移动定位大数据视角下的人群移动模式及城市空间结构研究进展

了解城市人群移动行为和空间结构对城市规划、交通管理、应急响应等具有重要的意义。近年来,随着信息技术(ICT)的快速发展,采集大规模、长时间序列的人群移动定位大数据变得容易,为人群移动行为研究带来了新的机遇和挑战。本文首先介绍了目前用于城市人群移动行为和空间结构研究的主要数据源及其特征,并分别从人群移动行为、城市空间结构2个方面对近3年国内外相关研究进行归纳总结。目前的研究主要从移动定位大数据中挖掘人群移动模式,理解人群移动时空规律,进一步透视城市的空间结构特征;而对城市空间结构与人群移动行为影响的研究较少。未来可通过融合多源时空数据,综合研究人群移动行为与城市空间结构之间的相互作用,发展大规模群体移动行为时空分析理论和模型,进一步深入理解人群移动行为与城市空间结构的耦合关系。     

A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data

ABSTRACT

International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturally endemic areas. Existing studies mostly focus on describing the quantitative relationship between imported cases and local transmission but ignore the space-time diffusion mode of imported cases under the influence of individual mobility. In this paper, we propose a comprehensive framework at a fine scale to establish the disease transmission network and a mathematical model, which constructs ‘source-sink’ links between the imported and indigenous cases on a regular grid with a spatial resolution of 1 km to explore the diffusion pattern and spatiotemporal heterogeneity of imported cases. An application to Guangzhou, China, reveals the main flow and transmission path of imported cases under the influence of human movement and identifies the spatiotemporal distribution of transmission speed according to the time lag of each source-sink link. In addition, we demonstrate that using individual-based movement data and socio-economic factors to study human mobility and imported cases can help to understand the driving forces of dengue spread. Our research provides a comprehensive framework for the analysis of early dengue transmission patterns with benefits to similar urban applications.     

社区生活圈的新时间地理学研究框架

社区生活圈从居民日常活动及行为视角考察城市社区,是城市地理学和城市相关学科的研究前沿,也是中国国土空间规划体系创新的重要组成部分,以及中国城市社会可持续发展的重要抓手。伴随着流动性和信息化的不断深入,社区生活圈的主体日益多元化、社区活动和居民时空行为日益多样化、社区空间的功能与意义日益丰富化,亟需城市地理学的研究创新与实践引导。时间地理学是理解人与环境关系的社会—技术—生态综合方法,为早期基于时空行为与生活空间的社区生活圈研究提供了重要基础。新时间地理学重视家庭及其他组织企划的交互与时空组合,可为社区生活圈内个体—家庭—社区之间的复杂互动关系研究、时空行为的社会文化制约与多情境分析及模拟提供重要支撑。论文基于新时间地理学方法,从理论、方法和实证3个维度提出社区生活圈的新时间地理学研究框架,具体包括构建社区生活圈的时空行为理论,揭示社区生活圈的时空间结构;创新社区生活圈的时空行为分析和模拟方法;从社区生活圈时空行为优化、社区交往生活圈、社区安全生活圈等方面创新中国城市规划与管理等研究内容。     

Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study

Travel activities are embodied as people’s needs to be physically present at certain locations. The development of Information and Communication Technologies (ICTs, such as mobile phones) has introduced new data sources for modeling human activities. Based on the scattered spatiotemporal points provided in mobile phone datasets, it is feasible to study the patterns (e.g., the scale, shape, and regularity) of human activities. In this paper, we propose methods for analyzing the distribution of human activity space from both individual and urban perspectives based on mobile phone data. The Weibull distribution is utilized to model three predefined measurements of activity space (radius, shape index, and entropy). The correlation between demographic factors (age and gender) and the usage of urban space is also tested to reveal underlying patterns. The results of this research will enhance the understanding of human activities in different urban systems and demographic groups, as well as providing novel methods to expand the important and widely applicable area of geographic knowledge discovery in the age of instant access.     

A proportional odds model of human mobility and migration patterns

The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.     

A framework for identifying activity groups from individual space-time profiles

ABSTRACT

Datasets collecting the ever-changing position of moving individuals are usually big and possess high spatial and temporal resolution to reveal activity patterns of individuals in greater detail. Information about human mobility, such as ‘when, where and why people travel’, is contained in these datasets and is necessary for urban planning and public policy making. Nevertheless, how to segregate the users into groups with different movement and behaviours and generalise the patterns of groups are still challenging. To address this, this article develops a theoretical framework for uncovering space-time activity patterns from individual’s movement trajectory data and segregating users into subgroups according to these patterns. In this framework, individuals’ activities are modelled as their visits to spatio-temporal region of interests (ST-ROIs) by incorporating both the time and places the activities take place. An individual’s behaviour is defined as his/her profile of time allocation on the ST-ROIs she/he visited. A hierarchical approach is adopted to segregate individuals into subgroups based upon the similarity of these individuals’ profiles. The proposed framework is tested in the analysis of the behaviours of London foot patrol police officers based on their GPS trajectories provided by the Metropolitan Police.     

Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.     

基于个体时空邻近指数的户籍维度社会空间分异研究——以上海市郊区10个典型镇为例

社会空间分异是城市研究的经典议题。在人类移动性不断增强的背景下,传统的基于居住空间汇总的社会空间分异测度方法表现出一定的局限性,对于居住空间外的日常活动空间隔离的探讨相对缺乏,对于不同活动和不同时段间分异格局差异的考虑有所不足。因此有必要从“基于人”的视角出发,探索社会空间分异测度的新方法,探讨不同时空间维度的社会分异格局。本研究基于上海市郊区10个典型镇的活动日志调查数据,构建“个体时空邻近指数”,聚焦户籍这一反映中国城市特征的重要维度,以不同户籍类型人群之间的分异程度为研究对象,分析其时空间特征,并对结果进行可视化。研究表明：上海市不同户籍人群在活动、时间和空间维度上存在明显的社会空间分异。本研究创新了基于活动空间的社会空间分异测度方法,从活动与时空间结合的视角探讨了户籍维度的社会空间分异,为更好的理解在中国大城市日益凸显的社会空间分异问题提供了新的视角。     

人类活动轨迹的分类、模式和应用研究综述

各种传感器的应用与发展,如车载GPS、手机、公交卡、银行卡等,记录了人类的活动轨迹。这些海量的人类活动轨迹数据中蕴含着人类行为的时空分布模式。通过对这些轨迹的研究可以挖掘个体轨迹模式,理解人类动力学特征,进而为对轨迹预测、城市规划、交通监测等提供支持。因此,研究各类传感器记录的人类活动轨迹数据成为当前的研究热点。本文对人类活动轨迹的获取与表达方式进行剖析,并将人类的活动轨迹按照采样方式和驱动因素的不同分为基于时间间隔采样、基于位置采样和基于事件触发采样等3类轨迹数据。由于各类轨迹数据均由起始点、锚点和一般节点等构成,因而将轨迹模式挖掘的研究按照锚点、出行范围、形状模式、OD流模式、时间模式等进行组织,研究成果揭示人类活动轨迹在时间、空间的从聚模式、周期性等特点。在此基础上,将人类活动轨迹在城市研究中的应用,按照用户轨迹预测、城市动态景观、城市交通模拟与监控、城市功能单元识别以及城市中其他方面的研究应用进行系统综述,认为人类活动模式挖掘是城市规划、城市交通、公共安全等方面应用的基础。     

人类活动研究的新支点：Wi-Fi数据的特点、研究现状与应用前景

把握人类活动的时空特征是地理学研究中探究人地关系、提升人类福祉的重要基础和核心内容,日益普及的Wi-Fi网络能够为此提供可靠的数据支持。为明确Wi-Fi数据融入地理学研究的切入点和发展方向,论文通过与GPS、手机信令、蓝牙等位置感应数据的比较,认为Wi-Fi数据具有更高的采样精度和更强的采样代表性,能够获取个体在室内外各类城市空间的连续活动轨迹,支撑精细尺度下的人类活动研究。通过系统梳理人群活动状态监测、个体间的社会关系识别、建筑物的功能识别和降低隐私泄露风险等方面的研究进展,认为Wi-Fi数据将会在基于实时动态人口数据的城市功能设施规划、融合多源数据的人地关系探究、以居民福祉为导向的宜居城市建设等方面具有应用前景,有望成为地理学研究人类活动的新支点。     

Spatial mining of migration patterns from web demographics

Volunteered Geographic Information, social media, and data from Information and Communication Technology are emerging sources of big data that contribute to the development and understanding of the spatiotemporal distribution of human population. However, the inherent anonymity of these crowd-sourced or crowd-harvested data sources lack the socioeconomic and demographic attributes to examine and explain human mobility and spatiotemporal patterns. In this paper, we investigate an Internet-based demographic data source, personal microdata databases publicly accessible on the World Wide Web (hereafter web demographics), as potential sources of aspatial and spatiotemporal information regarding the landscape of human dynamics. The objectives of this paper are twofold: (1) to develop an analytical framework to identify mobile population from web demographics as an individual-level residential history data, and (2) to explore their geographic and demographic patterns of migration. Using web demographics of Vietnamese–Americans in Texas collected in 2010 as a case study, this paper (1) addresses entity resolution and identifies mobile population through the application of a Cost-Sensitive Alternative Decision Tree (CS-ADT) algorithm, (2) investigates migration pathways and clusters to include both short- and long-distance patterns, and (3) analyze the demographic characteristics of mobile population and the functional relationship with travel distance. By linking the physical space at the individual level, this unique methodology attempts to enhance the understanding of human movement at multiple spatial scales.     

An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACT

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.     

多源地理大数据支撑下的地理事件建模与分析

地理事件作为描述地理过程的基本单元,逐渐成为地理信息科学（GIS）核心研究内容。由于受人类活动数据获取限制,GIS对地理事件的建模和分析主要关注事件所引起的地理空间要素变化及要素之间的相互影响与作用机制。然而,近年来随着基于位置服务数据（LBS）爆炸式的增长和人类活动大数据定量刻画手段的快速发展,地理事件对人类活动的影响以及公众对地理事件的网络参与度都引起了多个领域的广泛关注,对地理事件的时空认知、建模方法和分析框架提出了巨大的挑战。对此,本文首先深入分析了大数据时代地理事件的概念与分类体系;其次,基于地理事件的时空语义给出了基于图模型的事件数据建模,建立了事件本体及其次生或级联事件的“节点—边”表达结构,开展了事件自身时空演化及其前“因”后“果”的形式化描述;第三,从时空数据分析与挖掘的角度,给出了大数据时代地理事件建模与分析的整体框架,拟突破传统“地理实体空间”事件探测与分析方法的局限性,融合“虚拟空间”事件发现与传播模拟思路,实现多源地理大数据支撑下的面向地理事件的人类活动多尺度时空响应与区域差异分析;最后,本文以城市暴雨事件为例诠释了本文所提出的地理事件建模与分析方法,从城市和城市内部两个尺度进行了暴雨事件与人类活动的一致性响应及区域差异分析,得到了明确的结论,验证了前文分析框架的可行性与实用性。     

基于居民行为周期特征的城市空间研究

伴随着中国经济社会进入“新常态”的发展阶段,对城市存量空间的研究提出了更加精细化的要求,基于居民行为活动的周期规律对城市空间进行研究,进而提升城市空间的品质日益重要。随着信息通信技术的快速发展,使许多大数据的获取成为可能,并由于其低成本、即时、大样本等优势,在城市空间研究方面具有巨大的价值。以上海市中心城区为例,利用手机信令数据,探究居民活动的空间周期变化特征,并基于空间的周期特征曲线,采用相似性传播聚类算法进行空间分类。研究表明,居民活动有平日一日周期和平日加周末二日周期,与人的作息规律相符合。市核心区、城市副中心及主要就业中心,昼夜波动和平日周末活动强度的差异都较为明显。空间分类结果显示,城市活动空间的组织既体现出个体充分的空间能动性,也反映出对土地使用类型以及设施建设、投入程度的耦合性。上海市内环内核心区混合多样的用地模式使得活动区内居民活动内容丰富,周期特征功能区边界模糊。研究成果可为未来的城市空间规划提供指导,为城市空间结构、功能布置、设施布局等优化提供决策支撑和科学依据。     

Land fragmentation due to rapid urbanization in the Phoenix Metropolitan Area: Analyzing the spatiotemporal patterns and drivers

Rapid urbanization of the Phoenix Metropolitan Area exemplifies the dominant US Southwest urban growth pattern of the past six decades. Using a combination of multitemporal land cover data, gradient analysis, and landscape metrics, we quantify and characterize spatiotemporal patterns of land fragmentation observed in Phoenix. We analyze historical, qualitative data to identify five major socio-ecological drivers critical to understanding the urbanization processes and fragmentation patterns: population dynamics, water provisioning, technology and transportation, institutional factors, and topography. A second objective is to assess the applicability and accuracy of National Land cover Database (NLCD)-—a widely used land cover dataset—-to detect and measure urban growth and land fragmentation patterns in the relatively treeless desert biome of the US Southwest. In contrast to studies in the temperate eastern USA where NLCD has proved inaccurate for detection of exurban development, our study demonstrates that NLCD is a reliable data source for measuring land use in the southwest, even in low-density environments. By combining qualitative analyses of social-ecological drivers with fragmentation analyses, we move toward an improved understanding of urbanization and insights on the human modification framework used widely in land change science.     

面向城市社区规划的参与式感知与计算——概念模型与技术框架

科学准确地感知社区居民参与现状、诊断存在问题,及时广泛地了解社区居民需求与诉求,对于提升新形势下社区居民参与城市社区规划的能力与水平意义重大。借助网络和移动设备等技术手段,采取以人为主体的参与式感知方式获取数据,可实时感知和分析居民的情感、行为和所处的环境,进而提高社区居民参与的广泛性和时效性。国内外该领域的研究刚刚起步,对面向城市社区规划的参与式感知与计算尚缺乏系统深入的机理探索和方法研究。本文针对中国城市社区规划的实际需求,构建了面向城市社区规划的参与式感知与计算概念模型,提出实现参与式感知与计算的技术框架,并探讨其中涉及的具体技术研究内容。本研究将深化面向城市社区的参与式感知与计算的相关理论与方法研究,为城市社区规划的公众参与和科学评估提供新思路、新方法。     

A collective human mobility analysis method based on data usage detail records

Human mobility patterns have been widely investigated due to their application in a wide variety of fields, for example urban planning and epidemiology. Many studies have introduced spatial networks into human mobility analyses at the collective level. However, these studies merely analyzed spatial network structure, and the underlying collective mobility patterns were not further discussed. In this paper, we propose a collective mobility discovery method based on community differences (CMDCD). We constructed spatial networks where nodes represent geographical entities and edge weights denote collective mobility intensity between geographical entities. The differences between communities detected from the networks constructed in different periods were then identified. Since collective spatial movement has a large influence on network structure, we can discover groups with different mobility patterns based on community differences. By applying the method to data usage detail records collected from the cellular networks in a city of China, we analyzed different collective mobility patterns between the Spring Festival vacation and workdays. The experimental results show that our method can solve these two problems of identifying community differences and discovering users with different mobility patterns simultaneously. Moreover, the CMDCD method is an integrated approach to discover groups whose mobility patterns have changed in different periods at the large spatial scale and the small spatial scale. The discovered collective mobility patterns can be used to guide urban planning, traffic forecasting, urban resource allocation, providing new insights into human mobility patterns and spatial interaction analyses.     

新冠肺炎疫情精准防控的时空间行为地理学研究框架

面向新冠肺炎疫情精准防控的重大需求,时空间行为地理学可以发挥其独特的学科价值。以时间地理学、行为主义地理学、移动性地理学等时空间行为的地理学经典理论为基础,以时空路径表达、活动的复杂情境分析、风险感知地图分析为核心方法,以时空间行为风险评估、居民时空间行为规划与引导、心理情绪引导与智慧社区治理为重点应用方向,尝试搭建疫情精准防控的时空间行为地理学研究框架。未来时空间行为研究应该立足人本导向、流动性导向和应用导向,突出时空间行为地理学在城市规划、城市管理、居民服务等方面的应用价值。     

Data-driven approach to characterize urban vitality: how spatiotemporal context dynamically defines Seoul’s nighttime

ABSTRACT

This study takes a data-driven approach to define urban nighttime by examining the spatiotemporal dynamics of urban vitality. Using micro-scale spatiotemporal analysis, this paper empirically provides a comprehensive, yet granular, picture of collective human behaviors in cities. Using Seoul, South Korea as a case study site, it prioritizes the spatiotemporal context in order to mitigate uncertain contextual effects inherent in such forms of data-driven analysis. Instead of leaving the data re-grouping up to researcher’s arbitrary decision, this paper employs a functional principal component analysis (FPCA) to systematically transform a set of discrete data to a continuous functional form. This paper applies FPCA on 24-hour-based dataset of pedestrian traffic in Seoul in order to make a data-driven extraction of principal components that characterize the city’s unique patterns of urban vitality. Extracting principal components allows for less statistically obvious phenomena to be measured that would have otherwise been hidden within the data. This approach proved successful in capturing nighttime vitality patterns that are eclipsed by the overwhelming trend of daytime patterns. Additionally, this paper compares differences between regions and seasons to examine what the differences can tell about the definition of nighttime.     

居民出行活动特征与收入水平的关系——以上海市为例

居民出行活动与居民的收入水平关系是公共交通、城市地理研究的重要问题。传统获取居民出行活动信息主要基于问卷调查的方式,不仅成本高、样本量有限,且研究局限于定性讨论,研究结果易因受访者的主观意识而产生偏颇。随着信息技术的革新,传感器记录的大规模人类活动信息为研究居民出行活动特征与居民收入水平关系提供了可能性。本文利用上海市居民时空轨迹数据,从居民出行活动的角度出发,首先构建居民出行活动指标,并利用主成分分析法提取居民出行活动特征的主要成分;然后对主成分进行K-Means聚类,并针对不同出行活动特征的类别,分析居民出行活动特征与居民收入水平的关系,结果表明：①居民出行地点多样性与居民出行范围大小是反映居民出行活动特征的主要成分;②移动范围越小、移动地点多样性越低的居民类别,其平均工资水平越高;③不同移动性特征的类别平均收入水平差异与各类别居民工作地的产业发展有关。研究结论可为城市规划及相关经济政策制定提供参考。