，本文简要回顾了地理位置大数据（尤其是手机数据）在追踪人类流动与提升自然灾害适应性方面的应用。（30−32）
Geolocated big data are often user-generated and provide precise location information on individual users. These extensive data sets are primarily sourced from personal mobile devices, including social media posts, call detail records, and web search queries. Among all types of geolocated data sets, mobile phone location data are advantageous in their rapid and high-frequency data collection, stable longitudinal time frame, and wide population coverage. This stability in data collection ensures that mobile phone signals can predict human mobility with remarkable accuracy even in chaotic situations, reaching levels as high as 95%. (35,36) This feature enables researchers to observe, estimate, and model human digital geographical footprints at unprecedented granularity. We emphasize three pivotal areas in which mobile phone data can deepen our knowledge of human mobility and hazard adaptation.
地理定位大数据通常由用户生成，提供个人用户的精确位置信息。这些庞大的数据集主要来源于个人移动设备，包括社交媒体帖子、通话详单记录和网络搜索查询。在所有类型的地理定位数据集中，手机位置数据因其快速高频的数据采集、稳定的纵向时间框架和广泛的人口覆盖而具有优势。这种数据采集的稳定性确保了即使在混乱情况下，手机信号也能以高达 95%的准确度预测人类流动。(35,36) 这一特性使研究人员能够以前所未有的精细度观察、估计和模拟人类的数字地理足迹。我们强调手机数据在三个关键领域能够深化我们对人类流动性和灾害适应性的认识。
地理定位大数据通常由用户生成，提供个人用户的精确位置信息。这些庞大的数据集主要来源于个人移动设备，包括社交媒体帖子、通话详单记录和网络搜索查询。在所有类型的地理定位数据集中，手机位置数据因其快速高频的数据采集、稳定的纵向时间框架和广泛的人口覆盖而具有优势。这种数据采集的稳定性确保了即使在混乱情况下，手机信号也能以高达 95%的准确度预测人类流动。(35,36) 这一特性使研究人员能够以前所未有的精细度观察、估计和模拟人类的数字地理足迹。我们强调手机数据在三个关键领域能够深化我们对人类流动性和灾害适应性的认识。
2.1. Tracking Mobility Response After Small-Scale Shocks
2.1. 小规模冲击后的移动性响应追踪
Most geolocated data sets, without enough temporal density and stable monitoring of individual users (e.g., social media posts), have focused on mobility tracking after unique large-scale disasters. However, some small-scale events, such as traffic jams, crowding, and also rainfall anomalies involved in this study, may only cause short-term mobility variations in small-scale regions but may lead to intensive exposure to risks. In this case, mobile phone signal data are more capable of capturing the dynamics due to their higher spatial–temporal granularity. For example, Perazzini et al. (37) leveraged mobile phone data to estimate people crowding and traffic intensity in small urban areas, which can help effectively mitigate exposure to natural disasters while ensuring the quality of life at the “small area” level.
大多数地理位置数据集，由于缺乏足够的时间密度和对个体用户（如社交媒体帖子）的稳定监测，主要集中在独特大规模灾难后的移动性追踪上。然而，某些小规模事件，如交通拥堵、人群聚集以及本研究涉及的降雨异常，虽仅导致小范围区域的短期移动性变化，却可能引发密集的风险暴露。在此情况下，移动电话信号数据因其更高的时空粒度，更能捕捉这些动态变化。例如，Perazzini 等人（37）利用移动电话数据评估了小型城市区域的人群聚集和交通强度，这有助于在确保“小区域”生活质量的同时，有效减轻自然灾害的风险暴露。
大多数地理位置数据集，由于缺乏足够的时间密度和对个体用户（如社交媒体帖子）的稳定监测，主要集中在独特大规模灾难后的移动性追踪上。然而，某些小规模事件，如交通拥堵、人群聚集以及本研究涉及的降雨异常，虽仅导致小范围区域的短期移动性变化，却可能引发密集的风险暴露。在此情况下，移动电话信号数据因其更高的时空粒度，更能捕捉这些动态变化。例如，Perazzini 等人（37）利用移动电话数据评估了小型城市区域的人群聚集和交通强度，这有助于在确保“小区域”生活质量的同时，有效减轻自然灾害的风险暴露。
2.2. Monitoring Natural or Human-Made Intervention Effects
2.2. 监测自然或人为干预效应
Another important feature of mobile phone position data is that they are collected automatically and at high frequency, which ensures a stable, continuous, and adequate number of data samples. Therefore, they are useful for evaluating the intervention effects of both natural events and external human manipulations. For example, Qian et al. (32) examined rainstorms and geotagged behaviors (measured by smartphone location data) in eight cities. They found city residents’ mobility anomalies disrupted by rainstorms are significant, and the sensitivity varies in different cities. Mobile phone data are also used to examine the controlling effect of mobility restriction during the recent coronavirus (COVID-19) pandemic. Lai et al. (38) analyzed anonymous mobile phones in Shenzhen, China, and showed that various types and magnitudes of lockdowns are effective in curbing COVID-19 outbreaks.
移动电话位置数据的另一个重要特征是它们能够自动且高频率地收集，确保了数据样本的稳定、连续和充足。因此，这些数据对于评估自然事件和人为干预措施的影响效果非常有用。例如，Qian 等人(32)研究了八个城市的暴雨事件与地理标记行为（通过智能手机位置数据衡量）。他们发现暴雨导致的市民移动异常现象显著，且不同城市的敏感度各异。移动电话数据还被用于考察近期新冠病毒（COVID-19）大流行期间移动限制的控制效果。Lai 等人(38)分析了中国深圳的匿名移动电话数据，并表明不同类型和强度的封锁措施在遏制 COVID-19 疫情爆发方面均有效。
移动电话位置数据的另一个重要特征是它们能够自动且高频率地收集，确保了数据样本的稳定、连续和充足。因此，这些数据对于评估自然事件和人为干预措施的影响效果非常有用。例如，Qian 等人(32)研究了八个城市的暴雨事件与地理标记行为（通过智能手机位置数据衡量）。他们发现暴雨导致的市民移动异常现象显著，且不同城市的敏感度各异。移动电话数据还被用于考察近期新冠病毒（COVID-19）大流行期间移动限制的控制效果。Lai 等人(38)分析了中国深圳的匿名移动电话数据，并表明不同类型和强度的封锁措施在遏制 COVID-19 疫情爆发方面均有效。
2.3. Explaining Influencing Factors of Human Mobility
2.3. 解释人类流动性的影响因素
Understanding why people change mobility patterns is crucial in predicting individual mobility trajectories and making interventions at an early stage. (36,39) Since mobile phone data provide continuous observation of individual user’s specific behavior, it serves as a link to understand mobility decision-making in real time. (40) For example, González et al. (41) analyzed the trajectory of 100 000 anonymized mobile phone users and found that human trajectories show a high degree of temporal and spatial regularity. Buckee et al. (42) discussed the potential of using mobile phone data as useful proxies for behavioral drivers of disease transmission and investigated social, economic, and cultural forces to shape patterns of exposure. Leveraging these advantages of mobile phone big data, in this study, we attempt to address research existing gaps in urban flood risk mapping by quantifying human mobility changes triggered by small-scale natural intervention events (i.e., heavy precipitation), explaining driving factors across cities, and informing urban flood risk adaptation.
理解人们改变移动模式的原因对于预测个体移动轨迹及早期干预至关重要。（36,39）由于手机数据提供了对个体用户特定行为的持续观察，它成为实时理解移动决策的桥梁。（40）例如，González 等人（41）分析了 10 万名匿名手机用户的轨迹，发现人类轨迹显示出高度的时间和空间规律性。Buckee 等人（42）探讨了利用手机数据作为疾病传播行为驱动因素的有用代理的潜力，并研究了塑造暴露模式的社会、经济和文化力量。借助手机大数据的这些优势，本研究试图通过量化由小规模自然干预事件（如强降水）引发的人类移动变化，解释跨城市的驱动因素，并指导城市洪水风险适应，来填补城市洪水风险制图中的研究空白。
理解人们改变移动模式的原因对于预测个体移动轨迹及早期干预至关重要。（36,39）由于手机数据提供了对个体用户特定行为的持续观察，它成为实时理解移动决策的桥梁。（40）例如，González 等人（41）分析了 10 万名匿名手机用户的轨迹，发现人类轨迹显示出高度的时间和空间规律性。Buckee 等人（42）探讨了利用手机数据作为疾病传播行为驱动因素的有用代理的潜力，并研究了塑造暴露模式的社会、经济和文化力量。借助手机大数据的这些优势，本研究试图通过量化由小规模自然干预事件（如强降水）引发的人类移动变化，解释跨城市的驱动因素，并指导城市洪水风险适应，来填补城市洪水风险制图中的研究空白。
3. Methods and Materials 3. 方法与材料
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
3.1. Mapping Changes in Human Mobility Patterns After Heavy Rainfalls
3.1. 强降雨后人类移动模式的变化映射
3.1.1. Study Region 3.1.1. 研究区域
Located on the eastern coast of China, Jiangsu stands out as one of the highly developed provinces (see the location and population in Figure S1). By the year 2020, Jiangsu province had achieved a remarkable gross domestic product (GDP) of around 1.6 trillion USD, placing it on par with significant global economies, such as Canada, Russia, and South Korea. Notwithstanding its elevated level of development, Jiangsu is exposed to significant risks attributed to its low elevation and comparatively high annual average rainfall, typically ranging from 800 to 1200 mm. (43) The distinctive geographical and climatic attributes position Jiangsu Province as the optimal choice for our primary research focus.
位于中国东部沿海的江苏省，以其高度发达的经济地位引人注目（地理位置及人口分布见图 S1）。截至 2020 年，江苏省实现了约 1.6 万亿美元的惊人国内生产总值（GDP），与加拿大、俄罗斯及韩国等全球重要经济体并驾齐驱。尽管发展水平显著，江苏却因其地势低洼及相对较高的年均降水量（通常在 800 至 1200 毫米之间）而面临重大风险。(43) 这些独特的地理与气候特征，使得江苏省成为我们首要研究对象的理想之选。
位于中国东部沿海的江苏省，以其高度发达的经济地位引人注目（地理位置及人口分布见图 S1）。截至 2020 年，江苏省实现了约 1.6 万亿美元的惊人国内生产总值（GDP），与加拿大、俄罗斯及韩国等全球重要经济体并驾齐驱。尽管发展水平显著，江苏却因其地势低洼及相对较高的年均降水量（通常在 800 至 1200 毫米之间）而面临重大风险。(43) 这些独特的地理与气候特征，使得江苏省成为我们首要研究对象的理想之选。
3.1.2. Mobile Phone Signaling Data Collection and Preprocessing
3.1.2. 手机信令数据采集与预处理
We obtained anonymized mobile phone cellular signaling data from China Telecom, one of the largest telecommunication companies in China. The research design and the use of human-related signaling data involving privacy issues have gained ethical approval from the Research Committee of the School of Environment, Nanjing University (NO. NJUSE20230613). China Telecom owns ∼28 million mobile phone users in Jiangsu, accounting for ∼35% of its total population. (44) The anonymized data set included 2.6 million records from 90 000 mobile phone towers in Jiangsu and covered the period from June 1, 2018, to September 30, 2018 (see example data in Table S1, locations of towers in Figure S4), during which time the middle and lower reaches of the Yangtze River in China had already entered the monsoon season and were hit by urban flooding due to excessively rainfall. (45)
我们从中国电信获取了匿名的手机蜂窝信号数据，中国电信是中国最大的电信公司之一。该研究设计及涉及隐私问题的人类相关信号数据使用已获得南京大学环境学院研究委员会的伦理批准（编号：NJUSE20230613）。中国电信在江苏省拥有约 2800 万移动电话用户，占其总人口的约 35%。（44）该匿名数据集包含来自江苏省 90000 个移动电话基站的 260 万条记录，涵盖了 2018 年 6 月 1 日至 2018 年 9 月 30 日的时间段（示例数据见表 S1，基站位置见图 S4），在此期间，中国长江中下游地区已进入梅雨季节，并因降雨过多遭受城市内涝。（45）
我们从中国电信获取了匿名的手机蜂窝信号数据，中国电信是中国最大的电信公司之一。该研究设计及涉及隐私问题的人类相关信号数据使用已获得南京大学环境学院研究委员会的伦理批准（编号：NJUSE20230613）。中国电信在江苏省拥有约 2800 万移动电话用户，占其总人口的约 35%。（44）该匿名数据集包含来自江苏省 90000 个移动电话基站的 260 万条记录，涵盖了 2018 年 6 月 1 日至 2018 年 9 月 30 日的时间段（示例数据见表 S1，基站位置见图 S4），在此期间，中国长江中下游地区已进入梅雨季节，并因降雨过多遭受城市内涝。（45）
Each mobile phone tower recorded user requests, including calls, messages, Internet access, and other mobile phone uses per hour per day. If a person makes multiple accesses to the same tower within 1 h, it is recorded as a single visit. Figure S2 showed that daily visits had a significant (R = 0.69, p < 0.001) correlation with the grid population, demonstrating that our data set had a good representation of the local population and human mobility patterns.
每个移动电话塔记录了每小时每天的用户请求，包括通话、短信、互联网接入及其他手机使用情况。若某人在 1 小时内多次访问同一塔台，则记录为一次访问。图 S2 显示，每日访问量与网格人口存在显著（R = 0.69，p < 0.001）相关性，表明我们的数据集很好地反映了当地人口及人类流动模式。
每个移动电话塔记录了每小时每天的用户请求，包括通话、短信、互联网接入及其他手机使用情况。若某人在 1 小时内多次访问同一塔台，则记录为一次访问。图 S2 显示，每日访问量与网格人口存在显著（R = 0.69，p < 0.001）相关性，表明我们的数据集很好地反映了当地人口及人类流动模式。
We then aggregated the signal data from hourly to daily excluding the time window from 0:00 am to 6:00 am, as it had been pointed out in the literature that nighttime rains had no significant impact on human activities. (32) To remove the effect of short-term weekly fluctuations as well as the long-term increasing trend of mobile phone users, we further applied the Seasonal Hybrid Extreme Studentized Deviate algorithm to remove these trend components. (46) More details of this method can be found in the Text S1.
随后，我们将信号数据从每小时汇总至每日，排除了凌晨 0:00 至 6:00 的时间窗口，因为文献指出夜间降雨对人类活动影响不大。(32) 为消除短期周波动及长期移动电话用户增长趋势的影响，我们进一步采用季节性混合极端学生化偏差算法来剔除这些趋势成分。(46) 该方法的更多细节可在补充资料 S1 中查阅。
随后，我们将信号数据从每小时汇总至每日，排除了凌晨 0:00 至 6:00 的时间窗口，因为文献指出夜间降雨对人类活动影响不大。(32) 为消除短期周波动及长期移动电话用户增长趋势的影响，我们进一步采用季节性混合极端学生化偏差算法来剔除这些趋势成分。(46) 该方法的更多细节可在补充资料 S1 中查阅。
3.1.3. Identifying Heavy Rainfall Events
3.1.3. 识别强降雨事件
To identify rainfall events, we first obtained hourly simulated precipitation data derived from the European Centre for Medium-Range Weather Forecasting with a spatial resolution of 0.25° (∼25 km). (47) The grid-level simulated precipitation data were validated against the monitoring station data set, which showed a high correlation (R = 0.82, p < 0.001) (Figure S3(A)). We then simply aggregated the hourly rainfall data to daily and only kept heavy rainfall events with accumulated 24 h rainfall of more than 25 mm, according to the standards set by the China Meteorological Administration. (48) In the end, we recognized a total of 1082 rainfall events in 37 days. Figure S3 presents the frequency of rainfall events.
为了识别降雨事件，我们首先获取了欧洲中期天气预报中心提供的空间分辨率为 0.25°（约 25 公里）的小时模拟降水数据。（47）通过将网格级模拟降水数据与监测站数据集进行验证，结果显示高度相关（R = 0.82，p < 0.001）（图 S3(A)）。随后，我们将小时降雨数据简单汇总至日尺度，并仅保留中国气象局标准规定的累计 24 小时降雨量超过 25 毫米的强降雨事件。（48）最终，我们识别出 37 天内共 1082 次降雨事件。图 S3 展示了降雨事件的频率。
为了识别降雨事件，我们首先获取了欧洲中期天气预报中心提供的空间分辨率为 0.25°（约 25 公里）的小时模拟降水数据。（47）通过将网格级模拟降水数据与监测站数据集进行验证，结果显示高度相关（R = 0.82，p < 0.001）（图 S3(A)）。随后，我们将小时降雨数据简单汇总至日尺度，并仅保留中国气象局标准规定的累计 24 小时降雨量超过 25 毫米的强降雨事件。（48）最终，我们识别出 37 天内共 1082 次降雨事件。图 S3 展示了降雨事件的频率。
3.1.4. Quantifying Changes in Human Mobility at the 500 m × 500 m Grid Level
3.1.4. 量化 500 米×500 米网格级别的人类流动性变化
Mobile phone towers are typically hundreds of meters apart in urban areas. (49) To this end, we developed a framework to quantify changes in human mobility patterns for each mobile phone tower. The process was schematically presented in Figure 1.
城市地区，手机信号塔通常相隔数百米。为此，我们开发了一个框架，用于量化每个手机信号塔所反映的人类移动模式变化。该过程的示意图已在图 1 中展示。
城市地区，手机信号塔通常相隔数百米。为此，我们开发了一个框架，用于量化每个手机信号塔所反映的人类移动模式变化。该过程的示意图已在图 1 中展示。
Figure 1 图 1
Figure 1. Data processing steps that calculate grid-level mobility changes after heavy rainfalls. (A) Spatial overlay of cell tower, 500 m fishnet grid, and 25 km precipitation grid. (B) Conceptualization of the process to calculate tower-level visit changes due to the shock of rainfall events.
图 1. 计算暴雨后网格级移动性变化的数据处理步骤。(A) 基站、500 米渔网网格与 25 公里降水网格的空间叠加。(B) 概念化降雨事件冲击下计算基站级访问变化的过程。
We first assigned each tower to a precipitation grid (25 km) based on its geo-coordinates. Then, for a specific rainfall event, we calculated a five-day moving average visit for each cell tower to reflect an average activity trend without the shock of rainfall, as shown in eq 1,
我们首先根据各基站的地理坐标，将其分配至一个降水网格（25 公里）。随后，针对某一特定降雨事件，我们计算了每个基站五日的移动平均访问量，以反映平均活动趋势，避免降雨冲击的影响，如公式 1 所示，
我们首先根据各基站的地理坐标，将其分配至一个降水网格（25 公里）。随后，针对某一特定降雨事件，我们计算了每个基站五日的移动平均访问量，以反映平均活动趋势，避免降雨冲击的影响，如公式 1 所示，
(1)
Specifically, visit(i,t) is the number of visits recorded by tower i on rainy day t. We assumed that human activity near a cell phone signal tower will keep a linear trend as time goes by without disturbance of the rainfall event, as shown in Figure 1(B). Hence, visittrend(i,t), which calculates the mean tower visits in five consecutive days (including 2 days before and 2 days after the rainfall event), can serve as a baseline in the absence of heavy rain. We also performed sensitivity analysis by using 3- and 7-day moving averages as the calculation baseline and removing multiday consecutive rainfall events. The results, as reported in Figure S7, indicate that the 5-day moving average derives the most robust result. At the same time, we also compared whether the data on the rainy day should be included in the calculation of the baseline; see Texts S2 and S4 for details.
具体而言，visit(i,t)表示在雨天 t 时塔记录的访问次数。我们假设在没有降雨事件干扰的情况下，靠近手机信号塔的人类活动将随时间保持线性趋势，如图 1(B)所示。因此，visittrend(,t)计算了包括降雨事件前后各两天在内的连续五天的平均塔访问量，可在无大雨情况下作为基准。我们还通过采用 3 天和 7 天移动平均作为计算基准，并剔除连续多日的降雨事件，进行了敏感性分析。如图 S7 所示，结果表明 5 天移动平均得出的结果最为稳健。同时，我们也探讨了雨天数据是否应纳入基准计算的问题，详见文本 S2 和 S4。
具体而言，visit(i,t)表示在雨天 t 时塔记录的访问次数。我们假设在没有降雨事件干扰的情况下，靠近手机信号塔的人类活动将随时间保持线性趋势，如图 1(B)所示。因此，visittrend(,t)计算了包括降雨事件前后各两天在内的连续五天的平均塔访问量，可在无大雨情况下作为基准。我们还通过采用 3 天和 7 天移动平均作为计算基准，并剔除连续多日的降雨事件，进行了敏感性分析。如图 S7 所示，结果表明 5 天移动平均得出的结果最为稳健。同时，我们也探讨了雨天数据是否应纳入基准计算的问题，详见文本 S2 和 S4。
Next, to show the impact of specific rainfall events on human activity, we used eq 2 to get visitshock(i,t), the percentage changes between the baseline visits and the rainy day visits of tower i.
接下来，为了展示特定降雨事件对人类活动的影响，我们利用公式 2 计算了 visitshock(i,t)，即塔楼在雨天与基准日访问量之间的百分比变化。
接下来，为了展示特定降雨事件对人类活动的影响，我们利用公式 2 计算了 visitshock(i,t)，即塔楼在雨天与基准日访问量之间的百分比变化。
(2)
Also, it is worth noting that each cell tower might experience several precipitation events in the time span of our research. We simply calculated the average change in visits for each tower, as shown in eq 3, where N is the total number of rainfall events that have occurred covering tower i. This indicator, on the other hand, can reflect the overall perturbations of human mobility in the surrounding area when rainfall events occur. Large variations in cell phone tower visits indicated that the region is more sensitive to the impact of local rainfall events.
此外，值得注意的是，在我们研究的时间跨度内，每个基站可能会经历多次降水事件。我们简单计算了每个基站的访问量平均变化，如公式 3 所示，其中 N 表示覆盖该基站的总降雨事件数。另一方面，这一指标能够反映降雨事件发生时周边区域人类移动性的整体扰动情况。基站访问量的大幅波动表明该地区对局部降雨事件的影响更为敏感。
此外，值得注意的是，在我们研究的时间跨度内，每个基站可能会经历多次降水事件。我们简单计算了每个基站的访问量平均变化，如公式 3 所示，其中 N 表示覆盖该基站的总降雨事件数。另一方面，这一指标能够反映降雨事件发生时周边区域人类移动性的整体扰动情况。基站访问量的大幅波动表明该地区对局部降雨事件的影响更为敏感。
(3)
Finally, in order to compile a mobility map for the whole province at a high spatial resolution, we manually created a 500 m × 500 m grid layer and joined each tower to a corresponding grid cell. We chose this spatial resolution because the radius of the tower signal coverage is about 500 m. (49) Also, a grid cell might contain multiple towers in a populous urban area. We therefore directly calculated the mean tower-level visit shocks in each grid cell to measure human mobility responses to heavy rainfall events at the 500 m × 500 m grid level. This high-resolution map could help identify hotspots of changes in human mobility within the city.
最后，为了编制全省高空间分辨率的流动性图，我们手动创建了一个 500 米×500 米的网格层，并将每个基站对应到相应的网格单元中。我们选择这一空间分辨率是因为基站信号覆盖的半径大约为 500 米。（49）此外，在人口密集的城市区域，一个网格单元可能包含多个基站。因此，我们直接计算了每个网格单元内基站级别访问冲击的平均值，以衡量在 500 米×500 米网格级别上人类流动性对强降雨事件的响应。这种高分辨率地图有助于识别城市内人类流动性变化的热点区域。
最后，为了编制全省高空间分辨率的流动性图，我们手动创建了一个 500 米×500 米的网格层，并将每个基站对应到相应的网格单元中。我们选择这一空间分辨率是因为基站信号覆盖的半径大约为 500 米。（49）此外，在人口密集的城市区域，一个网格单元可能包含多个基站。因此，我们直接计算了每个网格单元内基站级别访问冲击的平均值，以衡量在 500 米×500 米网格级别上人类流动性对强降雨事件的响应。这种高分辨率地图有助于识别城市内人类流动性变化的热点区域。
3.2. Explaining Prefecture-Level Variations in Human Mobility Change
3.2. 解释地级市层面人类流动性变化的差异
After mapping human activity variations after rainfall events, we attempted to reveal key factors that drive different levels of mobility change across cities. It is not feasible to explain mobility changes at the grid cell level due to the lack of information at such high resolution and the risk of model overfitting because of the large samples. Therefore, we aggregated grid human mobility changes to the prefecture level according to urban administrative boundaries, (50) trying to identify influencing factors at the city level. It should be noted that mobility changes in a city may not be balanced, so positive aggregated values may indicate abnormal crowding and minus aggregated values indicate overall reduced activity. Considering that sparsely populated grid cells might disproportionately contribute to our observations, we used the grid population as a weight in the aggregation process. A total of 53 urban areas in Jiangsu province were included here to build up models and explain factors that may determine the difference in mobility changes across cities. Detailed city location, administrative boundary, and background information were presented in Figure S4 and Table S2.
在绘制降雨事件后人类活动变化图谱的基础上，我们试图揭示驱动城市间不同程度移动性变化的关键因素。由于高分辨率下信息匮乏及大量样本带来的模型过拟合风险，解释网格单元层面的移动性变化并不可行。因此，我们依据城市行政边界，将网格人类移动性变化汇总至地级市层面，（50）旨在识别城市层面的影响因素。需注意的是，城市内的移动性变化可能并不均衡，正汇总值可能指示异常拥挤，而负汇总值则表明整体活动减少。考虑到人口稀疏的网格单元可能对观察结果产生不成比例的影响，我们在汇总过程中采用了网格人口作为权重。江苏省内共 53 个城市区域被纳入，以构建模型并解释可能决定城市间移动性变化差异的因素。城市具体位置、行政边界及背景信息详见图 S4 与表 S2。
在绘制降雨事件后人类活动变化图谱的基础上，我们试图揭示驱动城市间不同程度移动性变化的关键因素。由于高分辨率下信息匮乏及大量样本带来的模型过拟合风险，解释网格单元层面的移动性变化并不可行。因此，我们依据城市行政边界，将网格人类移动性变化汇总至地级市层面，（50）旨在识别城市层面的影响因素。需注意的是，城市内的移动性变化可能并不均衡，正汇总值可能指示异常拥挤，而负汇总值则表明整体活动减少。考虑到人口稀疏的网格单元可能对观察结果产生不成比例的影响，我们在汇总过程中采用了网格人口作为权重。江苏省内共 53 个城市区域被纳入，以构建模型并解释可能决定城市间移动性变化差异的因素。城市具体位置、行政边界及背景信息详见图 S4 与表 S2。
Our primary goal is to elucidate the heterogeneity in human mobility across cities. In order to scrutinize and model the intricate relationships among multiple variables concurrently, we employed the piecewise SEM to assess the connections between indicators (see the rationale for selecting piecewise SEM in Text S3). The use of piecewise SEM enables the breakdown of the overall path into a sequence of structured equations. This transition from global to local estimation provides flexibility for fitting diverse distributions and adapting to various sampling designs. (51) Moreover, from a theoretical standpoint, it facilitates the fitting of smaller data sets, aligning well with the specific conditions of this study. (52) We focused on four latent variables, namely, “city size,” “transportation,” “crowding,” and “economy.” The conceptual model is reported with the result in Figure 3.
我们的主要目标是阐明城市间人类流动性的异质性。为了同时细致审查和建模多个变量之间错综复杂的关系，我们采用了分段结构方程模型（SEM）来评估指标间的联系（选择分段 SEM 的理由详见文本 S3）。分段 SEM 的使用使得能够将整体路径分解为一系列结构化方程，这种从全局到局部的估计转变为适应不同分布和采样设计提供了灵活性。（51）此外，从理论角度来看，它有助于适应较小数据集的拟合，与本研究的特定条件非常吻合。（52）我们重点关注了四个潜在变量，即“城市规模”、“交通”、“拥挤度”和“经济”。概念模型及其结果在图 3 中报告。
我们的主要目标是阐明城市间人类流动性的异质性。为了同时细致审查和建模多个变量之间错综复杂的关系，我们采用了分段结构方程模型（SEM）来评估指标间的联系（选择分段 SEM 的理由详见文本 S3）。分段 SEM 的使用使得能够将整体路径分解为一系列结构化方程，这种从全局到局部的估计转变为适应不同分布和采样设计提供了灵活性。（51）此外，从理论角度来看，它有助于适应较小数据集的拟合，与本研究的特定条件非常吻合。（52）我们重点关注了四个潜在变量，即“城市规模”、“交通”、“拥挤度”和“经济”。概念模型及其结果在图 3 中报告。
Specifically, the first latent variable “city size” measures the level of urban development including two observational variables, i.e., built-up land area and total population. It is assumed that larger urban size and population involve more complex human movement, predict abnormal crowding, and thus increase mobility. The second latent variable “transportation” directly influences the mobility of local residents during emergencies. It is anticipated that a city with well-developed transport systems can offer more alternative traveling options when heavy rainfall occurs and thus can reduce mobility and avoid risks. Here, we select two observational variables to reflect urban transport, including bus stop density and vehicles per capita. The third latent variable, so-called “crowding”, measures social factors that may induce abnormal behaviors. We included two observational variables, i.e., population density and ratio of vulnerable populations (e.g., the old). Regions with a high population density and elderly people with limited mobility may increase the likelihood of accidental crowding. The last latent variable “economy” reflects a city’s economic condition. Developed cities may have more robust infrastructure to reduce mobility and avoid overwhelming shocks or have a large density of buildings like food courts, shopping malls, and entertainment centers that may cause crowding. (53) We considered two observational variables, including total GDP and per capita GDP. Finally, we introduced latent variables “mobility” (urban average mobility changes) to explore more potential pathways. Due to length limits, we report conceptual model and coefficient estimates together in the results and report data sources of observational variables and modeling details in the Supporting Information (Tables S3 and S4).
具体而言，第一个潜变量“城市规模”衡量包括两个观测变量，即建成区面积和总人口在内的城市发展水平。假定更大的城市规模和人口涉及更复杂的人类活动，预测异常拥挤，从而增加流动性。第二个潜变量“交通”直接影响紧急情况下当地居民的流动性。预期拥有发达交通系统的城市在遭遇强降雨时能提供更多替代出行选择，从而降低流动性并规避风险。在此，我们选取两个观测变量来反映城市交通状况，包括公交站点密度和人均车辆数。第三个潜变量，即所谓的“拥挤”，衡量可能引发异常行为的社会因素。我们纳入两个观测变量，即人口密度和弱势群体比例（如老年人）。高人口密度及行动受限的老年人聚集区域可能增加意外拥挤的可能性。最后一个潜变量“经济”反映城市的经济状况。 发达城市可能拥有更完善的基础设施来减少流动性并避免冲击过载，或者拥有如美食广场、购物中心和娱乐中心等高密度建筑，这些可能导致人群拥挤。（53）我们考虑了两个观测变量，即总 GDP 和人均 GDP。最后，我们引入了潜在变量“流动性”（城市平均流动性变化）以探索更多潜在路径。由于篇幅限制，我们在结果部分一并报告概念模型和系数估计，观测变量的数据来源及建模细节则报告在支持信息中（表 S3 和 S4）。
具体而言，第一个潜变量“城市规模”衡量包括两个观测变量，即建成区面积和总人口在内的城市发展水平。假定更大的城市规模和人口涉及更复杂的人类活动，预测异常拥挤，从而增加流动性。第二个潜变量“交通”直接影响紧急情况下当地居民的流动性。预期拥有发达交通系统的城市在遭遇强降雨时能提供更多替代出行选择，从而降低流动性并规避风险。在此，我们选取两个观测变量来反映城市交通状况，包括公交站点密度和人均车辆数。第三个潜变量，即所谓的“拥挤”，衡量可能引发异常行为的社会因素。我们纳入两个观测变量，即人口密度和弱势群体比例（如老年人）。高人口密度及行动受限的老年人聚集区域可能增加意外拥挤的可能性。最后一个潜变量“经济”反映城市的经济状况。 发达城市可能拥有更完善的基础设施来减少流动性并避免冲击过载，或者拥有如美食广场、购物中心和娱乐中心等高密度建筑，这些可能导致人群拥挤。（53）我们考虑了两个观测变量，即总 GDP 和人均 GDP。最后，我们引入了潜在变量“流动性”（城市平均流动性变化）以探索更多潜在路径。由于篇幅限制，我们在结果部分一并报告概念模型和系数估计，观测变量的数据来源及建模细节则报告在支持信息中（表 S3 和 S4）。
3.3. Calculating Mobility-Induced Variations in Urban Flood Risk Exposure
3.3. 计算城市洪水风险暴露中的流动性诱发变化
Changing mobility patterns can be regarded as an individual adaptation to rainfall because people may change their routines to avoid urban inundation or traffic. However, it is questionable whether these behaviors are effective as inappropriate clustering in risk-prone locations may instead increase urban flood risk. To investigate mobility-induced urban flood risk exposure changes, we extracted a flood map in Jiangsu over the research time window from Google Maps (54) where inundated urban areas are labeled at a resolution of 30 m. We then overlaid the flood map, population map, and city boundary map on the “mobility map” and calculated an index to reflect the changes in urban flood risk exposure, as shown in eq 4
改变的移动模式可视为个体对降雨的适应，因为人们可能会调整日常活动以避开城市内涝或交通拥堵。然而，这些行为是否有效尚存疑问，因为不当的聚集在易受灾地点反而可能增加城市洪水风险。为了研究移动模式引发的都市洪水风险暴露变化，我们从 Google Maps（54）中提取了研究时段内江苏省的洪水图，该图以 30 米分辨率标注了淹没的城市区域。随后，我们将洪水图、人口分布图和城市边界图叠加于“移动地图”之上，并计算了一个指数以反映城市洪水风险暴露的变化，如公式 4 所示。where ΔFloodRiski is the change of urban flood risk exposure in city i, as measured by percentage changes in population within urban floodplains due to mobility; Gi denotes all the flood grids in city i; visitshock(k) is the change of human mobility in grid k; and POPk indicates the population in grid k. Floodk is a binary variable and is coded with 1 if grid k is inundated and with 0 if not.
其中，ΔFloodRisk 表示城市洪水风险暴露的变化，以因人口流动性导致的城区洪泛区人口百分比变化来衡量；G 代表城市中所有洪水网格；visitshock(k)为网格 k 内人类流动性的变化；POPk 表示网格 k 中的人口数量。Floodk 是一个二元变量，若网格 k 被淹没则编码为 1，否则编码为 0。
改变的移动模式可视为个体对降雨的适应，因为人们可能会调整日常活动以避开城市内涝或交通拥堵。然而，这些行为是否有效尚存疑问，因为不当的聚集在易受灾地点反而可能增加城市洪水风险。为了研究移动模式引发的都市洪水风险暴露变化，我们从 Google Maps（54）中提取了研究时段内江苏省的洪水图，该图以 30 米分辨率标注了淹没的城市区域。随后，我们将洪水图、人口分布图和城市边界图叠加于“移动地图”之上，并计算了一个指数以反映城市洪水风险暴露的变化，如公式 4 所示。
(4)
其中，ΔFloodRisk 表示城市洪水风险暴露的变化，以因人口流动性导致的城区洪泛区人口百分比变化来衡量；G 代表城市中所有洪水网格；visitshock(k)为网格 k 内人类流动性的变化；POPk 表示网格 k 中的人口数量。Floodk 是一个二元变量，若网格 k 被淹没则编码为 1，否则编码为 0。
4. Results and Discussion
4. 结果与讨论
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
4.1. Human Mobility Changes in Response to Rainfall Events
4.1. 人类流动性对降雨事件的响应变化
As visualized in Figure 2(A), the visitshock indicator, measuring percent changes in human activities at the 500 m × 500 m grid cell level, well represents urban residents’ responses to rainfall events. There are about 0.16 million grids in Jiangsu Province (excluding large water bodies), and more than a third of them (∼54 thousand grids) capture changes in human mobility after precipitation. The distribution of affected grids is more centered in the urban area than the rural region probably due to intensive social activities and dense road networks in cities.
如图 2(A)所示，visitshock 指标以 500 米×500 米网格单元为单位，衡量人类活动的百分比变化，很好地反映了城市居民对降雨事件的响应。江苏省（不包括大型水体）约有 0.16 百万个网格，其中超过三分之一（约 5.4 万个网格）在降水后捕捉到了人类移动性的变化。受影响网格的分布在城市区域比农村地区更为集中，这可能是因为城市密集的社会活动和密集的道路网络。
如图 2(A)所示，visitshock 指标以 500 米×500 米网格单元为单位，衡量人类活动的百分比变化，很好地反映了城市居民对降雨事件的响应。江苏省（不包括大型水体）约有 0.16 百万个网格，其中超过三分之一（约 5.4 万个网格）在降水后捕捉到了人类移动性的变化。受影响网格的分布在城市区域比农村地区更为集中，这可能是因为城市密集的社会活动和密集的道路网络。
Figure 2 图 2
Figure 2. Mapping human mobility changes in response to rainfall events. (A) Changes in human mobility (i.e., indicator visitshock) after rainfall at the grid level (500 m). Urban boundaries with dense populations are marked by bold solid lines. (B, C) Enlarged view of Nanjing and Suzhou urban agglomeration areas.
图 2. 降雨事件响应下人类流动性变化图。(A) 降雨后网格层面（500 米）人类流动性变化（即指标 visitshock）。人口密集的城市边界以粗实线标示。(B, C) 南京与苏州城市群区域的放大视图。
The directions of the changes in human mobility are also mixed. For example, zooming in on the maps of two urban agglomeration areas, Nanjing and Suzhou (Figure 2B,C), we find places affected by rainfalls are clustered and intertwined but show different spatial patterns across cities. In Nanjing, there is an increasing trend of human activities (red squares) in the urban center but a decreasing trend in suburbs (blue squares). While in Suzhou, the pattern is different with overall attenuated human activities in the downtown area. This may be due to the proximity of the two cities to two different types of large bodies of water with different proximities. To identify hotspots of spatial clusters with similar patterns of human mobility changes, we further conducted Anselin Local Moran’s I analysis (55) for the whole province Figure S5. This map is favorable for the detection of large volumes of people flow within the cities and efficient emergency resource allocation.
人类流动性的变化方向同样呈现混合态势。例如，聚焦于南京与苏州两大城市群区域的地图（图 2B、C），可见受降雨影响的地域集中且交织，但在不同城市间展现出迥异的空间分布模式。在南京，市中心的人类活动（红色方块）呈上升趋势，而郊区（蓝色方块）则呈下降趋势；而在苏州，市区整体上人类活动有所减弱。这可能归因于两座城市与不同类型大型水体的距离差异。为识别具有相似人流变化模式的空间聚集热点，我们对全省进行了 Anselin 局部莫兰指数 I 分析（55）（图 S5）。该地图有助于捕捉城市内大规模人流动态，并实现高效的应急资源配置。
人类流动性的变化方向同样呈现混合态势。例如，聚焦于南京与苏州两大城市群区域的地图（图 2B、C），可见受降雨影响的地域集中且交织，但在不同城市间展现出迥异的空间分布模式。在南京，市中心的人类活动（红色方块）呈上升趋势，而郊区（蓝色方块）则呈下降趋势；而在苏州，市区整体上人类活动有所减弱。这可能归因于两座城市与不同类型大型水体的距离差异。为识别具有相似人流变化模式的空间聚集热点，我们对全省进行了 Anselin 局部莫兰指数 I 分析（55）（图 S5）。该地图有助于捕捉城市内大规模人流动态，并实现高效的应急资源配置。
We find grid-level absolute changes in human mobility after heavy rainfall events range between 0 and 87.4%, having a mean value of 3.2%. When drawing density plots to examine grid-level mobility changes between urban and rural areas (see Figure S6) that distinguished according to the functional urban boundary proposed by Ma and Long, (50) we find that data distributions are both left-skewed with more than 80% of grids having a change in mobility less than 5%. However, grids in the urban area with absolute mobility change greater than 15% are about 20% more than those in rural regions. We perform t test and demonstrate significant differences in grid-level mobility changes between urban and rural areas (t = 12.102, p < 0.001), indicating that residents’ activities in cities tend to be more easily affected by heavy rainfall events.
我们发现，在强降雨事件后，人类移动性的网格级绝对变化范围在 0 至 87.4%之间，平均值为 3.2%。通过绘制密度图来检查城市与农村地区（参见图 S6）根据马和龙提出的功能城市边界区分的网格级移动性变化时，我们发现数据分布均呈左偏态，超过 80%的网格移动性变化小于 5%。然而，城市地区中移动性绝对变化大于 15%的网格比农村地区多约 20%。我们进行了 t 检验，并证明了城市与农村地区在网格级移动性变化上存在显著差异（t = 12.102, p < 0.001），这表明城市居民的活动更容易受到强降雨事件的影响。
我们发现，在强降雨事件后，人类移动性的网格级绝对变化范围在 0 至 87.4%之间，平均值为 3.2%。通过绘制密度图来检查城市与农村地区（参见图 S6）根据马和龙提出的功能城市边界区分的网格级移动性变化时，我们发现数据分布均呈左偏态，超过 80%的网格移动性变化小于 5%。然而，城市地区中移动性绝对变化大于 15%的网格比农村地区多约 20%。我们进行了 t 检验，并证明了城市与农村地区在网格级移动性变化上存在显著差异（t = 12.102, p < 0.001），这表明城市居民的活动更容易受到强降雨事件的影响。
4.2. Potential Drivers of Human Mobility Changes at the Prefecture Level
4.2. 地级层面人类流动性变化的潜在驱动因素
We aggregate grid-level human mobility changes to the prefecture level according to the cities’ boundaries Figure 3(A). The grid-level population is chosen as the weight when aggregating the data. We find that prefecture-level changes in mobility vary ranging from −3.6 to 8.9% (excluding two counties, Feng and Pei, over 8%, the range becomes −3.6–1.2%). It indicates that the mobility changes within cities are not balanced. Some cities have shown increased aggregated mobility, which may indicate more crowded conditions after rainfall shocks, while some others have shown decreased aggregated mobility, which may indicate overall reduced human activities to avoid risks.
我们将网格级的人类流动性变化按照城市边界（图 3(A)）汇总至地级市层面。在汇总数据时，选择网格级人口作为权重。我们发现，地级市层面的流动性变化幅度从-3.6%到 8.9%不等（排除两个超过 8%的县——丰县和沛县，范围变为-3.6%至 1.2%）。这表明城市内部的流动性变化并不均衡。一些城市显示出流动性增加，这可能意味着降雨冲击后拥挤状况加剧；而另一些城市则表现出流动性减少，这可能意味着为规避风险而整体上减少了人类活动。
我们将网格级的人类流动性变化按照城市边界（图 3(A)）汇总至地级市层面。在汇总数据时，选择网格级人口作为权重。我们发现，地级市层面的流动性变化幅度从-3.6%到 8.9%不等（排除两个超过 8%的县——丰县和沛县，范围变为-3.6%至 1.2%）。这表明城市内部的流动性变化并不均衡。一些城市显示出流动性增加，这可能意味着降雨冲击后拥挤状况加剧；而另一些城市则表现出流动性减少，这可能意味着为规避风险而整体上减少了人类活动。
Figure 3 图 3
Figure 3. Changes in human mobility after heavy rainfall at the prefecture level and potential drivers. (A) Percent changes in mobility at the prefecture level. (B) Results of piecewise SEM between changes in mobility and eight explanatory variables. Colors are used to distinguish the direction of correlation (orange for positive correlation and green for negative correlation) (*p < 0.05; **p < 0.01; ***p < 0.001). (C) Direct and indirect effects of the four latent variables.
图 3. 强降雨后地级市层面人类流动性的变化及其潜在驱动因素。(A) 地级市层面流动性的百分比变化。(B) 流动性变化与八个解释变量之间的分段结构方程模型(SEM)结果。颜色用于区分相关方向（橙色表示正相关，绿色表示负相关）(*p < 0.05; **p < 0.01; ***p < 0.001)。(C) 四个潜在变量的直接与间接效应。
To explain heterogeneity across cities in the mobility pattern, we applied piecewise SEM to reveal indirect and spurious relationships between aggregated mobility change and four prefecture-level latent variables (as shown in Figure 3(B)). We first examine the direct impacts of four latent variables on changes in population mobility. The latent variable “transportation” displays a significant negative correlation with mobility change, indicating that cities with more robust transportation systems reduce the likelihood of abnormal population clustering during rainfall, as shown by decreased aggregated mobility (β̂ = −0.638, p = 0.032). The latent variable “crowding” is positively correlated with mobility change, suggesting that a high density of elder people with limited mobility predicts a greater degree of abnormal population clustering (β̂ = 0.245, p = 0.048). Moreover, the latent variable “city size” plays a role in increasing mobility after rainfalls, indicating that large cities are more likely to have population clustering (β̂ = 0.366, p = 0.041). Lastly, the latent variable “economy” does not directly influence mobility (β̂ = 0.253, p = 0.398).
为解释城市间在移动模式上的异质性，我们采用了分段结构方程模型（SEM）来揭示聚合移动变化与四个地级潜在变量之间的间接及虚假关系（如图 3(B)所示）。首先，我们考察了四个潜在变量对人口移动变化的直接影响。潜在变量“交通”显示出与移动变化显著的负相关关系，表明拥有更健全交通系统的城市在降雨期间减少异常人口聚集的可能性，表现为聚合移动的下降（β̂ = −0.638, p = 0.032）。潜在变量“拥挤度”与移动变化呈正相关，意味着老年人移动能力有限的高密度地区预示着更高程度的异常人口聚集（β̂ = 0.245, p = 0.048）。此外，潜在变量“城市规模”在降雨后对增加移动性起作用，表明大城市更可能出现人口聚集现象（β̂ = 0.366, p = 0.041）。最后，潜在变量“经济”并不直接影响移动性（β̂ = 0.253, p = 0.398）。
为解释城市间在移动模式上的异质性，我们采用了分段结构方程模型（SEM）来揭示聚合移动变化与四个地级潜在变量之间的间接及虚假关系（如图 3(B)所示）。首先，我们考察了四个潜在变量对人口移动变化的直接影响。潜在变量“交通”显示出与移动变化显著的负相关关系，表明拥有更健全交通系统的城市在降雨期间减少异常人口聚集的可能性，表现为聚合移动的下降（β̂ = −0.638, p = 0.032）。潜在变量“拥挤度”与移动变化呈正相关，意味着老年人移动能力有限的高密度地区预示着更高程度的异常人口聚集（β̂ = 0.245, p = 0.048）。此外，潜在变量“城市规模”在降雨后对增加移动性起作用，表明大城市更可能出现人口聚集现象（β̂ = 0.366, p = 0.041）。最后，潜在变量“经济”并不直接影响移动性（β̂ = 0.253, p = 0.398）。
We then examine all potential relationships between variables and identify three indirect pathways that a city’s economic condition may affect residents’ mobility responses to heavy rainfalls. First, “Economy” positively influences “transport” (β̂ = 0.795, p = 0.000) and supports the reduction of mobility. It indicates that developed cities facilitate people to evacuate and avoid clustering after rainfall with better-designed public transportation systems. Second, “economy” negatively correlates with “crowding” (β̂ = −0.532, p = 0.008) and indirectly reduces mobility after heavy precipitation, as shown in Figure 3(C). It might be because developed cities are associated with more urban spaces and young age citizens, which reduces the likelihood of people crowding. Third, “economy” is associated with expanded “city size,” which, in turn, induces higher aggregated mobility (β̂ = 0.753, p = 0.000). It indicates that megacities with not only developed economies but also large urban sizes and populations are more likely to face abnormal crowding after heavy rain shocks. Detailed information about the model can be seen in Tables S4–S6.
随后，我们审视了变量间所有潜在的关联，并识别出城市经济状况可能影响居民对暴雨移动性反应的三条间接路径。首先，“经济”正向影响“交通”（β̂ = 0.795, p = 0.000），并支持减少移动性。这表明发达城市通过设计更完善的公共交通系统，便于人们在降雨后疏散并避免聚集。其次，“经济”与“拥挤”呈负相关（β̂ = -0.532, p = 0.008），间接降低了暴雨后的移动性，如图 3(C)所示。这可能是因为发达城市拥有更多城市空间和年轻人口，从而降低了人群聚集的可能性。第三，“经济”与“城市规模”扩大相关，进而引发更高的总体移动性（β̂ = 0.753, p = 0.000）。这表明，不仅经济发达而且城市规模和人口庞大的特大城市，在暴雨冲击后更易遭遇异常拥挤。模型详细信息可参见表 S4 至 S6。
随后，我们审视了变量间所有潜在的关联，并识别出城市经济状况可能影响居民对暴雨移动性反应的三条间接路径。首先，“经济”正向影响“交通”（β̂ = 0.795, p = 0.000），并支持减少移动性。这表明发达城市通过设计更完善的公共交通系统，便于人们在降雨后疏散并避免聚集。其次，“经济”与“拥挤”呈负相关（β̂ = -0.532, p = 0.008），间接降低了暴雨后的移动性，如图 3(C)所示。这可能是因为发达城市拥有更多城市空间和年轻人口，从而降低了人群聚集的可能性。第三，“经济”与“城市规模”扩大相关，进而引发更高的总体移动性（β̂ = 0.753, p = 0.000）。这表明，不仅经济发达而且城市规模和人口庞大的特大城市，在暴雨冲击后更易遭遇异常拥挤。模型详细信息可参见表 S4 至 S6。
4.3. Mobility Changes Reveal Urban Flood Risk Maladaptation
4.3. 移动性变化揭示城市洪水风险适应不良
When heavy rainfall occurs, people may adopt adaptation behaviors such as seeking shelters, which will increase or decrease the time people stay in a certain place and reshape the patterns of urban flood risk exposure. We thus overlay inundation hotspot maps on grid-level mobility change maps to calculate prefecture-level flood risk exposure changes. Figure 4 presents how the changes in urban flood risk exposures are associated with aggregated human mobility dynamics after rainfall events.
当发生强降雨时，人们可能会采取寻求避难所等适应行为，这将增加或减少人们在某地停留的时间，并重塑城市洪水风险暴露模式。因此，我们将淹水热点图与网格级移动性变化图叠加，以计算出各县域洪水风险暴露的变化情况。图 4 展示了降雨事件后，城市洪水风险暴露变化与人类移动性动态聚合之间的关联。
当发生强降雨时，人们可能会采取寻求避难所等适应行为，这将增加或减少人们在某地停留的时间，并重塑城市洪水风险暴露模式。因此，我们将淹水热点图与网格级移动性变化图叠加，以计算出各县域洪水风险暴露的变化情况。图 4 展示了降雨事件后，城市洪水风险暴露变化与人类移动性动态聚合之间的关联。
Figure 4 图 4
Figure 4. Change in urban flood risk exposure per city and its association with human mobility after heavy rainfalls. (A, B) Changes in flood risk exposure after heavy rainfalls. The size of the point is proportional to the change in the number of people staying in areas at risk of flooding. The color of the point indicates the direction of change. (C) Process of overlaying four layers. (D) Histogram of the impacted population in the flood-prone grid.
图 4. 暴雨后各城市内涝风险暴露变化及其与人口流动的关联。(A, B) 暴雨后内涝风险暴露的变化。点的尺寸与滞留在易涝区域人数的变化成正比，点的颜色表示变化的方向。(C) 四层叠加过程。(D) 易涝网格中受影响人口的直方图。
We found a significant positive correlation between population mobility and urban flood risk exposure (β̂ = 1.50, p = 5.6e-4). This suggests that given the current urban development status of each city in Jiangsu Province, reducing population mobility during rainfall periods can be beneficial for urban flood risk adaptation. Moreover, Figure 4(A) reflects the flood risk adaptation status of different cities in Jiangsu. As presented by blue circles in Quadrant III, urban residents’ activities and flood exposure both decreased in 23 cities as the result of sudden precipitation shocks. A mean of 2.6% urban flood risk can be avoided in this group, with a total affected population of 0.45 million. Cities where risk-reduction effects are prominent include Suzhou (SZ), Wuxi (WX), and Changzhou (CZ). On the opposite, five cities including Nanjing city (NJ) have a mean of 1.4% increase in flood risk exposure with a total affected population of 0.18 million, as shown by red circles in Quadrant I. We also notice that seven cities fallen in Quadrant II are those having declined human activities but rising urban flood risk exposure. Human mobility instead increases the exposure to flood risks in these two types of cities, thereby raising the risk level of the cities, suggesting that these cities are experiencing flood risk maladaptation.
我们发现人口流动性与城市洪水风险暴露之间存在显著正相关关系（β̂ = 1.50, p = 5.6e-4）。这表明，鉴于江苏省各城市当前的城市发展状况，在降雨期间减少人口流动对城市洪水风险适应是有益的。此外，图 4(A)反映了江苏省不同城市的洪水风险适应状况。如图中第三象限的蓝色圆圈所示，由于突发降水冲击，23 个城市的城市居民活动和洪水暴露均有所下降。这一群体平均可避免 2.6%的城市洪水风险，总受影响人口达 0.45 百万。风险降低效果显著的城市包括苏州（SZ）、无锡（WX）和常州（CZ）。相反，如图中第一象限的红色圆圈所示，南京市（NJ）等五个城市的洪水风险暴露平均增加了 1.4%，总受影响人口为 0.18 百万。我们还注意到，落在第二象限的七个城市的特点是人类活动减少但城市洪水风险暴露增加。 人类流动性反而增加了这两类城市面临洪灾风险的机会，从而提升了城市的风险等级，这表明这些城市正在经历洪灾风险的适应不良。
我们发现人口流动性与城市洪水风险暴露之间存在显著正相关关系（β̂ = 1.50, p = 5.6e-4）。这表明，鉴于江苏省各城市当前的城市发展状况，在降雨期间减少人口流动对城市洪水风险适应是有益的。此外，图 4(A)反映了江苏省不同城市的洪水风险适应状况。如图中第三象限的蓝色圆圈所示，由于突发降水冲击，23 个城市的城市居民活动和洪水暴露均有所下降。这一群体平均可避免 2.6%的城市洪水风险，总受影响人口达 0.45 百万。风险降低效果显著的城市包括苏州（SZ）、无锡（WX）和常州（CZ）。相反，如图中第一象限的红色圆圈所示，南京市（NJ）等五个城市的洪水风险暴露平均增加了 1.4%，总受影响人口为 0.18 百万。我们还注意到，落在第二象限的七个城市的特点是人类活动减少但城市洪水风险暴露增加。 人类流动性反而增加了这两类城市面临洪灾风险的机会，从而提升了城市的风险等级，这表明这些城市正在经历洪灾风险的适应不良。
To validate our findings, we employ Nanjing as a case study, specifically examining population mobility within urban flood-prone areas at a 500 m grid scale during rainfall events. The results are presented in Figure 4(C), where the circle size represents the grid’s population, and color signifies changes in mobility during rainfall. Notably, during rainfall, population mobility in Nanjing shifted toward central areas, indicating a net influx into high-risk zones. Figure 4(D) further reinforces these observations. In this figure, we calculated changes in population mobility in flood-prone grid areas by considering the population, resulting in a frequency distribution histogram. The results reveal that 683 areas experienced a population decrease (average −170.467), while 507 areas witnessed an increase in population (average 324.897), indicating that flood-prone regions exhibited a net population increase.
为验证我们的发现，我们以南京为案例研究，具体考察降雨事件期间城市易涝区在 500 米网格尺度上的人口流动情况。结果如图 4(C)所示，圆圈大小代表网格内人口数量，颜色表示降雨期间流动性的变化。值得注意的是，降雨期间南京的人口流动向中心区域转移，表明高风险区域出现了净流入。图 4(D)进一步强化了这些观察。在该图中，我们通过考虑人口计算了易涝网格区域的人口流动性变化，得出一个频率分布直方图。结果显示，683 个区域人口减少（平均-170.467），而 507 个区域人口增加（平均 324.897），表明易涝地区总体上呈现人口净增长。
为验证我们的发现，我们以南京为案例研究，具体考察降雨事件期间城市易涝区在 500 米网格尺度上的人口流动情况。结果如图 4(C)所示，圆圈大小代表网格内人口数量，颜色表示降雨期间流动性的变化。值得注意的是，降雨期间南京的人口流动向中心区域转移，表明高风险区域出现了净流入。图 4(D)进一步强化了这些观察。在该图中，我们通过考虑人口计算了易涝网格区域的人口流动性变化，得出一个频率分布直方图。结果显示，683 个区域人口减少（平均-170.467），而 507 个区域人口增加（平均 324.897），表明易涝地区总体上呈现人口净增长。
4.4. Implications for Urban Flood Risk Management
4.4. 城市洪水风险管理的启示
Our study provides valuable implications for urban flood risk management from two aspects. First, our research offers a framework to map the hotspots of human mobility and urban flood risk after heavy precipitation by integrating real-time mobile phone signaling big data. This is helpful to enhance the early warning system of urban flooding by tracing where citizens are moving toward and identifying clusters with higher inundation risks. The map is also useful to inform citizens in advance about areas where flooding is likely to occur so that they can optimize travel decision-making. This alerting system is especially urgent to reduce the population flow that moves to risky areas due to limited information, which is urgently needed in those cities exhibiting maladaptation toward flood risks (Figure 4). In addition, the map can also guide residents in risky areas to evacuate in advance when rainfall occurs to prevent secondary disasters caused by human mobility dynamics.
本研究从两个方面为城市洪水风险管理提供了宝贵的启示。首先，通过整合实时移动电话信号大数据，我们的研究提供了一个框架，用于在强降水后绘制人类流动热点和城市洪水风险图。这有助于通过追踪市民的移动方向并识别具有较高淹没风险的聚集区，来增强城市洪水的预警系统。该地图还能提前告知市民可能发生洪水的区域，以便他们优化出行决策。这一预警系统对于减少因信息有限而流向风险区域的人口流动尤为迫切，对于那些对洪水风险表现出适应不良的城市而言，这一需求尤为紧迫（图 4）。此外，该地图还能指导风险区域的居民在降雨发生时提前疏散，以防止因人类流动动态引发的次生灾害。
本研究从两个方面为城市洪水风险管理提供了宝贵的启示。首先，通过整合实时移动电话信号大数据，我们的研究提供了一个框架，用于在强降水后绘制人类流动热点和城市洪水风险图。这有助于通过追踪市民的移动方向并识别具有较高淹没风险的聚集区，来增强城市洪水的预警系统。该地图还能提前告知市民可能发生洪水的区域，以便他们优化出行决策。这一预警系统对于减少因信息有限而流向风险区域的人口流动尤为迫切，对于那些对洪水风险表现出适应不良的城市而言，这一需求尤为紧迫（图 4）。此外，该地图还能指导风险区域的居民在降雨发生时提前疏散，以防止因人类流动动态引发的次生灾害。
The second implication is that we have revealed factors that drive the urban vulnerability toward heavy rainfalls, which is informative for the long-term capacity building of urban climate adaptation. As shown in the piecewise SEM model, we find that basic infrastructure such as a public transportation network, economic situation, and vulnerability of the population can significantly influence the magnitude and direction of human mobility. Accordingly, better management of urban transportation networks is one of the most efficient approaches to cope with mobility-induced urban flood risks in the context of climate change. Compared to other socioeconomic sectors, commercial activities are more vulnerable in the face of the shocks of extreme weather events. The findings suggest urban decision-makers increase local economic diversity and develop innovative markets to enhance flood risk adaptation.
第二个含义在于，我们揭示了推动城市应对暴雨脆弱性的因素，这对城市气候适应能力的长期建设具有指导意义。如分段结构方程模型所示，我们发现公共交通网络、经济状况及人口脆弱性等基础设施基本要素，能显著影响人类流动的规模与方向。因此，优化城市交通网络管理是应对气候变化背景下由人口流动引发的城市洪涝风险最为有效的途径之一。相较于其他社会经济领域，商业活动在极端天气事件冲击下显得更为脆弱。研究结果提示城市决策者应提升地方经济多样性，并开拓创新市场，以增强对洪涝风险的适应能力。
第二个含义在于，我们揭示了推动城市应对暴雨脆弱性的因素，这对城市气候适应能力的长期建设具有指导意义。如分段结构方程模型所示，我们发现公共交通网络、经济状况及人口脆弱性等基础设施基本要素，能显著影响人类流动的规模与方向。因此，优化城市交通网络管理是应对气候变化背景下由人口流动引发的城市洪涝风险最为有效的途径之一。相较于其他社会经济领域，商业活动在极端天气事件冲击下显得更为脆弱。研究结果提示城市决策者应提升地方经济多样性，并开拓创新市场，以增强对洪涝风险的适应能力。
Our work also sheds light on theoretical insights into studies of urban flood risk at a high spatial and temporal resolution. Using 2.6 million mobile phone signal data to quantify human mobility, the method bridges the knowledge gap between heavy rainfall, individual adaptation strategies, and urban flood risk. The results reveal that human mobility changes exhibit different patterns not only between cities but also within cities. Previous studies that examined human mobility response to weather events have mainly focused on extremely adverse natural disasters such as typhoons or earthquakes. (20) They generally have coarse spatial resolution and focus on migration behaviors before and after the disasters. This study extends current frameworks so that we can monitor public mobility changes when high-frequency and moderate-heavy rainfall events occur. Taking advantage of the high-resolution mobility and urban flood maps, we also reveal that poor city management may lead to maladaptation so that more residents are exposed to flood risks as they change their trip modes.
我们的研究还为高时空分辨率下的城市洪水风险研究提供了理论洞见。利用 260 万条手机信号数据量化人类流动性，该方法弥合了强降雨、个体适应策略与城市洪水风险之间的知识鸿沟。结果显示，人类流动性变化不仅在城市间呈现不同模式，在城市内部亦是如此。以往研究在探讨人类对天气事件的流动性反应时，主要集中于台风或地震等极端自然灾害，通常空间分辨率较粗，且关注灾害前后的迁移行为。本研究拓展了现有框架，使我们能够监测高频次中至强降雨事件发生时的公众流动性变化。借助高分辨率的流动性与城市洪水地图，我们还揭示了城市管理不善可能导致适应不良，从而使得更多居民因改变出行方式而暴露于洪水风险之中。
我们的研究还为高时空分辨率下的城市洪水风险研究提供了理论洞见。利用 260 万条手机信号数据量化人类流动性，该方法弥合了强降雨、个体适应策略与城市洪水风险之间的知识鸿沟。结果显示，人类流动性变化不仅在城市间呈现不同模式，在城市内部亦是如此。以往研究在探讨人类对天气事件的流动性反应时，主要集中于台风或地震等极端自然灾害，通常空间分辨率较粗，且关注灾害前后的迁移行为。本研究拓展了现有框架，使我们能够监测高频次中至强降雨事件发生时的公众流动性变化。借助高分辨率的流动性与城市洪水地图，我们还揭示了城市管理不善可能导致适应不良，从而使得更多居民因改变出行方式而暴露于洪水风险之中。
Several limitations in our study should be acknowledged here. First, our data were obtained from Jiangsu Telecom, whose subscribers account for roughly 35% of the total population in Jiangsu Province. Although our analysis (Figure S2) shows that mobile phone signaling data have a good representation of the local population and human mobility patterns at scale, there might exist differences across cities due to the varied proportion of subscribers. Meanwhile, our mobile phone data were obtained from 5 years ago. More recent data may enable us to carry out research on a finer spatial and temporal scale due to more advanced position-tracking technologies. Second, although we have applied algorithms to attempt to remove trend effects such as short-term weekly fluctuations and long-term user increases, it is difficult to control all the confounding factors that affect the patterns of mobile phone signals, e.g., traffic accidents, construction projects, and other important public events. Unlike us, in the work of Metulini et al., (56) they decomposed mobile phone signaling data in days and focused on seasonal structure, which is also an amazing attempt. In the future, it will be worthwhile to compare the robustness of these two approaches. Third, given data availability, our study does not track any specific users, and it is not possible to depict user-level intracity trip decision-makings or to capture intercity population movement. The urban flood maps we use were also based on historical records and do not reflect instant inundation hotspots. It is expected that incorporating more refined human-centric data (e.g., contents on social media) in the future could help us gain more understanding of how the impacts of small-scale natural hazards vary among people with different backgrounds. (57) At the same time, our study reveals the potential impact of different water body types and distances from water bodies on population movement, and we believe that it would be worthwhile to incorporate such spatial effects into the model.
本研究存在若干局限性，在此需予以承认。首先，数据来源于江苏电信，其用户约占江苏省总人口的 35%。尽管分析（图 S2）表明手机信令数据能较好地反映当地人口及大规模的人类流动模式，但由于各城市用户比例不一，城市间可能存在差异。同时，所用手机数据采集于 5 年前，更近期的数据得益于更先进的位置追踪技术，能使我们在更精细的空间与时间尺度上开展研究。其次，尽管已采用算法尝试剔除如短期周波动和长期用户增长等趋势效应，但诸如交通事故、建设项目及其他重大公共事件等影响手机信号模式的混杂因素难以完全控制。不同于我们，Metulini 等人的研究（56）将手机信令数据按日分解并聚焦于季节性结构，亦是一次令人瞩目的尝试。 未来，比较这两种方法的稳健性将是有价值的。第三，鉴于数据可得性，我们的研究并未追踪任何特定用户，因此无法描绘用户层面的城市内出行决策，也无法捕捉城市间的人口流动。我们所使用的城市洪水地图基于历史记录，并未反映即时淹没热点。预计未来纳入更精细的人本数据（如社交媒体内容）将有助于我们更深入地理解小规模自然灾害对不同背景人群影响的差异。（57）同时，本研究揭示了不同水体类型及与水体距离对人口流动的潜在影响，我们认为将此类空间效应纳入模型是值得的。
本研究存在若干局限性，在此需予以承认。首先，数据来源于江苏电信，其用户约占江苏省总人口的 35%。尽管分析（图 S2）表明手机信令数据能较好地反映当地人口及大规模的人类流动模式，但由于各城市用户比例不一，城市间可能存在差异。同时，所用手机数据采集于 5 年前，更近期的数据得益于更先进的位置追踪技术，能使我们在更精细的空间与时间尺度上开展研究。其次，尽管已采用算法尝试剔除如短期周波动和长期用户增长等趋势效应，但诸如交通事故、建设项目及其他重大公共事件等影响手机信号模式的混杂因素难以完全控制。不同于我们，Metulini 等人的研究（56）将手机信令数据按日分解并聚焦于季节性结构，亦是一次令人瞩目的尝试。 未来，比较这两种方法的稳健性将是有价值的。第三，鉴于数据可得性，我们的研究并未追踪任何特定用户，因此无法描绘用户层面的城市内出行决策，也无法捕捉城市间的人口流动。我们所使用的城市洪水地图基于历史记录，并未反映即时淹没热点。预计未来纳入更精细的人本数据（如社交媒体内容）将有助于我们更深入地理解小规模自然灾害对不同背景人群影响的差异。（57）同时，本研究揭示了不同水体类型及与水体距离对人口流动的潜在影响，我们认为将此类空间效应纳入模型是值得的。
Supporting Information 支持信息
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
Introduction to S–H-ESD method; general and geographic information about 53 cities and mobile phone towers in Jiangsu, China; examples of mobile phone signaling data; data validation including rainfall data and mobile phone signaling data; comparison and validation of baseline calculation method; analyses results related to piecewise SEM; explanatory variables data source; Anselin Local Moran’s I analysis of mobility changes; and differences in mobility changes between rural and urban areas (PDF)
S-H-ESD 方法简介；中国江苏省 53 个城市及移动电话基站的概况与地理信息；移动电话信令数据示例；数据验证，包括降雨数据与移动电话信令数据；基线计算方法的对比与验证；分段 SEM 分析结果；解释变量数据来源；Anselin 局部 Moran's I 分析流动性变化；城乡流动性变化差异（PDF）
Terms & Conditions 条款与条件
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
大多数电子支持信息文件无需订阅 ACS 网络版即可获取。这些文件可按文章下载用于研究用途（若相关文章附有公共使用许可，该许可可能允许其他用途）。通过 RightsLink 权限系统（网址：http://pubs.acs.org/page/copyright/permissions.html）可向 ACS 申请其他用途的权限。
Author Information 作者信息
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
- Jianxun Yang - State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;
https://orcid.org/0009-0005-0821-7698;
杨建勋 - 污染控制与资源化研究国家重点实验室，环境学院，南京大学，南京 210023，中国；江苏省大气环境与装备技术协同创新中心（CICAEET），南京信息工程大学，南京 210044，江苏，中国； https://orcid.org/0009-0005-0821-7698；电子邮箱：yangjx@nju.edu.cn - Miaomiao Liu - State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;https://orcid.org/0000-0002-9043-3584;
刘苗苗 - 污染控制与资源化研究国家重点实验室，环境学院，南京大学，南京 210023，中国；江苏省大气环境与装备技术协同创新中心（CICAEET），南京信息工程大学，南京 210044，江苏，中国； https://orcid.org/0000-0002-9043-3584；电子邮箱：liumm@nju.edu.cn
- Wen Fang - State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;https://orcid.org/0000-0001-5669-322X
文芳 - 污染控制与资源化研究国家重点实验室，环境学院，南京大学，南京 210023，中国； https://orcid.org/0000-0001-5669-322X
Acknowledgments 致谢
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
The project was financially supported by the National Natural Science Foundation of China (Grant Nos 72304136, 72222012, 71921003, and 52170189), National Postdoctoral Program for Innovative Talent (Grant No. BX20230159), Jiangsu R&D Special Fund for Carbon Peaking and Carbon Neutrality (Grant No. BK20220014), and Jiangsu Natural Science Foundation (Grant No. BK20220125). Dr. Jianxun Yang acknowledges the Yuxiu Young Scholar Postdoc Fellowship granted by Nanjing University and the Research Committee of the School of Environment, Nanjing University, for approving this study.
该项目得到了国家自然科学基金（批准号：72304136、72222012、71921003、52170189）、国家创新人才支持计划博士后项目（批准号：BX20230159）、江苏省碳达峰碳中和科技创新专项资金（批准号：BK20220014）以及江苏省自然科学基金（批准号：BK20220125）的资助。杨建勋博士感谢南京大学毓秀青年学者博士后基金和南京大学环境学院研究委员会对本研究的批准。
References 参考文献
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
This article references
57 other publications.
本文引用了 57 篇其他出版物。
- 1Pörtner, H.-O.; Roberts, D. C.; Adams, H.; Adler, C.; Aldunce, P.; Ali, E.; Begum, R. A.; Betts, R.; Kerr, R. B.; Biesbroek, R. Climate Change: Impacts, Adaptation, and Vulnerability 2022; IPCC Sixth Assessment Report , 2022.Google Scholar 谷歌学术There is no corresponding record for this reference.
波特纳, H.-O.; 罗伯茨, D. C.; 亚当斯, H.; 阿德勒, C.; 阿尔杜恩斯, P.; 阿里, E.; 贝古姆, R. A.; 贝茨, R.; 克尔, R. B.; 比斯布罗克, R. 气候变化：影响、适应与脆弱性 2022；IPCC 第六次评估报告, 2022. - 2Harrison, C. G.; Williams, P. R. A systems approach to natural disaster resilience. Simul. Modell. Pract. Theory 2016, 65, 11– 31, DOI: 10.1016/j.simpat.2016.02.008Google Scholar 谷歌学术2https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38zltVKhug%253D%253D&md5=418cef3fe7045d494747a382467eea73A systems approach to natural disaster resilienceHarrison Colin G; Williams Peter RSimulation modelling practice and theory (2016), 65 (), 11-31 ISSN:1569-190X.The frequency, social, and economic impacts of natural disasters show exponential increases in recent decades. Cities and countries around the world have begun to realize that these events are no longer "hundred year" storms, but repeat within a few years. As urbanisation continues throughout this century, more and more people and more economic activity will be concentrated in at-risk areas; especially as new arrivals in cities throughout Asia and Africa are likely to be concentrated in the highest risk districts, much as they often are in North America and Europe today. This article reviews recent growth of natural disasters and considers how a systems approach can improve approaches to mitigation and adaptation of these risks and to recovery from such events.
哈里森, C. G.; 威廉姆斯, P. R. 自然灾害恢复力的系统方法. 模拟.建模.实践与理论 2016, 65, 11–31, DOI: 10.1016/j.simpat.2016.02.008(acccessed 2022–10–23) （访问于 2022 年 10 月 23 日）
- 3Huang, Y.; Tian, Z.; Ke, Q.; Liu, J.; Irannezhad, M.; Fan, D.; Hou, M.; Sun, L. Nature-based solutions for urban pluvial flood risk management. Wiley Interdiscip. Rev.: Water 2020, 7 (3), e1421 DOI: 10.1002/wat2.1421Google Scholar 谷歌学术There is no corresponding record for this reference.
黄艳, 田展, 柯强, 刘佳, Irannezhad, M., 范冬, 侯梦, 孙磊. 基于自然的解决方案在城市暴雨洪水风险管理中的应用. 威立跨学科评论: 水 2020, 7(3), e1421. DOI: 10.1002/wat2.1421 - 4Hammond, M. J.; Chen, A. S.; Djordjević, S.; Butler, D.; Mark, O. Urban flood impact assessment: A state-of-the-art review. Urban Water J. 2015, 12 (1), 14– 29, DOI: 10.1080/1573062X.2013.857421Google Scholar 谷歌学术There is no corresponding record for this reference.
4Hammond, M. J.; Chen, A. S.; Djordjević, S.; Butler, D.; Mark, O. 城市洪水影响评估：一项最新技术综述。城市水务杂志，2015，12（1），14-29，DOI：10.1080/1573062X.2013.857421 - 5Winsemius, H. C.; Aerts, J. C.; Van Beek, L. P.; Bierkens, M. F.; Bouwman, A.; Jongman, B.; Kwadijk, J. C.; Ligtvoet, W.; Lucas, P. L.; Van Vuuren, D. P.; Ward, P. J. Global drivers of future river flood risk. Nat. Clim. Change 2016, 6 (4), 381– 385, DOI: 10.1038/nclimate2893Google Scholar 谷歌学术There is no corresponding record for this reference.
5Winsemius, H. C.; Aerts, J. C.; Van Beek, L. P.; Bierkens, M. F.; Bouwman, A.; Jongman, B.; Kwadijk, J. C.; Ligtvoet, W.; Lucas, P. L.; Van Vuuren, D. P.; Ward, P. J. 全球未来河流洪水风险的主要驱动因素。自然·气候变化 2016, 6 (4), 381–385, DOI: 10.1038/nclimate2893 - 6Rentschler, J.; Salhab, M.; Jafino, B. A. Flood exposure and poverty in 188 countries. Nat. Commun. 2022, 13 (1), 3527 DOI: 10.1038/s41467-022-30727-4Google Scholar 谷歌学术6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhslajsL3E&md5=ee2aabcbf1e383f0904ea316e6716b8cFlood exposure and poverty in 188 countriesRentschler, Jun; Salhab, Melda; Jafino, Bramka ArgaNature Communications (2022), 13 (1), 3527CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)Abstr.: Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global ests. of the no. of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-yr floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world's flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development.
6Rentschler, J.; Salhab, M.; Jafino, B. A. 洪水暴露与 188 个国家贫困状况。自然通讯, 2022, 13(1), 3527 DOI: 10.1038/s41467-022-30727-4 - 7Alexander, L. V.; Zhang, X.; Peterson, T. C.; Caesar, J.; Gleason, B.; Klein Tank, A.; Haylock, M.; Collins, D.; Trewin, B.; Rahimzadeh, F. Global observed changes in daily climate extremes of temperature and precipitation J. Geophys. Res.: Atmos. 2006; Vol. 111 D5 DOI: 10.1029/2005JD006290 .Google Scholar 谷歌学术There is no corresponding record for this reference.
7Alexander, L. V.; Zhang, X.; Peterson, T. C.; Caesar, J.; Gleason, B.; Klein Tank, A.; Haylock, M.; Collins, D.; Trewin, B.; Rahimzadeh, F. 全球观测到的每日气候极端变化：温度与降水。《地球物理研究杂志：大气》2006 年；第 111 卷 D5 页，DOI: 10.1029/2005JD006290。 - 8Rus, K.; Kilar, V.; Koren, D. Resilience assessment of complex urban systems to natural disasters: A new literature review. Int. J. Disaster Risk Reduct. 2018, 31, 311– 330, DOI: 10.1016/j.ijdrr.2018.05.015Google Scholar 谷歌学术There is no corresponding record for this reference.
Rus, K.; Kilar, V.; Koren, D. 复杂城市系统对自然灾害的韧性评估：新文献综述。国际灾害风险减少杂志，2018, 31, 311–330, DOI: 10.1016/j.ijdrr.2018.05.015 - 9Kuzma, S.; Luo, T. The Number of People Affected by Floods Will Double between 2010 and 2030; World Resources Institude, 2020. (accessed 2022–10–23).Google Scholar 谷歌学术There is no corresponding record for this reference.
库兹马, S.; 陆, T. 2010 年至 2030 年间受洪水影响人数将翻倍；世界资源研究所，2020 年。（访问于 2022 年 10 月 23 日）。 - 10Assembly, U. G.. Transforming our world: the 2030 agenda for sustainable development, 21 October 2015 2015. Retrieved from.Google Scholar 谷歌学术There is no corresponding record for this reference.
联合国大会，U. G.《变革我们的世界：2030 年可持续发展议程》，2015 年 10 月 21 日。检索自。 - 11Pradhan, P.; Costa, L.; Rybski, D.; Lucht, W.; Kropp, J. P. A systematic study of sustainable development goal (SDG) interactions. Earth’s Future 2017, 5 (11), 1169– 1179, DOI: 10.1002/2017EF000632Google Scholar 谷歌学术There is no corresponding record for this reference.
11Pradhan, P.; Costa, L.; Rybski, D.; Lucht, W.; Kropp, J. P. 可持续性发展目标（SDG）相互作用的系统性研究。地球未来 2017, 5 (11), 1169–1179, DOI: 10.1002/2017EF000632 - 12Mansour, M. M.; Ibrahim, M. G.; Fujii, M.; Nasr, M. Sustainable development goals (SDGs) associated with flash flood hazard mapping and management measures through morphometric evaluation. Geocarto Int. 2022, 37 (26), 11116– 11133, DOI: 10.1080/10106049.2022.2046868Google Scholar 谷歌学术There is no corresponding record for this reference.
Mansour, M. M.; Ibrahim, M. G.; Fujii, M.; Nasr, M. 通过形态测量评估与快速洪水危险性制图及管理措施相关的可持续发展目标(SDGs)。《地理制图学国际期刊》2022 年，第 37 卷(第 26 期)，11116-11133 页，DOI: 10.1080/10106049.2022.2046868 - 13Sebestyén, V.; Dörgo, G.; Ipkovich, Á.; Abonyi, J. Identifying the links among urban climate hazards, mitigation and adaptation actions and sustainability for future resilient cities. Urban Climate 2023, 49, 101557 DOI: 10.1016/j.uclim.2023.101557Google Scholar 谷歌学术There is no corresponding record for this reference.
13Sebestyén, V.; Dörgo, G.; Ipkovich, Á.; Abonyi, J. 识别未来韧性城市中城市气候灾害、缓解与适应行动及可持续性之间的关联。城市气候 2023, 49, 101557 DOI: 10.1016/j.uclim.2023.101557 - 14Pour, S. H.; Abd Wahab, A. K.; Shahid, S.; Asaduzzaman, M.; Dewan, A. Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges. Sustainable Cities Soc. 2020, 62, 102373 DOI: 10.1016/j.scs.2020.102373Google Scholar 谷歌学术There is no corresponding record for this reference.
14Pour, S. H.; Abd Wahab, A. K.; Shahid, S.; Asaduzzaman, M.; Dewan, A. 低影响开发技术在缓解气候变化引发的城市洪涝影响中的应用：当前趋势、问题与挑战。可持续城市与社会。2020, 62, 102373 DOI: 10.1016/j.scs.2020.102373 - 15Sanders, B. F.; Schubert, J. E.; Goodrich, K. A.; Houston, D.; Feldman, D. L.; Basolo, V.; Luke, A.; Boudreau, D.; Karlin, B.; Cheung, W. Collaborative modeling with fine-resolution data enhances flood awareness, minimizes differences in flood perception, and produces actionable flood maps. Earth’s Future 2020, 8 (1), e2019EF001391 DOI: 10.1029/2019EF001391Google Scholar 谷歌学术There is no corresponding record for this reference.
桑德斯, B. F.; 舒伯特, J. E.; 古德里奇, K. A.; 休斯顿, D.; 费尔德曼, D. L.; 巴索莫, V.; 卢克, A.; 布德罗, D.; 卡林, B.; 丘, W. 通过精细分辨率数据协作建模提升洪水意识,缩小洪水感知差异,并生成可操作的洪水地图。地球未来 2020, 8 (1), e2019EF001391 DOI: 10.1029/2019EF001391 - 16Leis, J.-L.; Kienberger, S. Climate risk and vulnerability assessment of floods in Austria: Mapping homogenous regions, hotspots and typologies. Sustainability 2020, 12 (16), 6458, DOI: 10.3390/su12166458Google Scholar 谷歌学术There is no corresponding record for this reference.
Leis, J.-L.; Kienberger, S. 奥地利洪水气候风险与脆弱性评估：绘制同质区域、热点及类型图。可持续发展 2020, 12 (16), 6458, DOI: 10.3390/su12166458 - 17Winsemius, H. C.; Van Beek, L.; Jongman, B.; Ward, P.; Bouwman, A. A framework for global river flood risk assessments. Hydrol. Earth Syst. Sci. 2013, 17 (5), 1871– 1892, DOI: 10.5194/hess-17-1871-2013
17Winsemius, H. C.; Van Beek, L.; Jongman, B.; Ward, P.; Bouwman, A. 全球河流洪水风险评估框架。水文地球系统科学, 2013, 17(5), 1871–1892, DOI: 10.5194/hess-17-1871-2013 - 18De Moel, H.; Jongman, B.; Kreibich, H.; Merz, B.; Penning-Rowsell, E.; Ward, P. J. Flood risk assessments at different spatial scales. Mitigation Adaptat. Strategies Global Change 2015, 20, 865– 890, DOI: 10.1007/s11027-015-9654-z
De Moel, H.; Jongman, B.; Kreibich, H.; Merz, B.; Penning-Rowsell, E.; Ward, P. J. 不同空间尺度下的洪水风险评估。减缓与适应策略全球变化 2015, 20, 865–890, DOI: 10.1007/s11027-015-9654-z - 19Cariolet, J.-M.; Vuillet, M.; Diab, Y. Mapping urban resilience to disasters–A review. Sustainable Cities Soc. 2019, 51, 101746 DOI: 10.1016/j.scs.2019.101746
19Cariolet, J.-M.; Vuillet, M.; Diab, Y. 城市灾害复原力映射研究综述。可持续城市与社会, 2019, 51, 101746 DOI: 10.1016/j.scs.2019.101746 - 20Wang, Q.; Taylor, J. E. Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS One 2016, 11 (1), e0147299 DOI: 10.1371/journal.pone.0147299Google Scholar 谷歌学术20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1OqtL7I&md5=505046a185f8238b0cec166c1b45fdcePatterns and limitations of urban human mobility resilience under the influence of multiple types of natural disasterWang, Qi; Taylor, John E.PLoS One (2016), 11 (1), e0147299/1-e0147299/14CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)Natural disasters pose serious threats to large urban areas, therefore understanding and predicting human movements is crit. for evaluating a population's vulnerability and resilience and developing plans for disaster evacuation, response and relief. However, only limited research has been conducted into the effect of natural disasters on human mobility. This study examines how natural disasters influence human mobility patterns in urban populations using individuals' movement data collected from Twitter. We selected fifteen destructive cases across five types of natural disaster and analyzed the human movement data before, during, and after each event, comparing the perturbed and steady state movement data. The results suggest that the power-law can describe human mobility in most cases and that human mobility patterns obsd. in steady states are often correlated with those in perturbed states, highlighting their inherent resilience. However, the quant. anal. shows that this resilience has its limits and can fail in more powerful natural disasters. The findings from this study will deepen our understanding of the interaction between urban dwellers and civil infrastructure, improve our ability to predict human movement pat- terns during natural disasters, and facilitate contingency planning by policymakers.
王琦; 泰勒, J. E. 多类型自然灾害影响下城市人类流动性的恢复力模式与局限. 公共科学图书馆·综合 2016, 11 (1), e0147299 DOI: 10.1371/journal.pone.0147299 - 21Roy, K. C.; Cebrian, M.; Hasan, S. Quantifying human mobility resilience to extreme events using geo-located social media data. EPJ Data Sci. 2019, 8 (1), 18, DOI: 10.1140/epjds/s13688-019-0196-6
Roy, K. C.; Cebrian, M.; Hasan, S. 利用地理定位社交媒体数据量化人类流动性对极端事件的恢复力。EPJ 数据科学，2019，8（1），18，DOI：10.1140/epjds/s13688-019-0196-6 - 22Kang, C.; Ma, X.; Tong, D.; Liu, Y. Intra-urban human mobility patterns: An urban morphology perspective. Phys. A 2012, 391 (4), 1702– 1717, DOI: 10.1016/j.physa.2011.11.005
康成; 马啸; 童第; 刘宇. 城市内部人类移动模式：城市形态学视角. 物理 A 2012, 391 (4), 1702–1717, DOI: 10.1016/j.physa.2011.11.005 - 23Rahimi-Golkhandan, A.; Garvin, M. J.; Wang, Q. Assessing the impact of transportation diversity on postdisaster intraurban mobility. J. Manage. Eng. 2021, 37 (1), 04020106 DOI: 10.1061/(ASCE)ME.1943-5479.0000872
拉希米-高尔坎丹, A.; 加文, M. J.; 王, Q. 评估交通多样性对灾后城市内部流动性的影响。管理工程杂志, 2021, 37(1), 04020106 DOI: 10.1061/(ASCE)ME.1943-5479.0000872 - 24Pan, X.; Han, C. S.; Dauber, K.; Law, K. H. A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. Ai Soc. 2007, 22 (2), 113– 132, DOI: 10.1007/s00146-007-0126-1
潘欣; 韩春生; 达尔伯, K.; 劳, K. H. 基于多智能体框架的紧急疏散中人类与社会行为仿真。人工智能社会学, 2007, 22(2), 113–132, DOI: 10.1007/s00146-007-0126-1 - 25Cutter, S. L.; Burton, C. G.; Emrich, C. T. Disaster resilience indicators for benchmarking baseline conditions J. Homeland Secur. Emerg. Manage. 2010; Vol. 7 1 DOI: 10.2202/1547-7355.1732 .
25Cutter, S. L.; Burton, C. G.; Emrich, C. T. 灾害恢复力指标用于基准线条件对标研究。《国土安全与应急管理杂志》2010 年；第 7 卷 1 期 DOI: 10.2202/1547-7355.1732。 - 26Cutter, S. L.; Ash, K. D.; Emrich, C. T. The geographies of community disaster resilience. Global Environ. Change 2014, 29, 65– 77, DOI: 10.1016/j.gloenvcha.2014.08.005
26Cutter, S. L.; Ash, K. D.; Emrich, C. T. 社区灾害恢复力的地理学研究。全球环境变化 2014, 29, 65–77, DOI: 10.1016/j.gloenvcha.2014.08.005 - 27Ahas, R.; Silm, S.; Saluveer, E.; Järv, O. Modelling Home and Work Locations of Populations Using Passive Mobile Positioning Data. In Location based Services and TeleCartography II; Springer, 2009; pp 301– 315.Google Scholar 谷歌学术There is no corresponding record for this reference.
27Ahas, R.; Silm, S.; Saluveer, E.; Järv, O. 利用被动式移动定位数据建模人口的居住与工作地点。在基于位置的服务与电信地理学 II 中; Springer, 2009; 第 301–315 页。 - 28Jiang, S.; Ferreira, J.; Gonzalez, M. C. Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Trans. Big Data 2017, 3 (2), 208– 219, DOI: 10.1109/TBDATA.2016.2631141Google Scholar 谷歌学术There is no corresponding record for this reference.
28. 江松; Ferreira, J.; Gonzalez, M. C. 基于移动电话数据推断的活动型人类移动模式：以新加坡为例。IEEE 大数据 Transactions 2017, 3 (2), 208–219, DOI: 10.1109/TBDATA.2016.2631141 - 29González, M. C.; Hidalgo, C. A.; Barabási, A.-L. Understanding individual human mobility patterns. Nature 2008, 453 (7196), 779– 782, DOI: 10.1038/nature06958Google Scholar 谷歌学术29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmvVGmsLg%253D&md5=18df62c848b76a959faaf93b343c2b65Understanding individual human mobility patternsGonzalez, Marta C.; Hidalgo, Cesar A.; Barabasi, Albert-LaszloNature (London, United Kingdom) (2008), 453 (7196), 779-782CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Despite their importance for urban planning, traffic forecasting and the spread of biol. and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modeling.
冈萨雷斯, M. C.; 希戈尔, C. A.; 巴拉巴西, A.-L. 理解个体人类移动模式. 自然 2008, 453 (7196), 779–782, DOI: 10.1038/nature06958 - 30Eyre, R.; De Luca, F.; Simini, F. Social media usage reveals recovery of small businesses after natural hazard events. Nat. Commun. 2020, 11 (1), 1629 DOI: 10.1038/s41467-020-15405-7Google Scholar 谷歌学术30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38zgsVSksw%253D%253D&md5=6322ab19262cd3db5848af5700983daaSocial media usage reveals recovery of small businesses after natural hazard eventsEyre Robert; Simini Filippo; De Luca Flavia; Simini FilippoNature communications (2020), 11 (1), 1629 ISSN:.The challenge of nowcasting the effect of natural hazard events (e.g., earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme events. Traditional recovery estimates, such as surveys and interviews, are usually costly, time consuming and do not scale. Here we present a methodology to indirectly estimate the post-emergency recovery status (downtime) of small businesses in urban areas looking at their online posting activity on social media. Analysing the time series of posts before and after an event, we quantify the downtime of small businesses for three natural hazard events occurred in Nepal, Puerto Rico and Mexico. A convenient and reliable method for nowcasting the post-emergency recovery status of economic activities could help local governments and decision makers to better target their interventions and distribute the available resources more effectively.
Eyre, R.; De Luca, F.; Simini, F. 社交媒体使用情况揭示了自然灾害事件后小型企业的恢复情况。《自然·通讯》2020, 11(1), 1629 DOI: 10.1038/s41467-020-15405-7 - 31Kryvasheyeu, Y.; Chen, H.; Obradovich, N.; Moro, E.; Hentenryck, P. V.; Fowler, J.; Cebrian, M. Rapid assessment of disaster damage using social media activity. Sci. Adv. 2016, 2 (3), e1500779 DOI: 10.1126/sciadv.1500779Google Scholar 谷歌学术There is no corresponding record for this reference.
31Kryvasheyeu, Y.; Chen, H.; Obradovich, N.; Moro, E.; Hentenryck, P. V.; Fowler, J.; Cebrian, M. 利用社交媒体活动快速评估灾害损失。科学进展，2016，2（3），e1500779 DOI: 10.1126/sciadv.1500779 - 32Qian, J.; Du, Y.; Yi, J.; Liang, F.; Wang, N.; Ma, T.; Pei, T. Quantifying unequal urban resilience to rains across China from location-aware big data. Nat. Hazards Earth Syst. Sci. Discuss. 2022, 1– 20Google Scholar 谷歌学术There is no corresponding record for this reference.
32 钱瑾; 杜宇; 易静; 梁峰; 王宁; 马涛; 裴韬. 基于位置感知大数据的中国城市降雨韧性不平等量化研究. 自然灾害地球系统科学讨论. 2022, 1-20 - 33Rajput, A. A.; Mostafavi, A. Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility Networks during Urban Flooding. Sci. Rep. 2023, 13 (1), 10953, DOI: 10.1038/s41598-023-37965-6Google Scholar 谷歌学术33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXhsVeju7nM&md5=7a3c2eca2df81b76d011a1fd45f93f17Latent sub-structural resilience mechanisms in temporal human mobility networks during urban floodingRajput, Akhil Anil; Mostafavi, AliScientific Reports (2023), 13 (1), 10953CODEN: SRCEC3; ISSN:2045-2322. (Nature Portfolio)Abstr.: In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examd. to det. the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resoln. aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topol. network properties indicate that the network has recovered. The findings highlight the importance of examg. the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities.
33Rajput, A. A.; Mostafavi, A. 城市洪灾期间时变人类流动网络中的潜在亚结构韧性机制。科学报告, 2023, 13(1), 10953, DOI: 10.1038/s41598-023-37965-6 - 34Cao, J.; Li, Q.; Tu, W.; Gao, Q.; Cao, R.; Zhong, C. Resolving urban mobility networks from individual travel graphs using massive-scale mobile phone tracking data. Cities 2021, 110, 103077 DOI: 10.1016/j.cities.2020.103077Google Scholar 谷歌学术There is no corresponding record for this reference.
曹军, 李强, 涂伟, 高强, 曹锐, 钟成. 利用大规模移动电话追踪数据解析个体出行图谱的城市移动网络. 城市研究 2021, 110, 103077 DOI: 10.1016/j.cities.2020.103077 - 35Song, C.; Qu, Z.; Blumm, N.; Barabási, A.-L. Limits of predictability in human mobility. Science 2010, 327 (5968), 1018– 1021, DOI: 10.1126/science.1177170Google Scholar 谷歌学术35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXitVSjtbc%253D&md5=58a83167a6ee45480b9ce21489c98473Limits of Predictability in Human MobilitySong, Chaoming; Qu, Zehui; Blumm, Nicholas; Barabasi, Albert-LaszloScience (Washington, DC, United States) (2010), 327 (5968), 1018-1021CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
宋超智; 曲哲; Blumm, N.; Barabási, A.-L. 人类流动性的可预测性极限。科学 2010, 327 (5968), 1018–1021, DOI: 10.1126/science.1177170 - 36Lu, X.; Wetter, E.; Bharti, N.; Tatem, A. J.; Bengtsson, L. Approaching the limit of predictability in human mobility. Sci. Rep. 2013, 3 (1), 2923 DOI: 10.1038/srep02923Google Scholar 谷歌学术36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2c%252FnsFWrsg%253D%253D&md5=20c7462142e29cf212b6d2590a97ce45Approaching the limit of predictability in human mobilityLu Xin; Wetter Erik; Bharti Nita; Tatem Andrew J; Bengtsson LinusScientific reports (2013), 3 (), 2923 ISSN:.In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
陆旭；Wetter, E.；Bharti, N.；Tatem, A. J.；Bengtsson, L. 人类移动性的预测极限逼近。科学报告，2013，3（1），2923 DOI: 10.1038/srep02923 - 37Perazzini, S.; Metulini, R.; Carpita, M. Statistical indicators based on mobile phone and street maps data for risk management in small urban areas. Stat. Methods Appl. 2023, 1– 28, DOI: 10.1007/s10260-023-00719-9Google Scholar 谷歌学术There is no corresponding record for this reference.
Perazzini, S.; Metulini, R.; Carpita, M. 基于移动电话和街道地图数据的统计指标在小城市区域风险管理中的应用。统计方法与应用 2023, 1-28, DOI: 10.1007/s10260-023-00719-9 - 38Lai, S.; Ruktanonchai, N. W.; Zhou, L.; Prosper, O.; Luo, W.; Floyd, J. R.; Wesolowski, A.; Santillana, M.; Zhang, C.; Du, X. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature 2020, 585 (7825), 410– 413, DOI: 10.1038/s41586-020-2293-xGoogle Scholar 谷歌学术38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVCju7jM&md5=2fd050be767ba9099cf3550d8147e228Effect of non-pharmaceutical interventions to contain COVID-19 in ChinaLai, Shengjie; Ruktanonchai, Nick W.; Zhou, Liangcai; Prosper, Olivia; Luo, Wei; Floyd, Jessica R.; Wesolowski, Amy; Santillana, Mauricio; Zhang, Chi; Du, Xiangjun; Yu, Hongjie; Tatem, Andrew J.Nature (London, United Kingdom) (2020), 585 (7825), 410-413CODEN: NATUAS; ISSN:0028-0836. (Nature Research)On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quant. research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiol. data on COVID-19 and anonymized data on human movement4,5, we develop a modeling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We est. that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 Feb. 2020. Without non-pharmaceutical interventions, we predict that the no. of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 Feb. 2020, and we find that the effectiveness of different interventions varied. We est. that early detection and isolation of cases prevented more infections than did travel restrictions and contact redns., but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 Feb. 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on av. 25% redn. in contact between individuals that continues until late Apr. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.
赖圣杰; Ruktanonchai, N. W.; 周麟; Prosper, O.; 罗卫; Floyd, J. R.; Wesolowski, A.; Santillana, M.; 张超; 杜欣. 中国实施非药物干预措施对遏制 COVID-19 的影响. 自然 2020, 585(7825), 410–413, DOI: 10.1038/s41586-020-2293-x - 39Lu, X.; Bengtsson, L.; Holme, P. Predictability of population displacement after the 2010 Haiti earthquake. Proc. Natl. Acad. Sci. U.S.A. 2012, 109 (29), 11576– 11581, DOI: 10.1073/pnas.1203882109Google Scholar 谷歌学术39https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xht1Ciu7zM&md5=a2fbce6911823d81ab0f2614050ba70fPredictability of population displacement after the 2010 Haiti earthquakeLu, Xin; Bengtsson, Linus; Holme, PetterProceedings of the National Academy of Sciences of the United States of America (2012), 109 (29), 11576-11581, S11576/1-S11576/8CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Most severe disasters cause large population movements. These movements make it difficult for relief organizations to efficiently reach people in need. Understanding and predicting the locations of affected people during disasters is key to effective humanitarian relief operations and to long-term societal reconstruction. We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, to 341 d after the devastating Haiti earthquake of Jan. 12, 2010. Nineteen days after the earthquake, population movements had caused the population of the capital Port-au-Prince to decrease by an estd. 23%. Both the travel distances and size of people's movement trajectories grew after the earthquake. These findings, in combination with the disorder that was present after the disaster, suggest that people's movements would have become less predictable. Instead, the predictability of people's trajectories remained high and even increased slightly during the three-month period after the earthquake. Moreover, the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds. For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought.
陆曦; Bengtsson, L.; Holme, P. 2010 年海地地震后人口迁移的可预测性研究. 美国国家科学院院刊 2012, 109 (29), 11576–11581, DOI: 10.1073/pnas.1203882109 - 40Blanford, J. I.; Huang, Z.; Savelyev, A.; MacEachren, A. M. Geo-located tweets. Enhancing mobility maps and capturing cross-border movement. PLoS One 2015, 10 (6), e0129202 DOI: 10.1371/journal.pone.0129202Google Scholar 谷歌学术40https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhsVKlsrjP&md5=3adf21ff8c72c2fc84caf2f4d15b3116Geo-Located tweets. enhancing mobility maps and capturing cross-border movementBlanford, Justine I.; Huang, Zhuojie; Savelyev, Alexander; MacEachren, Alan M.PLoS One (2015), 10 (6), e0129202/1-e0129202/16CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)Capturing human movement patterns across political borders is difficult and this difficulty highlights the need to investigate alternative data streams. With the advent of smart phones and the ability to attach accurate coordinates to Twitter messages, users leave a geog. digital footprint of their movement when posting tweets. In this study we analyzed 10 mo of geo-located tweets for Kenya and were able to capture movement of people at different temporal (daily to periodic) and spatial (local, national to international) scales. We were also able to capture both long and short distances travelled, highlighting regional connections and cross-border movement between Kenya and the surrounding countries. The findings from this study has broad implications for studying movement patterns and mapping inter/intra-region movement dynamics.
布兰福德, J. I.; 黄, Z.; 萨维列夫, A.; 麦凯奇伦, A. M. 地理定位推文：增强移动地图与捕捉跨境流动。公共科学图书馆·综合 2015, 10 (6), e0129202 DOI: 10.1371/journal.pone.0129202 - 41González, M. C.; Hidalgo, C. A.; Barabasi, A.-L. Understanding individual human mobility patterns. Nature 2008, 453 (7196), 779– 782, DOI: 10.1038/nature06958Google Scholar 谷歌学术41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmvVGmsLg%253D&md5=18df62c848b76a959faaf93b343c2b65Understanding individual human mobility patternsGonzalez, Marta C.; Hidalgo, Cesar A.; Barabasi, Albert-LaszloNature (London, United Kingdom) (2008), 453 (7196), 779-782CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Despite their importance for urban planning, traffic forecasting and the spread of biol. and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modeling.
冈萨雷斯, M. C.; 希达尔戈, C. A.; 巴拉巴西, A.-L. 理解个体人类移动模式. 自然 2008, 453 (7196), 779–782, DOI: 10.1038/nature06958 - 42Buckee, C.; Noor, A.; Sattenspiel, L. Thinking clearly about social aspects of infectious disease transmission. Nature 2021, 595 (7866), 205– 213, DOI: 10.1038/s41586-021-03694-xGoogle Scholar 谷歌学术42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXhsVGnu7%252FN&md5=ffd6cc9f7d26943116c97c050582eb5fThinking clearly about social aspects of infectious disease transmissionBuckee, Caroline; Noor, Abdisalan; Sattenspiel, LisaNature (London, United Kingdom) (2021), 595 (7866), 205-213CODEN: NATUAS; ISSN:0028-0836. (Nature Portfolio)Abstr.: Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn det. epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behavior, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiol. are hard to quantify and have limited the generalizability of modeling frameworks in a policy context, new sources of data on relevant aspects of human behavior are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioral drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behavior in relation to infectious diseases.
Buckee, C.; Noor, A.; Sattenspiel, L. 清晰思考传染病传播的社会层面问题。自然 2021, 595 (7866), 205–213, DOI: 10.1038/s41586-021-03694-x - 43Mu, X.; Huang, A.; Wu, Y.; Xu, Q.; Zheng, Y.; Lin, H.; Fang, D.; Zhang, X.; Tang, Y.; Cai, S. Characteristics of the precipitation diurnal variation and underlying mechanisms over jiangsu, eastern China, during warm season. Front. Earth Sci. 2021, 9, 703071 DOI: 10.3389/feart.2021.703071Google Scholar 谷歌学术There is no corresponding record for this reference.
穆旭, 黄安, 吴毅, 徐强, 郑宇, 林辉, 方丹, 张雪, 唐颖, 蔡珊. 中国东部江苏省暖季降水日变化特征及潜在机制. 地球科学前沿, 2021, 9, 703071. DOI: 10.3389/feart.2021.703071 - 44Zhou, Q.; Qu, S.; Ding, J.; Liu, M.; Huang, X.; Bi, J.; Ji, J. S.; Kinney, P. L. Association between PM2. 5 and daily pharmacy visit tendency in China: A time series analysis using mobile phone cellular signaling data. J. Cleaner Prod. 2022, 340, 130688 DOI: 10.1016/j.jclepro.2022.130688Google Scholar 谷歌学术44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XmsFWmsr4%253D&md5=7002e569433cc33596e1a220f7a3e00dAssociation between PM2.5 and daily pharmacy visit tendency in China: A time series analysis using mobile phone cellular signaling dataZhou, Qi; Qu, Shen; Ding, Jiongchao; Liu, Miaomiao; Huang, Xianjin; Bi, Jun; Ji, John S.; Kinney, Patrick L.Journal of Cleaner Production (2022), 340 (), 130688CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)While much is known about the effects of PM2.5 pollution on severe health outcomes, less is known about the heterogeneous impacts of air pollution on less severe health effects experienced by large nos. of people, such as those resulting in pharmacy visits. Based on anonymized daily pharmacy visits extd. from about 136,000 mobile phone station macrocells and 28,170 pharmacy locations, we used the generalized additive model and meta-anal. to investigate the effects of PM2.5 exposure on pharmacy visit tendencies (PVTs), as a proxy variable of health facility utilization, across regions, groups and social contexts. Overall, PVTs increased by 0.338% (95% CI: 0.335%-0.342%) for per 10μg/m3 increases in PM2.5. The exposure-response curve was nonlinear, having a sharp slope below 60μg/m3 and then flattening. Sensitivity analyses showed that results were robust to modeling choices and existence of noise in captured PVT data. At pharmacy level, higher percentage increases in PVTs were obsd. in near-residency pharmacies (0.474%), designated medical insurance pharmacies (0.360%), and pharmacies in urban areas (0.457%) than their counterparts (0.156%, 0.321%, 0.180%). This indicates that social contexts, including inconvenient access to the pharmacy and limited social medical insurance coverage, inhibited residents from utilizing health facilities for the health care needed. Interactions of social contexts led to strong heterogeneity among cities, between rural and urban regions as well as across communities, which call for differentiated health resources allocations to ensure health equity.
周琦; 屈伸; 丁杰; 刘敏; 黄晓; 毕军; 季建林; Kinney, P. L. PM2.5 与我国每日药店访问倾向的关联性研究：基于移动电话蜂窝信号数据的时间序列分析. 清洁生产杂志, 2022, 340, 130688. DOI: 10.1016/j.jclepro.2022.130688 - 45Xin, X.; Wu, T.; Zhang, J.; Yao, J.; Fang, Y. Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int. J. Climatol. 2020, 40 (15), 6423– 6440, DOI: 10.1002/joc.6590Google Scholar 谷歌学术There is no corresponding record for this reference.
45Xin, X.; Wu, T.; Zhang, J.; Yao, J.; Fang, Y. CMIP6 与 CMIP5 对中国及东亚夏季风降水模拟的比较研究。国际气候学杂志, 2020, 40(15), 6423–6440, DOI: 10.1002/joc.6590 - 46Vallis, O.; Hochenbaum, J.; Kejariwal, A. A Novel Technique for Long-Term Anomaly Detection in the Cloud. In 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 14) 2014.Google Scholar 谷歌学术There is no corresponding record for this reference.
Vallis, O.; Hochenbaum, J.; Kejariwal, A. 云环境中长期异常检测的新技术。在第六届 USENIX 云计算热点话题研讨会(HotCloud 14) 2014 年。 - 47Hersbach, H.; Bell, B.; Berrisford, P.; Biavati, G.; Horányi, A.; Muñoz Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Rozum, I. ERA5 hly Data on Single Levels from 1979 to Present Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 2018; Vol. 10.Google Scholar 谷歌学术There is no corresponding record for this reference.
47Hersbach, H.; Bell, B.; Berrisford, P.; Biavati, G.; Horányi, A.; Muñoz Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Rozum, I. 从 1979 年至今的 ERA5 小时数据单层级资料，由哥白尼气候变化服务(C3S)气候数据存储(CDS)提供，2018 年；第 10 卷。 - 48Ye, H.; China Meteorological News Agency. Rainfall Rating 2018 https://www.cma.gov.cn/2011xzt/2018zt/20100728/2010072804/201807/t20180706_472586.html. accessed Oct 23, 2022.Google Scholar 谷歌学术There is no corresponding record for this reference.
48 叶, H.; 中国气象新闻社. 2018 年降雨评级 https://www.cma.gov.cn/2011xzt/2018zt/20100728/2010072804/201807/t20180706_472586.html. 访问于 2022 年 10 月 23 日. - 49Dujardin, S.; Jacques, D.; Steele, J.; Linard, C. Mobile phone data for urban climate change adaptation: Reviewing applications, opportunities and key challenges. Sustainability 2020, 12 (4), 1501, DOI: 10.3390/su12041501Google Scholar 谷歌学术There is no corresponding record for this reference.
49Dujardin, S.; Jacques, D.; Steele, J.; Linard, C. 移动电话数据在城市气候变化适应中的应用：回顾应用、机遇与关键挑战。可持续发展 2020, 12 (4), 1501, DOI: 10.3390/su12041501 - 50Ma, S.; Long, Y. Identifying spatial cities in China at the community scale. J. Urban Reg. Plan 2019, 11, 37– 50Google Scholar 谷歌学术There is no corresponding record for this reference.
50Ma, S.; Long, Y. 识别社区尺度下的中国空间城市。城市与区域规划杂志，2019，11，37-50。 - 51Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 2009, 90 (2), 363– 368, DOI: 10.1890/08-1034.1Google Scholar 谷歌学术51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1M3jsF2msA%253D%253D&md5=2a3c123232b4ff6e3af8c8a38f2ca648Confirmatory path analysis in a generalized multilevel contextShipley BillEcology (2009), 90 (2), 363-8 ISSN:0012-9658.This paper describes how to test, and potentially falsify, a multivariate causal hypothesis involving only observed variables (i.e., a path analysis) when the data have a hierarchical or multilevel structure, when different variables are potentially defined at different levels of such a hierarchy, and when different variables have different sampling distributions. The test is a generalization of Shipley's d-sep test and can be conducted using standard statistical programs capable of fitting generalized mixed models.
51Shipley, B. 在广义多层次背景下的确认路径分析。生态学 2009, 90 (2), 363–368, DOI: 10.1890/08-1034.1 - 52Shipley, B. A new inferential test for path models based on directed acyclic graphs. Struct. Equation Model. 2000, 7 (2), 206– 218, DOI: 10.1207/S15328007SEM0702_4Google Scholar 谷歌学术There is no corresponding record for this reference.
52Shipley, B. 基于有向无环图的新型路径模型推断检验。结构方程模型，2000，7（2），206–218，DOI: 10.1207/S15328007SEM0702_4 - 53Johnson, C.; Blackburn, S. Advocacy for urban resilience: UNISDR’s making cities resilient campaign. Environ. Urbanization 2014, 26 (1), 29– 52, DOI: 10.1177/0956247813518684Google Scholar 谷歌学术There is no corresponding record for this reference.
约翰逊, C.; 布莱克本, S. 城市韧性倡导：联合国国际减灾战略的“让城市更具韧性”运动。环境城市化, 2014, 26(1), 29-52, DOI: 10.1177/0956247813518684 - 54Mehmood, H.; Conway, C.; Perera, D. Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform. Atmosphere 2021, 12 (7), 866, DOI: 10.3390/atmos12070866Google Scholar 谷歌学术There is no corresponding record for this reference.
Mehmood, H.; Conway, C.; Perera, D. 利用 Landsat 与 Google Earth Engine 云平台进行洪泛区地图绘制。大气 2021, 12 (7), 866, DOI: 10.3390/atmos12070866 - 55Anselin, L. Local indicators of spatial association─LISA. Geogr. Anal. 1995, 27 (2), 93– 115, DOI: 10.1111/j.1538-4632.1995.tb00338.xGoogle Scholar 谷歌学术There is no corresponding record for this reference.
安瑟林, L. 局部空间关联指标——LISA. 地理分析, 1995, 27(2), 93–115, DOI: 10.1111/j.1538-4632.1995.tb00338.x - 56Metulini, R.; Carpita, M. Modeling and forecasting traffic flows with mobile phone big data in flooding risk areas to support a data-driven decision making. Ann. Oper. Res. 2023, 1– 26, DOI: 10.1007/s10479-023-05195-8Google Scholar 谷歌学术There is no corresponding record for this reference.
Metulini, R.; Carpita, M. 利用手机大数据在洪水风险区域建模与预测交通流量以支持数据驱动决策。运筹学年报，2023，1-26，DOI: 10.1007/s10479-023-05195-8 - 57Wang, B.; Loo, B. P.; Zhen, F.; Xi, G. Urban resilience from the lens of social media data: Responses to urban flooding in Nanjing, China. Cities 2020, 106, 102884 DOI: 10.1016/j.cities.2020.102884Google Scholar 谷歌学术There is no corresponding record for this reference.
王斌; 卢秉恒; 郑峰; 奚歌. 社交媒体数据视角下的城市韧性：南京城市洪涝响应研究. 城市研究 2020, 106, 102884 DOI: 10.1016/j.cities.2020.102884
Cited By 被引用次数
Click to copy section link
点击复制章节链接Section link copied!
点击复制章节链接Section link copied!
This article has not yet been cited by other publications.
本文尚未被其他出版物引用。
Article Views 文章浏览量
1557
Altmetric
-
Citations 引文
-
Learn about these metrics
了解这些指标
了解这些指标
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.
Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.
The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.
Recommended Articles 推荐文章
Mitigation Measures Could Aggravate Unbalanced Nitrogen and Phosphorus Emissions from Land-Use Activities
缓解措施可能加剧土地利用活动中氮磷排放的不平衡Photoaged Tire Wear Particles Leading to the Oxidative Damage on Earthworms (Eisenia fetida) by Disrupting the Antioxidant Defense System: The Definitive Role of Environmental Free Radicals
光老化轮胎磨损颗粒通过破坏抗氧化防御系统对蚯蚓（赤子爱胜蚓）造成的氧化损伤：环境自由基的决定性作用Formaldehyde Exposure Racial Disparities in Southeast Texas
东南德克萨斯州甲醛暴露的种族差异Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors
大量全氟及多氟烷基物质与十种核受体相互作用的预测AhR Agonistic Components in Urban Particulate Matter Regulate Astrocytic Activation and Function
城市颗粒物中的 AhR 激动成分调控星形胶质细胞活化与功能
Abstract 摘要
Figure 1 图 1
Figure 1. Data processing steps that calculate grid-level mobility changes after heavy rainfalls. (A) Spatial overlay of cell tower, 500 m fishnet grid, and 25 km precipitation grid. (B) Conceptualization of the process to calculate tower-level visit changes due to the shock of rainfall events.
图 1. 计算暴雨后网格级移动性变化的数据处理步骤。(A) 基站、500 米渔网网格与 25 公里降水网格的空间叠加。(B) 概念化降雨事件冲击下计算基站级访问变化的过程。Figure 2 图 2
Figure 2. Mapping human mobility changes in response to rainfall events. (A) Changes in human mobility (i.e., indicator visitshock) after rainfall at the grid level (500 m). Urban boundaries with dense populations are marked by bold solid lines. (B, C) Enlarged view of Nanjing and Suzhou urban agglomeration areas.
图 2. 降雨事件响应下人类流动性变化图。(A) 降雨后网格层面（500 米）人类流动性变化（即指标 visitshock）。人口密集的城市边界以粗实线标示。(B, C) 南京与苏州城市群区域的放大视图。Figure 3 图 3
Figure 3. Changes in human mobility after heavy rainfall at the prefecture level and potential drivers. (A) Percent changes in mobility at the prefecture level. (B) Results of piecewise SEM between changes in mobility and eight explanatory variables. Colors are used to distinguish the direction of correlation (orange for positive correlation and green for negative correlation) (*p < 0.05; **p < 0.01; ***p < 0.001). (C) Direct and indirect effects of the four latent variables.
图 3. 强降雨后地级市层面人类流动性的变化及其潜在驱动因素。(A) 地级市层面流动性的百分比变化。(B) 流动性变化与八个解释变量之间的分段结构方程模型(SEM)结果。颜色用于区分相关方向（橙色表示正相关，绿色表示负相关）(*p < 0.05; **p < 0.01; ***p < 0.001)。(C) 四个潜在变量的直接与间接效应。Figure 4 图 4
Figure 4. Change in urban flood risk exposure per city and its association with human mobility after heavy rainfalls. (A, B) Changes in flood risk exposure after heavy rainfalls. The size of the point is proportional to the change in the number of people staying in areas at risk of flooding. The color of the point indicates the direction of change. (C) Process of overlaying four layers. (D) Histogram of the impacted population in the flood-prone grid.
图 4. 暴雨后各城市内涝风险暴露变化及其与人口流动的关联。(A, B) 暴雨后内涝风险暴露的变化。点的尺寸与滞留在易涝区域人数的变化成正比，点的颜色表示变化的方向。(C) 四层叠加过程。(D) 易涝网格中受影响人口的直方图。References
This article references 57 other publications.
- 1Pörtner, H.-O.; Roberts, D. C.; Adams, H.; Adler, C.; Aldunce, P.; Ali, E.; Begum, R. A.; Betts, R.; Kerr, R. B.; Biesbroek, R. Climate Change: Impacts, Adaptation, and Vulnerability 2022; IPCC Sixth Assessment Report , 2022.There is no corresponding record for this reference.
- 2Harrison, C. G.; Williams, P. R. A systems approach to natural disaster resilience. Simul. Modell. Pract. Theory 2016, 65, 11– 31, DOI: 10.1016/j.simpat.2016.02.0082https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38zltVKhug%253D%253D&md5=418cef3fe7045d494747a382467eea73A systems approach to natural disaster resilienceHarrison Colin G; Williams Peter RSimulation modelling practice and theory (2016), 65 (), 11-31 ISSN:1569-190X.The frequency, social, and economic impacts of natural disasters show exponential increases in recent decades. Cities and countries around the world have begun to realize that these events are no longer "hundred year" storms, but repeat within a few years. As urbanisation continues throughout this century, more and more people and more economic activity will be concentrated in at-risk areas; especially as new arrivals in cities throughout Asia and Africa are likely to be concentrated in the highest risk districts, much as they often are in North America and Europe today. This article reviews recent growth of natural disasters and considers how a systems approach can improve approaches to mitigation and adaptation of these risks and to recovery from such events.
(acccessed 2022–10–23)
- 5Winsemius, H. C.; Aerts, J. C.; Van Beek, L. P.; Bierkens, M. F.; Bouwman, A.; Jongman, B.; Kwadijk, J. C.; Ligtvoet, W.; Lucas, P. L.; Van Vuuren, D. P.; Ward, P. J. Global drivers of future river flood risk. Nat. Clim. Change 2016, 6 (4), 381– 385, DOI: 10.1038/nclimate2893There is no corresponding record for this reference.
- 6Rentschler, J.; Salhab, M.; Jafino, B. A. Flood exposure and poverty in 188 countries. Nat. Commun. 2022, 13 (1), 3527 DOI: 10.1038/s41467-022-30727-46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhslajsL3E&md5=ee2aabcbf1e383f0904ea316e6716b8cFlood exposure and poverty in 188 countriesRentschler, Jun; Salhab, Melda; Jafino, Bramka ArgaNature Communications (2022), 13 (1), 3527CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)Abstr.: Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global ests. of the no. of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-yr floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world's flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development.
- 7Alexander, L. V.; Zhang, X.; Peterson, T. C.; Caesar, J.; Gleason, B.; Klein Tank, A.; Haylock, M.; Collins, D.; Trewin, B.; Rahimzadeh, F. Global observed changes in daily climate extremes of temperature and precipitation J. Geophys. Res.: Atmos. 2006; Vol. 111 D5 DOI: 10.1029/2005JD006290 .There is no corresponding record for this reference.
- 9Kuzma, S.; Luo, T. The Number of People Affected by Floods Will Double between 2010 and 2030; World Resources Institude, 2020. (accessed 2022–10–23).There is no corresponding record for this reference.
- 10Assembly, U. G.. Transforming our world: the 2030 agenda for sustainable development, 21 October 2015 2015. Retrieved from.There is no corresponding record for this reference.
- 12Mansour, M. M.; Ibrahim, M. G.; Fujii, M.; Nasr, M. Sustainable development goals (SDGs) associated with flash flood hazard mapping and management measures through morphometric evaluation. Geocarto Int. 2022, 37 (26), 11116– 11133, DOI: 10.1080/10106049.2022.2046868There is no corresponding record for this reference.
- 13Sebestyén, V.; Dörgo, G.; Ipkovich, Á.; Abonyi, J. Identifying the links among urban climate hazards, mitigation and adaptation actions and sustainability for future resilient cities. Urban Climate 2023, 49, 101557 DOI: 10.1016/j.uclim.2023.101557There is no corresponding record for this reference.
- 14Pour, S. H.; Abd Wahab, A. K.; Shahid, S.; Asaduzzaman, M.; Dewan, A. Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges. Sustainable Cities Soc. 2020, 62, 102373 DOI: 10.1016/j.scs.2020.102373There is no corresponding record for this reference.
- 15Sanders, B. F.; Schubert, J. E.; Goodrich, K. A.; Houston, D.; Feldman, D. L.; Basolo, V.; Luke, A.; Boudreau, D.; Karlin, B.; Cheung, W. Collaborative modeling with fine-resolution data enhances flood awareness, minimizes differences in flood perception, and produces actionable flood maps. Earth’s Future 2020, 8 (1), e2019EF001391 DOI: 10.1029/2019EF001391There is no corresponding record for this reference.
- 16Leis, J.-L.; Kienberger, S. Climate risk and vulnerability assessment of floods in Austria: Mapping homogenous regions, hotspots and typologies. Sustainability 2020, 12 (16), 6458, DOI: 10.3390/su12166458There is no corresponding record for this reference.
- 17Winsemius, H. C.; Van Beek, L.; Jongman, B.; Ward, P.; Bouwman, A. A framework for global river flood risk assessments. Hydrol. Earth Syst. Sci. 2013, 17 (5), 1871– 1892, DOI: 10.5194/hess-17-1871-2013There is no corresponding record for this reference.
- 18De Moel, H.; Jongman, B.; Kreibich, H.; Merz, B.; Penning-Rowsell, E.; Ward, P. J. Flood risk assessments at different spatial scales. Mitigation Adaptat. Strategies Global Change 2015, 20, 865– 890, DOI: 10.1007/s11027-015-9654-zThere is no corresponding record for this reference.
- 19Cariolet, J.-M.; Vuillet, M.; Diab, Y. Mapping urban resilience to disasters–A review. Sustainable Cities Soc. 2019, 51, 101746 DOI: 10.1016/j.scs.2019.101746There is no corresponding record for this reference.
- 20Wang, Q.; Taylor, J. E. Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS One 2016, 11 (1), e0147299 DOI: 10.1371/journal.pone.014729920https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1OqtL7I&md5=505046a185f8238b0cec166c1b45fdcePatterns and limitations of urban human mobility resilience under the influence of multiple types of natural disasterWang, Qi; Taylor, John E.PLoS One (2016), 11 (1), e0147299/1-e0147299/14CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)Natural disasters pose serious threats to large urban areas, therefore understanding and predicting human movements is crit. for evaluating a population's vulnerability and resilience and developing plans for disaster evacuation, response and relief. However, only limited research has been conducted into the effect of natural disasters on human mobility. This study examines how natural disasters influence human mobility patterns in urban populations using individuals' movement data collected from Twitter. We selected fifteen destructive cases across five types of natural disaster and analyzed the human movement data before, during, and after each event, comparing the perturbed and steady state movement data. The results suggest that the power-law can describe human mobility in most cases and that human mobility patterns obsd. in steady states are often correlated with those in perturbed states, highlighting their inherent resilience. However, the quant. anal. shows that this resilience has its limits and can fail in more powerful natural disasters. The findings from this study will deepen our understanding of the interaction between urban dwellers and civil infrastructure, improve our ability to predict human movement pat- terns during natural disasters, and facilitate contingency planning by policymakers.
- 21Roy, K. C.; Cebrian, M.; Hasan, S. Quantifying human mobility resilience to extreme events using geo-located social media data. EPJ Data Sci. 2019, 8 (1), 18, DOI: 10.1140/epjds/s13688-019-0196-6There is no corresponding record for this reference.
- 22Kang, C.; Ma, X.; Tong, D.; Liu, Y. Intra-urban human mobility patterns: An urban morphology perspective. Phys. A 2012, 391 (4), 1702– 1717, DOI: 10.1016/j.physa.2011.11.005There is no corresponding record for this reference.
- 23Rahimi-Golkhandan, A.; Garvin, M. J.; Wang, Q. Assessing the impact of transportation diversity on postdisaster intraurban mobility. J. Manage. Eng. 2021, 37 (1), 04020106 DOI: 10.1061/(ASCE)ME.1943-5479.0000872There is no corresponding record for this reference.
- 24Pan, X.; Han, C. S.; Dauber, K.; Law, K. H. A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. Ai Soc. 2007, 22 (2), 113– 132, DOI: 10.1007/s00146-007-0126-1There is no corresponding record for this reference.
- 25Cutter, S. L.; Burton, C. G.; Emrich, C. T. Disaster resilience indicators for benchmarking baseline conditions J. Homeland Secur. Emerg. Manage. 2010; Vol. 7 1 DOI: 10.2202/1547-7355.1732 .There is no corresponding record for this reference.
- 26Cutter, S. L.; Ash, K. D.; Emrich, C. T. The geographies of community disaster resilience. Global Environ. Change 2014, 29, 65– 77, DOI: 10.1016/j.gloenvcha.2014.08.005There is no corresponding record for this reference.
- 27Ahas, R.; Silm, S.; Saluveer, E.; Järv, O. Modelling Home and Work Locations of Populations Using Passive Mobile Positioning Data. In Location based Services and TeleCartography II; Springer, 2009; pp 301– 315.There is no corresponding record for this reference.
- 28Jiang, S.; Ferreira, J.; Gonzalez, M. C. Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Trans. Big Data 2017, 3 (2), 208– 219, DOI: 10.1109/TBDATA.2016.2631141There is no corresponding record for this reference.
- 29González, M. C.; Hidalgo, C. A.; Barabási, A.-L. Understanding individual human mobility patterns. Nature 2008, 453 (7196), 779– 782, DOI: 10.1038/nature0695829https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmvVGmsLg%253D&md5=18df62c848b76a959faaf93b343c2b65Understanding individual human mobility patternsGonzalez, Marta C.; Hidalgo, Cesar A.; Barabasi, Albert-LaszloNature (London, United Kingdom) (2008), 453 (7196), 779-782CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Despite their importance for urban planning, traffic forecasting and the spread of biol. and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modeling.
- 30Eyre, R.; De Luca, F.; Simini, F. Social media usage reveals recovery of small businesses after natural hazard events. Nat. Commun. 2020, 11 (1), 1629 DOI: 10.1038/s41467-020-15405-730https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38zgsVSksw%253D%253D&md5=6322ab19262cd3db5848af5700983daaSocial media usage reveals recovery of small businesses after natural hazard eventsEyre Robert; Simini Filippo; De Luca Flavia; Simini FilippoNature communications (2020), 11 (1), 1629 ISSN:.The challenge of nowcasting the effect of natural hazard events (e.g., earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme events. Traditional recovery estimates, such as surveys and interviews, are usually costly, time consuming and do not scale. Here we present a methodology to indirectly estimate the post-emergency recovery status (downtime) of small businesses in urban areas looking at their online posting activity on social media. Analysing the time series of posts before and after an event, we quantify the downtime of small businesses for three natural hazard events occurred in Nepal, Puerto Rico and Mexico. A convenient and reliable method for nowcasting the post-emergency recovery status of economic activities could help local governments and decision makers to better target their interventions and distribute the available resources more effectively.
- 31Kryvasheyeu, Y.; Chen, H.; Obradovich, N.; Moro, E.; Hentenryck, P. V.; Fowler, J.; Cebrian, M. Rapid assessment of disaster damage using social media activity. Sci. Adv. 2016, 2 (3), e1500779 DOI: 10.1126/sciadv.1500779There is no corresponding record for this reference.
- 32Qian, J.; Du, Y.; Yi, J.; Liang, F.; Wang, N.; Ma, T.; Pei, T. Quantifying unequal urban resilience to rains across China from location-aware big data. Nat. Hazards Earth Syst. Sci. Discuss. 2022, 1– 20There is no corresponding record for this reference.
- 33Rajput, A. A.; Mostafavi, A. Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility Networks during Urban Flooding. Sci. Rep. 2023, 13 (1), 10953, DOI: 10.1038/s41598-023-37965-633https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXhsVeju7nM&md5=7a3c2eca2df81b76d011a1fd45f93f17Latent sub-structural resilience mechanisms in temporal human mobility networks during urban floodingRajput, Akhil Anil; Mostafavi, AliScientific Reports (2023), 13 (1), 10953CODEN: SRCEC3; ISSN:2045-2322. (Nature Portfolio)Abstr.: In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examd. to det. the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resoln. aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topol. network properties indicate that the network has recovered. The findings highlight the importance of examg. the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities.
- 34Cao, J.; Li, Q.; Tu, W.; Gao, Q.; Cao, R.; Zhong, C. Resolving urban mobility networks from individual travel graphs using massive-scale mobile phone tracking data. Cities 2021, 110, 103077 DOI: 10.1016/j.cities.2020.103077There is no corresponding record for this reference.
- 35Song, C.; Qu, Z.; Blumm, N.; Barabási, A.-L. Limits of predictability in human mobility. Science 2010, 327 (5968), 1018– 1021, DOI: 10.1126/science.117717035https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXitVSjtbc%253D&md5=58a83167a6ee45480b9ce21489c98473Limits of Predictability in Human MobilitySong, Chaoming; Qu, Zehui; Blumm, Nicholas; Barabasi, Albert-LaszloScience (Washington, DC, United States) (2010), 327 (5968), 1018-1021CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
- 36Lu, X.; Wetter, E.; Bharti, N.; Tatem, A. J.; Bengtsson, L. Approaching the limit of predictability in human mobility. Sci. Rep. 2013, 3 (1), 2923 DOI: 10.1038/srep0292336https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2c%252FnsFWrsg%253D%253D&md5=20c7462142e29cf212b6d2590a97ce45Approaching the limit of predictability in human mobilityLu Xin; Wetter Erik; Bharti Nita; Tatem Andrew J; Bengtsson LinusScientific reports (2013), 3 (), 2923 ISSN:.In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
- 38Lai, S.; Ruktanonchai, N. W.; Zhou, L.; Prosper, O.; Luo, W.; Floyd, J. R.; Wesolowski, A.; Santillana, M.; Zhang, C.; Du, X. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature 2020, 585 (7825), 410– 413, DOI: 10.1038/s41586-020-2293-x38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVCju7jM&md5=2fd050be767ba9099cf3550d8147e228Effect of non-pharmaceutical interventions to contain COVID-19 in ChinaLai, Shengjie; Ruktanonchai, Nick W.; Zhou, Liangcai; Prosper, Olivia; Luo, Wei; Floyd, Jessica R.; Wesolowski, Amy; Santillana, Mauricio; Zhang, Chi; Du, Xiangjun; Yu, Hongjie; Tatem, Andrew J.Nature (London, United Kingdom) (2020), 585 (7825), 410-413CODEN: NATUAS; ISSN:0028-0836. (Nature Research)On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quant. research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiol. data on COVID-19 and anonymized data on human movement4,5, we develop a modeling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We est. that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 Feb. 2020. Without non-pharmaceutical interventions, we predict that the no. of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 Feb. 2020, and we find that the effectiveness of different interventions varied. We est. that early detection and isolation of cases prevented more infections than did travel restrictions and contact redns., but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 Feb. 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on av. 25% redn. in contact between individuals that continues until late Apr. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.
- 39Lu, X.; Bengtsson, L.; Holme, P. Predictability of population displacement after the 2010 Haiti earthquake. Proc. Natl. Acad. Sci. U.S.A. 2012, 109 (29), 11576– 11581, DOI: 10.1073/pnas.120388210939https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xht1Ciu7zM&md5=a2fbce6911823d81ab0f2614050ba70fPredictability of population displacement after the 2010 Haiti earthquakeLu, Xin; Bengtsson, Linus; Holme, PetterProceedings of the National Academy of Sciences of the United States of America (2012), 109 (29), 11576-11581, S11576/1-S11576/8CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Most severe disasters cause large population movements. These movements make it difficult for relief organizations to efficiently reach people in need. Understanding and predicting the locations of affected people during disasters is key to effective humanitarian relief operations and to long-term societal reconstruction. We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, to 341 d after the devastating Haiti earthquake of Jan. 12, 2010. Nineteen days after the earthquake, population movements had caused the population of the capital Port-au-Prince to decrease by an estd. 23%. Both the travel distances and size of people's movement trajectories grew after the earthquake. These findings, in combination with the disorder that was present after the disaster, suggest that people's movements would have become less predictable. Instead, the predictability of people's trajectories remained high and even increased slightly during the three-month period after the earthquake. Moreover, the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds. For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought.
- 40Blanford, J. I.; Huang, Z.; Savelyev, A.; MacEachren, A. M. Geo-located tweets. Enhancing mobility maps and capturing cross-border movement. PLoS One 2015, 10 (6), e0129202 DOI: 10.1371/journal.pone.012920240https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhsVKlsrjP&md5=3adf21ff8c72c2fc84caf2f4d15b3116Geo-Located tweets. enhancing mobility maps and capturing cross-border movementBlanford, Justine I.; Huang, Zhuojie; Savelyev, Alexander; MacEachren, Alan M.PLoS One (2015), 10 (6), e0129202/1-e0129202/16CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)Capturing human movement patterns across political borders is difficult and this difficulty highlights the need to investigate alternative data streams. With the advent of smart phones and the ability to attach accurate coordinates to Twitter messages, users leave a geog. digital footprint of their movement when posting tweets. In this study we analyzed 10 mo of geo-located tweets for Kenya and were able to capture movement of people at different temporal (daily to periodic) and spatial (local, national to international) scales. We were also able to capture both long and short distances travelled, highlighting regional connections and cross-border movement between Kenya and the surrounding countries. The findings from this study has broad implications for studying movement patterns and mapping inter/intra-region movement dynamics.
- 41González, M. C.; Hidalgo, C. A.; Barabasi, A.-L. Understanding individual human mobility patterns. Nature 2008, 453 (7196), 779– 782, DOI: 10.1038/nature0695841https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmvVGmsLg%253D&md5=18df62c848b76a959faaf93b343c2b65Understanding individual human mobility patternsGonzalez, Marta C.; Hidalgo, Cesar A.; Barabasi, Albert-LaszloNature (London, United Kingdom) (2008), 453 (7196), 779-782CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Despite their importance for urban planning, traffic forecasting and the spread of biol. and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modeling.
- 42Buckee, C.; Noor, A.; Sattenspiel, L. Thinking clearly about social aspects of infectious disease transmission. Nature 2021, 595 (7866), 205– 213, DOI: 10.1038/s41586-021-03694-x42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXhsVGnu7%252FN&md5=ffd6cc9f7d26943116c97c050582eb5fThinking clearly about social aspects of infectious disease transmissionBuckee, Caroline; Noor, Abdisalan; Sattenspiel, LisaNature (London, United Kingdom) (2021), 595 (7866), 205-213CODEN: NATUAS; ISSN:0028-0836. (Nature Portfolio)Abstr.: Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn det. epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behavior, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiol. are hard to quantify and have limited the generalizability of modeling frameworks in a policy context, new sources of data on relevant aspects of human behavior are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioral drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behavior in relation to infectious diseases.
- 43Mu, X.; Huang, A.; Wu, Y.; Xu, Q.; Zheng, Y.; Lin, H.; Fang, D.; Zhang, X.; Tang, Y.; Cai, S. Characteristics of the precipitation diurnal variation and underlying mechanisms over jiangsu, eastern China, during warm season. Front. Earth Sci. 2021, 9, 703071 DOI: 10.3389/feart.2021.703071There is no corresponding record for this reference.
- 44Zhou, Q.; Qu, S.; Ding, J.; Liu, M.; Huang, X.; Bi, J.; Ji, J. S.; Kinney, P. L. Association between PM2. 5 and daily pharmacy visit tendency in China: A time series analysis using mobile phone cellular signaling data. J. Cleaner Prod. 2022, 340, 130688 DOI: 10.1016/j.jclepro.2022.13068844https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XmsFWmsr4%253D&md5=7002e569433cc33596e1a220f7a3e00dAssociation between PM2.5 and daily pharmacy visit tendency in China: A time series analysis using mobile phone cellular signaling dataZhou, Qi; Qu, Shen; Ding, Jiongchao; Liu, Miaomiao; Huang, Xianjin; Bi, Jun; Ji, John S.; Kinney, Patrick L.Journal of Cleaner Production (2022), 340 (), 130688CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)While much is known about the effects of PM2.5 pollution on severe health outcomes, less is known about the heterogeneous impacts of air pollution on less severe health effects experienced by large nos. of people, such as those resulting in pharmacy visits. Based on anonymized daily pharmacy visits extd. from about 136,000 mobile phone station macrocells and 28,170 pharmacy locations, we used the generalized additive model and meta-anal. to investigate the effects of PM2.5 exposure on pharmacy visit tendencies (PVTs), as a proxy variable of health facility utilization, across regions, groups and social contexts. Overall, PVTs increased by 0.338% (95% CI: 0.335%-0.342%) for per 10μg/m3 increases in PM2.5. The exposure-response curve was nonlinear, having a sharp slope below 60μg/m3 and then flattening. Sensitivity analyses showed that results were robust to modeling choices and existence of noise in captured PVT data. At pharmacy level, higher percentage increases in PVTs were obsd. in near-residency pharmacies (0.474%), designated medical insurance pharmacies (0.360%), and pharmacies in urban areas (0.457%) than their counterparts (0.156%, 0.321%, 0.180%). This indicates that social contexts, including inconvenient access to the pharmacy and limited social medical insurance coverage, inhibited residents from utilizing health facilities for the health care needed. Interactions of social contexts led to strong heterogeneity among cities, between rural and urban regions as well as across communities, which call for differentiated health resources allocations to ensure health equity.
- 46Vallis, O.; Hochenbaum, J.; Kejariwal, A. A Novel Technique for Long-Term Anomaly Detection in the Cloud. In 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 14) 2014.There is no corresponding record for this reference.
- 47Hersbach, H.; Bell, B.; Berrisford, P.; Biavati, G.; Horányi, A.; Muñoz Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Rozum, I. ERA5 hly Data on Single Levels from 1979 to Present Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 2018; Vol. 10.There is no corresponding record for this reference.
- 48Ye, H.; China Meteorological News Agency. Rainfall Rating 2018 https://www.cma.gov.cn/2011xzt/2018zt/20100728/2010072804/201807/t20180706_472586.html. accessed Oct 23, 2022.There is no corresponding record for this reference.
- 50Ma, S.; Long, Y. Identifying spatial cities in China at the community scale. J. Urban Reg. Plan 2019, 11, 37– 50There is no corresponding record for this reference.
- 51Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 2009, 90 (2), 363– 368, DOI: 10.1890/08-1034.151https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1M3jsF2msA%253D%253D&md5=2a3c123232b4ff6e3af8c8a38f2ca648Confirmatory path analysis in a generalized multilevel contextShipley BillEcology (2009), 90 (2), 363-8 ISSN:0012-9658.This paper describes how to test, and potentially falsify, a multivariate causal hypothesis involving only observed variables (i.e., a path analysis) when the data have a hierarchical or multilevel structure, when different variables are potentially defined at different levels of such a hierarchy, and when different variables have different sampling distributions. The test is a generalization of Shipley's d-sep test and can be conducted using standard statistical programs capable of fitting generalized mixed models.
Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c03145.
Introduction to S–H-ESD method; general and geographic information about 53 cities and mobile phone towers in Jiangsu, China; examples of mobile phone signaling data; data validation including rainfall data and mobile phone signaling data; comparison and validation of baseline calculation method; analyses results related to piecewise SEM; explanatory variables data source; Anselin Local Moran’s I analysis of mobility changes; and differences in mobility changes between rural and urban areas (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
