Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

Grain‐Size Characteristics of Dune Networks in China's Tengger Desert

Grain‐size characteristics of dune networks in the Tengger Desert of northwestern China were investigated. Grain‐size parameters (mean, standard deviation, skew, and kurtosis) were determined on dune surfaces at windward toe, stoss, crest and leeward toe locations. Multiple discriminant analyses were applied to distinguish deposition environments. Results indicated that the aeolian sediment is mainly composed of very fine and fine sand in the dune networks of the Tengger Desert. Sorting improves as grain size becomes finer. However, the relationships between mean grain size, skew, and kurtosis vary in space. There is a negative relationship between skew and sorting; similarly, the relationships between sorting and skew, and skew and kurtosis change in space. The sediment deposition environment includes aeolian sediment and lacustrine sediment. The lacustrine sediment provides the source material for dune windward toe and stoss formation and development, but the aeolian sediment provides the source material for dune crest and leeward toe formation and development. According to the log‐probability of grain size distributions, aeolian sediments in dune networks are composed of two distinct saltation populations.     

人工增雨防雹火箭弹道跟踪系统的研制及初步试验

由于受到高空风场的影响,人工增雨防雹火箭真实的弹道轨迹和射程与理论弹道值具有不一致性。为增强实施增雨防雹作业的科学性、精准性及其作业效果评估准确性,研制了一套人工增雨防雹火箭播撒作业跟踪系统。该系统分为火箭定位系统和数据传输系统两部分,利用卫星定位模块加上数据传输电台模式将火箭飞行轨迹实时传输至地面。并开展了一次人工增雨防雹火箭弹道跟踪实弹发射试验,发射两发人工增雨防雹测试火箭,除初始阶段4～5 s卫星定位失锁而没有数据外,其余时间所有数据均接收完整。结合高空风场数据对人工增雨防雹测试火箭进行风偏修正后的实际弹道更接近于理论弹道,风偏修正意义明显,有利于增雨防雹火箭精准作业。     

Improvement and Application of the Similarity Saltation Model: Wind-Tunnel Experimental Investigation and Numerical Simulation of the Vertical Sand Mass Flux Distribution in the Saltation Layer

Based upon comparisons between published experimental data and simulated results on the vertical sand flux distribution in the saltation layer, Shao’s similarity saltation model has been greatly improved by correcting the average vertical particle lift-off velocity and using a more suitable universal roughness length. By the improved model, the vertical sand flux profile over the bare, dry and loose uniform sandy surface, which is quite representative of real desert surfaces, can be reproduced very well. Meanwhile, the surface transport rate and the characteristic and average saltation heights have been simulated and analyzed in detail, disclosing their relationships with friction velocity, particle size and roughness length, and the possible underlying mechanisms. Besides, the average particle lift-off velocity and the average mean vertical aerodynamic action upon the ascending particle, which determine the saltation process, are explicitly expressed by parameters involved in the similarity model, and their relationships with friction velocity, particle size and roughness length are also described concisely. The corrected average particle lift-off velocity makes it possible to investigate the characteristic particle trajectory, whose initial velocity equals the average lift-off velocity, so as to estimate the average particle against surface impacting velocity and the average aerodynamic action upon the saltation process.     

一次持续空气污染过程的气象条件分析

2004年12月24-30日,淄博地区出现一次持续一周的强空气污染过程.本文分析了这次过程的主要污染物浓度变化特征和造成强污染的天气形势特点以及主要的气象变量分布特征.结果表明:高空维持稳定纬向环流、逆温层结持续存在、混合层厚度增加、低空风速小,导致污染物不能及时随大气扩散,污染物浓度增大.     

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.     

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.     

城市创意产业空间动态集聚演化的计算与可视优化方法

基于人文地理视角下的城市创意产业图像可视化分析对城市深层次空间综合和区域创新发展具有重大意义。但Swarm群智能动态时空建模难以满足创意产业空间集聚的可视化发展。本文研究目标是,从城市区域创意产业空间聚类影响因素指标出发,创新性地提出区域空间动态集聚轨迹算法（Density-Based Interest Spatial Clustering of Path,DBICP）,并与计算机浏览器共建聚类可视化图像,为城市管理提供决策依据。首先,根据影响因素指标体系,利用2014—2018年空间卡口流量数据和产业指标数据进行预处理,构建空间标准聚类算法DBSCAN。然后,对其进行聚类密度分级优化形成全新DBICP算法并得出初步轨迹图像。最后,通过源码转译实现了浏览器界面下空间动态集聚轨迹图像的输出。结果表明：以上海市为例,普陀区、浦东新区、徐汇地区的创意产业空间分布形成了3种不同的聚类模式,并相应提出了分摊、均布、虹吸的管控策略。此方法克服了传统图像的聚类分级和轨迹测量的缺失,可以有效地从指标数据中发现图像轨迹聚类信息,体现了地理信息科学和人文社会学科的交叉融合。也为大数据动态图像的集聚方法提供了全新视角和借鉴价值。     

基于粒子滤波的行车轨迹路网匹配方法

行车轨迹是一种时间序列的地理空间位置采样数据,而传统的轨迹—路网匹配方法主要以全局或局部寻优的方式建立轨迹—路网匹配关系,影响了时空场景中数据的匹配计算过程的相对独立性。针对这个问题,本文基于粒子滤波（Particle Filter,PF）原理建立行车轨迹与道路网络之间的匹配关系。首先,沿轨迹中车辆运动方向在道路网络中搜索邻近道路节点,在与道路节点拓扑邻接的道路弧段上初始化随机生成粒子,根据轨迹中车辆运动模型将粒子沿所在道路弧段移动;然后,基于PF原理计算各时刻粒子运动状态及与行车轨迹采样点之间的距离误差,根据高斯概率密度函数计算粒子权重并利用随机重采样方法进行粒子重采样,迭代更新粒子运动状态;最后,计算与搜索到的道路节点拓扑邻接的每条道路弧段中累计粒子权重,通过各道路弧段累计权重计算轨迹—路网匹配关系。以行车轨迹进行实验表明,利用本文方法可以通过粒子时空变化反映采样点的移动,行车轨迹—路网匹配结果的正确率大于85%,能够实现行车轨迹和路网的准确匹配。     

粗精匹配结合的矢量道路辅助惯性导航算法

针对无源环境下无法采用卫星导航定位方式对惯性导航系统的累积误差进行修正的问题,提出一种采用高精度的矢量道路数据进行粗精匹配的导航校正算法。该方法首先通过分析惯导轨迹的特征标示点以及外接矩形,剔除明显的误匹配道路和冗余道路,获取待匹配道路集合;进而,结合ICCP算法具有匹配精度较高,匹配结果较稳定的优点,完成惯导轨迹位置误差的补偿和校正;最后,根据匹配方差和历史匹配轨迹对匹配结果的准确性进行分析和判断。仿真实验结果表明,该算法能够提高惯性导航定位误差的校正精度,减少在复杂道路交叉口等情况的误匹配。