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.     

An approach for understanding human activity patterns with the motivations behind

A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations behind behaviors. The aim of this study is to model and understand human activity patterns within urban areas using both spatiotemporal and cognitive psychology methods to measure both human behavior patterns and the underlying motivations . We first propose a framework that enables us to analyze the spatiotemporal patterns of urban human activities, infer the associated semantic patterns that represent the motivations driving human mobility choices and behaviors, and measure the similarity between human activities. We then construct a human activity network based on the similarity to depict human activity patterns. The framework is applied to a case study of Toronto, Canada, where geotagged tweets are used as a proxy for human activities to explore activity patterns. The analysis of the human activity network shows that 61% of tweeter users follow similar activity patterns. Our work provides a new tool for better understanding the way individuals interact with urban environments that could be applied\ to a variety of urban applications.     

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.     

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。     

北京居民活动与出行行为时空数据采集与管理

本研究对传统活动日志调查与基于GPS、LBS的移动数据采集在居民活动—移动数据的获取和应用方面进行对比,并以2010年7月在北京进行的居民活动与移动调查为例,探讨了个体行为时空数据采集的方法、存在问题和处理方式。北京市的实验调查采取活动日志调查与基于GPS、GSM两种不同定位方式的移动数据采集相结合的方法,以定位设备为基础、以互动式调查网站为平台、以面对面和电话访谈为补充,对天通苑和亦庄两个郊区居住区的样本居民进行了为期一周的行为时空数据采集。针对时空轨迹和活动日志存在的问题分别进行处理,并对数据质量进行管理,旨在为城市活动—移动系统研究中的精细化的数据采集与管理提供理论、方法和实践经验。     

基于位置感知设备的人类移动研究综述

每个人在地理空间内的移动看似随机而没有规律,然而一个较大规模人群的移动却隐藏着特定的模式。为了研究某些地理问题,如交通、疾病传播等,可以从个体行为出发,在地理信息系统的支持下,发现人类移动模式,并构筑基于个体的模拟模型,从而建立微观和宏观之间的桥梁,并支持相应的决策过程。信息通讯技术的发展,一方面改变了人们的空间行为模式,另一方面使得基于位置感知设备获取海量人类移动数据成为可能。近年来,上述研究一直是地理信息科学及相关领域的热点,该文对此进行了总结和评述。     

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.     

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。     

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。     

Global multi-layer network of human mobility

Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility – while the first two highlight short-term visits of people from one country to another, the last one – migration – shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.     

STLP-GSM: a method to predict future locations of individuals based on geotagged social media data

ABSTRACT

An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users’ spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users’ mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations.     

珠三角快速城镇化地区国土调查典型试点探索与实证——面向第三次全国国土调查

选择东莞市4个镇（街）作为珠三角典型高度城镇化地区和第三次全国国土调查试点,针对试点内容设置、试点典型性选择、试点方法、成果和质量控制等全过程进行了总体策划和设计。从调查底图层面提出了数据源精选类别,形成了3套数据组合配置思路。从调查技术层面阐述了内业分析判读、外业调查核实和内业录入整理的3个关键方法实施路径。通过试点：形成了丰富的专题报告、专题报表、专题图和专题库等实验成果,以土地利用现状调查为例分析了各试点区历经快速城镇化后的土地利用差异性和空间格局特征,提出了在涉密基础数据使用、不一致图斑举证、细碎图斑处理、地类认定标准、多源数据空间不套合和标识码核心关联等7个问题环节的具体先验经验和未来需要深入探索提升的方向。试点成果可作为试点区土地精细化管理之用,试点经验可供快速城镇化地区参考之用。     

“Is there any time left for walking?” Physical activity implications of suburban commuting in the Barcelona metropolitan region

ABSTRACT

The present study delves into the explanatory factors of the walking patterns of residents in metropolitan regions, who tend to be pressed for time when travelling to their daily destinations or activities. We particularly focus on the effects of the commuting distance on the amount of walking that can be achieved, which has health, socioeconomic and environmental implications. This study confirms the potential benefits of using smartphone tracking data to examine walking patterns. To enable this, a smartphone tracking application was developed to obtain accurate mobility data from a group of adults (n = 93) residing in the Barcelona Metropolitan Region (Spain) and have to commute to a suburban university campus that can only be reached by using motorized transport modes. The results highlight the commuting distance and employment status as strong determinants of the amount of walking time achieved by this study group. Moreover, it was determined that among transit users, the commuting distance of male commuters was negatively associated with walking when compared with female transit users, whereas explanatory factors for private transport users bore insignificant results. Smartphone devices proved their potential as an effective and useful source of data in transportation and health research.     

The geography of crime fear: A pilot study exploring event-based perceptions of risk using mobile technology

The current pilot study explores whether mobile technology can be leveraged in survey research to gather meaningful context-dependent data on fear of crime and risk perception formation. A series of Ecological Momentary Assessments (EMAs) were administered to students enrolled at an Australian University (N = 20), using a smartphone application. Analysis of data collected from participants in their everyday activity spaces a) show strong internal consistency among multiple measures of crime fear; b) indicate that perceptual measures of social cohesion are significant predictors of victimisation worry; and c) support most hypothesised associations between concepts contained in contemporary models of crime fear. Unfortunately, some aspects of the pilot study design could not be implemented as planned, which have implications for future research. Specifically, we found that triggering participant's surveys based on their location (rather than time), produced data that was not conducive to robust place-based analysis. In spite of this limitation, we offer alternative means of measuring the effects of place on fear of crime using mobile devices.     

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.     

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.     

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。     

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.     

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.     

Overview and progress of Chinese geographical human settlement research

Increasing Chinese urbanization and industrialization has prompted greater attention to the study of human settlement and the human-land relationship in the fields of geography, architecture, and urban planning. We used bibliometric methods and statistical software to review 180 articles on human settlement in 16 Chinese geographical journals. We found that Chinese geographical human settlement research is characterized by the following: (1) Most research focuses on human settlement extension, valuation indicators, models for urban and rural settlements, theoretical exploration and the planning practices of single-factor, human settlement and complex, geographical livability in macro-scale, urban settlement differentiation and ideal patterns in medium scale, the comprehensive evaluation of settlement environment, and the planning of community units in micro-scale, community settlements; socio-cultural investigation and warnings about advancing human settlement. (2) No progress has been made in synthesizing and integrating method systems. PSR models and DPSIR models are used for targeting mechanisms, while the standard settlement evaluation system was composed of physical & economic indicators by questionnaire surveys. On the other hand, spatial clustering based on GIS has been a frequent focus in recent years. Pioneering research on human settlement and theoretical systems within the context of China’s urbanization and industrialization will provide guidance on the sustainability of Chinese cities and regions. The following five aspects require greater attention: (1) Natural suitability research on human settlement, and a survey of human settlement demands to reflect the range of different demands concerning ecologically suitable settlements in urban environments, the corresponding valuation indicators, systems, and evolution, and the impact of the residents’ socio-economic attributes. (2) Spatial-temporal evaluation and sustainability research on urban and rural human settlement at various scales, focusing on evolution and spatial differentiation at various scales such as city clusters and comparisons between cities, within the cities and communities. (3) Development of theory and technology for human settlement evolution research, including detection technology and methods, data mining measures, and forecasting and emulation of regional and urban human settlement evolution processes, mechanisms and patterns. (4) Research on the control of human settlement that focuses on optimization, patterns, and policies for effective management and development. (5) Estimating the human settlement system service value and establishing suitable human settlement systems, including social, economic, cultural and ecological service values.