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.     

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.     

Not all those who Wander are lost: exploring human mobility using a smartphone application

Since the 1970s, activity diaries have been the principal source of data used by geographers and urban planners to capture human mobility patterns; however, memory recall requirements and participant burden are limiting features of this approach. While handheld GPS devices were initially used to simply validate activity diaries, the ubiquity of smartphones makes GPS-based technology a viable alternative for collecting human mobility data. This research visualises human mobility patterns using spatiotemporally GPS-rich traces collected from 24 participants during a pilot study of a new smartphone application—Wander. The results illustrate the utility of such data for capturing individuals’ multi-nodal activity spaces to better understand people–place interactions in the context of increasingly fluid and mobile lifestyles. Finally, we draw on our experiences of implementing this pilot study to discuss the benefits and limitations of this technology for its broader application in domains such as transportation planning, tourism management, and in the management of crime and disorder.     

人类移动性与健康研究中的时间问题

随着地理信息科学的发展,地理学围绕着社会热点问题不断向其他学科交叉渗透,同时学科的交叉也为地理学研究本身注入了热量。空间一直是地理学研究中的基本问题,而时间与空间密不可分,从时间维度研究人类移动性及其丰富内涵具有重要意义。本文在总结人类移动性热点问题的基础上,重点从时间维度探讨了可达性、出行体验与幸福感、人类健康与疾病等问题的研究内容和方法,并从低收入者社区医疗保健可达性研究和阿帕拉契亚乡村地区吸烟者影响因素研究等案例进行详细阐释,最后对人类移动性与健康研究的发展方向进行了展望。     

流动量与关注度视角下的城市网络结构——以广州、深圳为例

在城市与区域转型发展的背景下,城市网络经历着剧烈的重构,广州和深圳是推动粤港澳大湾区一体化的主导力量,明晰其在网络中的发展定位与动态联系,对于引领区域协调发展具有重要的战略意义。然而,现有城市网络研究较缺乏对多尺度差异与实虚映射关系的关注,因此论文基于微博数据,从流动量、关注度出发,运用社会网络分析,探究了广深在粤港澳大湾区、全国、全球网络中的节点地位与联系特征。结果表明：① 多尺度网络下,广深不同的联系导向塑造了差异化的要素组织能力,广州辐射范围较广、联系相对均衡,深圳联系相对集中、与香港联系尤为紧密;② 实虚网络之间,广深的对外联系存在协同补充、路径依赖效应,且在各尺度下具有不同程度的体现,多重效应的叠加交融推动着区域联系趋向柔性化;③ 基于上述网络格局,广深应立足于不同的联系模式与发展实际,分别发挥区域交通枢纽及创新制度高地的优势,引领打造有序高效的区域网络系统。     

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.     

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R²) derived from the data with and without implementing the activity change detection. The results show that the R² is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.     

中国崛起与世界贸易网络演化：1980—2018年

随着网络分析方法的发展,利用社团检测和社交网络分析研究世界贸易网络成为当前重要的前沿问题。但目前国内外的研究多为短期尺度,中长期尺度研究亟需更新。当前世界经济发展不确定性要素增多,“一带一路”建设正进入深耕细作的阶段,分析20世纪80年代以来国际政治经济局势的转变以及发达国家与发展中国家的地位更替的长尺度变化,有利于揭示世界贸易网络的中长期演变趋势,并为中国“一带一路”建设提供历史启示。论文主要基于国际货币基金组织(IMF)的贸易方向统计数据库(DOTS),首先总结了1980—2018年世界贸易发展3大历史阶段的基本特征,然后研究了20世纪80年代以来的世界贸易网络中组团及国家地位的演变。研究发现：① 世界政治经济格局变化和技术进步对世界贸易网络影响深远。20世纪80年代以来资本主义的生产方式由福特制向后福特制的转变、新自由主义的推广、数字技术的大规模发展以及发展中国家的崛起对全球贸易空间格局产生了深远的影响,中国的崛起是全球贸易网络变化重要的力量。② 从Top1贸易网络上看,世界贸易格局呈现出“稳中有变”的演化趋势。世界贸易网络中,欧洲部分的德国、法国和俄罗斯组团相对稳定,而英国和意大利组团地位不断下降;美国主导了亚太部分,澳大利亚组团较为稳定,日本组团地位下降。③ 印度、巴西和南非等发展中国家的崛起,导致世界经济和贸易格局的重大变化,这些国家近年来成为了新兴的区域性贸易中心。④ 随着经济高速发展,中国在世界贸易中的地位迅速上升,成为了超过美国的世界贸易组团核心。最后,简要讨论了中长期尺度贸易网络研究对“一带一路”建设的历史启示。     

The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data

Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI < 5 minutes) to systematically assess the impacts of the TSI on four typical mobility indicators that describe human mobility patterns from different aspects, which are movement entropy, radius of gyration, eccentricity, and daily travel frequency. We find that different TSIs have complex impacts on the values of different mobility indicators. Specifically, (1) coarser TSIs tend to underestimate the values of the four selected indicators with different degrees; (2) the degrees of underestimation vary significantly among users for eccentricity and daily travel frequency but exhibit high inter-user consistency for radius of gyration and movement entropy. The above findings can help better understand the variations among human mobility studies.     

Building a Global Sense of Place: The Community Networking Movement in the United States

Community networks help build place-based community by offering individual citizens and organizations access to the Internet, the ability to post information online, and opportunities to discuss community affairs. Community networks are inspired by a dynamic vision of community that incorporates a global sense of place. A global sense of place is valuable because it helps to illuminate a place's connections with the world and may therefore enable scale-jumping forms of activism. This article analyzes interviews with participants in four U.S. community networks to explore how networks combine a politics of mobility, a politics of access, and a politics of place to construct a global sense of place. The degree to which each network emphasizes mobility, access, and place varies. This variation has implications for both community activism and issues of diversity in community. Networks that fail to link mobility, access, and place may increase, rather than decrease, inequalities in access to electronic communications as well as to forms of political power.     

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.     

全球人才流动网络复杂性的时空演化——基于全球高校留学生流动数据

高质量的人才跨越地理的流动影响和驱动着全球范围内的创新活动,正在重塑世界经济格局。研究基于全球高校留学生流动数据,构建加权有向网络模型,对2001—2015年全球人才流动网络复杂性时空演化进行全面刻画。主要结论如下：① 全球人才流动网络规模迅速扩大,网络发育具有明显的小世界性,两级分化显著。② 全球人才流动网络呈典型“金字塔结构”特征,基本呈“东向西,南到北”的地理格局,新兴国家作为人才吸纳国的角色逐渐上升。③ 全球人才流动网络核心-边缘结构显著,核心国家间存在较强的关系流,边缘国家间联系较弱,其人才主要流向了核心国家和半边缘国家。④ 全球人才流动网络分化成美国社团、欧盟社团、中国社团、南美和南非社团、马来西亚社团、独联体社团等6个凝聚子群。     

城市交通热点区域的空间交互网络分析

城市热点区域是人们频繁活动的体现,利用人们的出行可构建空间交互网络。目前的相关研究主要集中于对热点提取方法及其动态变化的研究,对交通热点的交互作用及其构成的空间交互网络的研究还很少。本文以武汉市的出租车轨迹为数据源,利用基于时空数据场的聚类方法提取城市交通热点区域;基于复杂网络理论与方法,分析城市交通热点区域之间的空间交互作用。通过研究发现：①节假日,热点区域之间的往返交互较多;工作日,热点区域之间的交互较少;②节假日,影响力较大的节点为车站、机场等;工作日,影响力较大的节点是社区和工作地;③社团探测发现,工作日跨越长江的交互较多,非工作日跨越长江的交互较少。上述研究结论可为交通管理部门针对节假日和工作日分别制定不同的交通管理政策和方法提供参考。     

Understanding the bias of call detail records in human mobility research

ABSTRACT

In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.     

全球留学生留学网络时空演化及其影响因素

基于联合国教科文组织公布的全球高等学校留学生流动数据,借鉴网络科学,利用社会网络分析方法和负二项回归模型,对2001—2015年全球留学网络时空演化及其影响因素进行研究。主要结论为：① 时序演化特征上,全球留学网络关系流从4921个增加到9137个,平均加权度从8004迅速增加到20834,表明全球留学网络不仅学生数量在增加,且留学路径数量也在不断增加。② 拓扑结构上,从以美国为单一核心,演变成以美国、中国为双核心的拓扑结构,亚洲国家逐渐成为全球留学网络的主体,欧洲国家的中心性逐渐萎缩。③ 空间结构上,全球留学网络具有明显的等级层次性,美国是全球性主导型节点,德国、捷克是区域性主导型节点,次级主导型节点从25个增加到32个,全球留学网络“东向西、南到北”的地理空间格局正在发生变化,中国、澳大利亚等国成为新的区域性中心,区域化成为全球留学网络一个重要趋势。④ 回归分析发现,留学目的地国高等教育质量、高校在校生数量、人均GDP、留学目的地国与留学生来源国间的历史联系及全球化水平与留学生流量呈显著正相关关系,地理距离、语言临近性与留学生流量呈显著负相关关系,表明语言差异的阻抗作用在迅速减弱,地理距离仍然是阻碍学生流动的一个重要因素。     

行为网络视角下区域空间结构研究进展

论文系统综述了区域空间结构研究的多种视角与分析方法及其发展脉络,认为行为视角下区域空间结构的研究经历了从行为汇总到行为交互、地方属性到网络联系的转变过程。早期的空间结构研究多通过行为汇总与地方属性的分析,聚焦于区域内空间单元的属性相关性,具体包括社会区、同质区与空间邻域等视角与方法。近期研究则逐步发生了“关系转向”与“网络转向”,更多从行为交互与网络联系出发,分析区域的网络结构和空间格局,包括传统的功能区分析、社群结构分析、核心—边缘结构研究等。通过比较不同分析视角,论文最后提出未来城市与区域空间结构研究应加深对行为交互与网络联系的理解,加强对行为空间网络的中观尺度结构分析,在方法应用上需要避免“方法决定论”,并探索适用于多尺度、多要素的多层网络分析视角与方法。     

高速铁路对城市网络结构的影响研究————基于铁路客运班列分析

城市网络是近年来城市与区域研究的一个新方向.基于2003-2013年间8个横截面的中国铁路客运班列数据,采用加权度中心性和社区结构模型,探讨高速铁路建设对城市等级和集聚性空间格局及演化的影响.研究表明:① 基于铁路客运组织的城市等级结构呈现出位序--规模分布的特征和集聚性,且三大地带和各社区内城市也呈现出明显的位序--规模分布,随着高速铁路网络的扩展,加权度中心性的集聚性更明显,并逐步呈现出较为明显的层级结构和社区结构.② 城市等级结构呈现出明显的东中西地带性,廊道效应和经济,人口的耦合性,即基于铁路客运班列的城市等级结构与基于属性数据(人口/GDP)的结果表现出较大的相似性,但也存在一定的差异.城市社区结构与中国高速铁路网络,城市群的空间分布格局基本一致.随着高速铁路的建设,东中西三大地带间城市等级的差异呈扩大态势,但各社区间城市等级的差异呈缩小态势,部分社区核心城市已形成.     

Incorporation of intra-city human mobility into urban growth simulation: A case study in Beijing

Journal of Geographical Sciences - The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics. As urbanization has slowed down in most...     

A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility

Sampling fraction is crucial to sampling-related studies and applications, especially in the big data era when most data are neither originally designed nor controllable in the data collection process. A common concern among researchers is ‘what’s the modelling accuracy when using a sample?’. Taking intra-city human mobility as the study objective, this study utilizes a simple and direct method to analyse the influences of various sampling fractions on modelling accuracy. Five common intra-city human mobility indicators (travel distance, travel time, travel frequency, radius of gyration and movement entropy) are evaluated considering mean value, median and probability distribution. Experimental results demonstrate that the representativeness of each considered indicator converges to 1 in its own unique rate and variances. The minimum required sampling fractions to satisfy specific accuracies differ for various indicators and evaluation measures. To further investigate how related factors influence the modelling accuracy of sampling fractions, additional experiments are conducted considering multiple sampling methods, study scopes, and data sources. Several interesting general findings are observed. This study provides a reference for other sampling-based applications.     

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。