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。     

复杂网络视角下时空行为轨迹模式挖掘研究

针对时空行为轨迹大数据的序列性、时空交互性、多维度性等复杂特性,构建结合时间地理学与复杂网络的分析框架,建立时空行为路径与时空行为网络之间的转换关系,利用复杂网络社群发现算法对时空行为轨迹进行社群聚类、模式挖掘与可视化。基于北京郊区居民一周内活动出行GPS轨迹数据的案例分析发现：① 复杂网络分析方法可以有效挖掘具有相似行为的群体特征和识别出典型的行为模式。② 可以灵活处理多元异构与多维度的行为轨迹大数据以及满足不同叙事、不同空间相互作用、不同时序的应用需求。③ 北京郊区被调查居民的行为模式存在日间差异与空间分异。     

时空轨迹聚类方法研究进展

时空轨迹(Trajectory)是移动对象的位置和时间的记录序列.作为一种重要的时空对象数据类型和信息源,时空轨迹的应用范围涵盖了人类行为、交通物流、应急疏散管理、动物习性和市场营销等诸多方面.通过对各种时空轨迹数据进行聚类分析,可以提取时空轨迹数据中的相似性与异常特征,并有助于发现其中有意义的模式.本文根据时空轨迹数...     

基于数据场的出租车轨迹热点区域探测方法

利用空间聚集模式探测方法可以从出租车轨迹中挖掘城市热点区域,从而为城市规划和交通管理提供支持。数据场借鉴物理学中场的理论,通过定量化计算数据对象间的相互作用,可分析空间数据的聚集模式。针对轨迹数据的特点,该文提出了一种利用数据场势值阈值法探测轨迹点的聚集模式,从而提取城市热点区域的方法。该方法首先在轨迹空间上划分网格,将轨迹点映射在网格中,并利用数据场势函数计算各网格单元的势值,然后利用单一阈值或多阈值分割法提取城市热点区域。以武汉市的出租车轨迹为例进行实验,利用单峰直方图阈值法进行高势值区域筛选,得到轨迹聚集区域,从而提取城市热点区域。且通过武汉市节假日与非节假日多时段的提取结果的对比分析,得到城市热点区域的时空分布模式。进一步研究将该文方法扩展到时空聚集模式探测,以多角度分析城市热点的时空动态变化。     

基于隐马尔可夫模型的时空轨迹语义匹配方法

时空轨迹数据关联的语义信息能更好地反映用户行为,对于POI密集分布的城市区域,轨迹的语义信息很难根据单一的距离或时间要素进行匹配,该文设计一种基于隐马尔可夫模型(HMM)的时空轨迹语义匹配方法。首先,利用时间阈值与距离阈值提取逗留点,并利用考虑时间的DBSCAN聚类方法对逗留点进行聚类,得到由抽象停留位置构成的轨迹;然后,结合POI数据获得停留位置的候选语义,再以停留位置序列为观测序列,以每个停留位置所关联的候选地点作为隐藏状态建立HMM,并用改进的加权距离的TF-IDF方法计算HMM的观测概率;最后,解算HMM得到最有可能的访问地点序列作为轨迹的语义匹配结果。该方法不依赖其他外部数据或训练数据,适用于POI密集分布的城市区域,基于真实时空轨迹数据集的实验结果验证了该方法的有效性。     

基于词向量数据场轨迹引力的多中心识别和空间交互分析

多中心化是分散城市人口,疏解交通拥堵,调节职住失衡,应对“大城市病”的重要手段。针对轨迹大数据,先利用词向量描述其空间特征和行为规律,再结合数据场理论表达城市区域对轨迹的吸引强度,并完成多中心识别,最后借鉴复杂网络理论对多中心空间交互规律进行探索和挖掘。结果表明：① 郑州市轨迹吸引强度呈核心强、外围弱、沿线蔓延的圈层空间分布形态,识别出的21个多中心轨迹引力差异较大,区域吸引能力不均衡;② 外围次级中心的区域引力强度和交互频次低,交互方向主要指向一级中心,呈现出外溢型多中心结构,为了实现其应有的分散疏解作用,还需加强统筹规划,带动其科学发展。提出基于词向量数据场轨迹引力的多中心识别分析方法,对于轨迹隐含的出行规律描述更加完整,对轨迹引力的表达更准确,从流动角度呈现了多中心的演化机理,为城市规划实践提供了新思路。     

中国时空间行为研究进展

自时间地理学和活动分析法引入中国以来的近20年间,时空间行为研究已经成为中国城市地理学的重要领域。中国时空间行为研究关注城市空间重构的描述与解释,试图从行为角度解释中国城市社会转型,强调转型期中国城市空间与居民个体行为之间的互动关系,重视日常生活、生活质量、社会公正、低碳社会、智慧城市等热点问题,探索在城市交通、旅游和城市规划等领域中的实践应用。中国时空间行为研究已经形成了以解读城市转型为目标、以规划应用为导向的鲜明特点,为理解中国城市制度与空间转型背景下人类行为模式的复杂性和多样性提供了一个全新的视角。但是,中国时空间行为研究依然面临着理论发展滞后、实践应用需要突破等挑战。本文是对时空行为研究近年来发展的综述性文章,从数据采集与分析方法演进、实证研究与规划应用进展等方面回顾了近20年来中国城市时空间行为研究的最新进展,致力于推动不同学科领域之间的交流和时空间行为研究自身的发展。     

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。     

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。     

利用出租车轨迹数据挖掘城市居民出行特征

通过用户产生的历史轨迹数据对城市的热点区域以及居民出行行为的时空特性进行挖掘研究逐渐受到重视,且取得了一定的进展。受电动力学中高斯定律的启发,该文在前人关于轨迹数据处理的基础上,针对出租车轨迹数据,将轨迹的方向和数量特征考虑在内,提出了一种基于高斯定律思想的轨迹挖掘方法,通过对不同时段出租车轨迹数据的挖掘,发现城市居民出行行为的时空特征以及城市的热点区域。     

Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns

Understanding the stability of urban flows is critical for urban transportation, urban planning and public health. However, few studies have measured the stability of aggregate human convergence or divergence patterns. We propose a spatiotemporal model for assessing the stability of human convergence and divergence patterns. A mobile phone location data set obtained from Shenzhen, China, was used to assess the stability of daily human convergence and divergence patterns at three different spatial scales, i.e. points (cell phone towers), lines (bus lines) and areas (traffic analysis zones [TAZs]). Our analysis results demonstrated that the proposed model can identify points and bus lines with time-dependent variations in stability, which is useful for delineating TAZs for transportation planning, or adjusting bus timetables and routes to meet the needs of bus riders. Comparisons of the results obtained from the proposed model and the widely used entropy measure indicated that the proposed model is suitable for assessing the differences in stability for various types of spatial analysis units, e.g. cell phone towers. Therefore, the proposed model is a useful alternative approach of measuring spatiotemporal stability of aggregate human convergence and divergence patterns, which can be derived from the space–time trajectories of moving objects.     

Identifying primary public transit corridors using multi-source big transit data

ABSTRACT

Effective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management.     

Crowdsourcing urban form and function

Urban form and function have been studied extensively in urban planning and geographical information science. However, gaining a greater understanding of how they merge to define the urban morphology remains a substantial scientific challenge. Toward this goal, this paper addresses the opportunities presented by the emergence of crowdsourced data to gain novel insights into form and function in urban spaces. We are focusing in particular on information harvested from social media and other open-source and volunteered datasets (e.g. trajectory and OpenStreetMap data). These data provide a first-hand account of form and function from the people who define urban space through their activities. This novel bottom-up approach to study these concepts complements traditional urban studies to provide a new lens for studying urban activity. By synthesizing recent advancements in the analysis of open-source data, we provide a new typology for characterizing the role of crowdsourcing in the study of urban morphology. We illustrate this new perspective by showing how social media, trajectory, and traffic data can be analyzed to capture the evolving nature of a city’s form and function. While these crowd contributions may be explicit or implicit in nature, they are giving rise to an emerging research agenda for monitoring, analyzing, and modeling form and function for urban design and analysis.     

Robust inferences of travel paths from GPS trajectories

Monitoring and predicting traffic conditions are of utmost importance in reacting to emergency events in time and for computing the real-time shortest travel-time path. Mobile sensors, such as GPS devices and smartphones, are useful for monitoring urban traffic due to their large coverage area and ease of deployment. Many researchers have employed such sensed data to model and predict traffic conditions. To do so, we first have to address the problem of associating GPS trajectories with the road network in a robust manner. Existing methods rely on point-by-point matching to map individual GPS points to a road segment. However, GPS data is imprecise due to noise in GPS signals. GPS coordinates can have errors of several meters and, therefore, direct mapping of individual points is error prone. Acknowledging that every GPS point is potentially noisy, we propose a radically different approach to overcome inaccuracy in GPS data. Instead of focusing on a point-by-point approach, our proposed method considers the set of relevant GPS points in a trajectory that can be mapped together to a road segment. This clustering approach gives us a macroscopic view of the GPS trajectories even under very noisy conditions. Our method clusters points based on the direction of movement as a spatial-linear cluster, ranks the possible route segments in the graph for each group, and searches for the best combination of segments as the overall path for the given set of GPS points. Through extensive experiments on both synthetic and real datasets, we demonstrate that, even with highly noisy GPS measurements, our proposed algorithm outperforms state-of-the-art methods in terms of both accuracy and computational cost.     

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.     

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.     

基于轨迹数据场的热点区域提取及空间交互分析 ——以深圳市为例

以深圳市出租车GPS数据为基础，运用时空拓展的轨迹数据场聚类方法提取城市交通热点区域，结合城市POI（Point of Interest）数据和地理实况对热点区域加以理解和分析。基于复杂网络的视角，计算交互分析指标并可视化热点区域的空间交互网络，探究城市交通和居民出行的时空规律。结果表明：1）交通枢纽（机场、火车站和口岸）、综合性商圈、城市重要主干道周边和城市商务中心在节假日和工作日均表现为持续热点区域；2）节假日热点区域分布较“发散”，主要反映了居民个性化出行需求；3）工作日热点区域分布较“收敛”，主要表现为职住分离的通勤模式；4）不同热点区域在空间交互网络中的重要性存在明显差异，其空间交互体现了距离衰减效应和局部抱团现象，居民出行的热点区域网络本身具有小世界效应和无标度特征。     

活动空间视角下的老年群体地铁出行时空特征——以昆明市为例

运用昆明市老年公交爱心卡和常规地铁刷卡数据,考虑老龄化不同程度的影响,剖析老年群体的地铁出行特征,并从活动空间的视角重点解析和对比低龄老年群体和高龄老年群体的地铁日常出行和活动模式。结果显示：1）同非老年群体相比,老年群体移动性更低,出行时刻集中于早高峰时段,活动范围相对集中于市中心。2）随年龄增长,老年群体的移动性进一步降低,活动空间收缩,但对公共交通的依赖性增强。相较于低龄老年群体（60~69岁）,高龄老年群体（≥70岁）具有“出行频次高、活动空间范围小”的出行特征。3）低龄老年和高龄老年群体的活动空间分布存在明显分异,其中高龄老年群体的活动空间向居住密集区聚集,且出行在较大程度上受建成环境的制约。