Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data

In this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general.     

顾及地理语境的旅游轨迹停留点语义标注

轨迹数据作为典型的时空大数据,具有较高的研究和应用价值。然而现有的轨迹数据挖掘主要聚焦于轨迹的空间特征,而较少关注轨迹数据语义的深度分析。本文面向智慧旅游服务的需求,重点探讨了旅游轨迹的轨迹停留点语义的自动标注问题。首先,针对POI短文本的特点,提出了基于《同义词词林》进行短文本语义扩展的方法对POI短文本进行特征扩展;同时,在顾及POI短文本的关键词集中、类别词分散等特征基础上,提出了改进TF-IDF的POI自动分类方法;其次,在POI分类的基础上,采用Native Bayes方法对轨迹停留点进行语义标注。结果表明,基于改进TF-IDF方法的POI自动分类可以达到约83%的精度,能够较好地实现POI的分类;而在POI自动分类基础上,基于Native Bayes的轨迹语义标注可以达到74%的精度,较好地实现了旅游轨迹停留点自动语义标注的目标。     

Extracting and analyzing semantic relatedness between cities using news articles

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.     

A temporal-contextual analysis of urban dynamics using location-based data

The dynamics of urban activities in Jerusalem were studied by analyzing a large-scale semantically rich movement dataset. The semantic enrichment process was based on coupling movement trajectories sampled by GPS loggers with contextual data derived from trajectory-based digital activity dairies. Although the utility of such procedures in generating trajectory-specific semantic data was noted before, their application stays limited. In this paper, we promote the utilization of these procedures by demonstrating that they are not only feasible but also important for mobility studies. We discuss the characteristics of the semantic enrichment process and the manner by which it was applied within the large-scale analysis of urban dynamics. This application uncovered a time- and context-dependent array of centers in Jerusalem, resulting in a semantically rich characterization of urban dynamics. Such characterization provides otherwise unobtainable insights crucial for urban analysis. Wider implications for movement studies may be derived, as this application demonstrates how diary-based enrichment approaches hold the potential to advance bridging the semantic gap in mobility research.     

FME在地理信息系统互操作上的应用研究

介绍了GIS互操作技术,对FME平台体系结构和语义转换技术进行了分析,并概括了FME平台的特性和优势。     

基于事件语义的地籍管理信息系统时空建模

在分析地籍管理信息系统对时空信息需求的基础上,引入事件语义描述,提出了基于事件语义的地籍管理信息系统时空模型,该模型通过描述事件的五个方面<对象、时间、空间、事件、属性(专题)>,记录宗地变更过程,建立时空模型,并根据地籍管理对时空数据的需求,分别从时间点、时间段角度对历史信息进行查询和回溯,验证了模型的有效性。     

基于交通网络几何和语义(交通)规则集成的公交换乘研究

换乘信息特别是基于地理信息系统技术的图文换乘信息服务,已经成为城市公共交通信息服务、线网规划和线路优化调整的重要指标与内容。但是公共交通线网具有特殊的网络特征和拓扑关系,它不同于城市道路网络,因此公共交通换乘不能简单应用于目前的最优路径算法,例如Dijkstra算法。本文将通过对公共交通线网的空间特征和语义(交通)规则分析,建立城市交通网络的几何和语义连通性的集成关系。几何连通性是语义连通性的基础和先决条件,而语义连通性是公共交通网络拓扑的核心。基于语义连通性的公共交通线网拓扑关系提升公共交通换乘算法的效率和信息准确度,任何两个站点之间的最优路径可以被迅速获取而不需要检索整个网络的几何拓扑信息。并且该最优换乘路径考虑到最少换乘次数、出行距离最短、费用最少、途经站点最少和换乘中最短步行距离等多目标换乘。     

主题学习和稀疏表示的MODIS图像超分辨率重建

针对MODIS图像分辨率受传感器限制和噪声干扰,且分辨率局限在一定水平等问题,提出一种采用主题学习和稀疏表示的MODIS图像超分辨率重建方法,该方法通过双边滤波将MODIS图像的平滑及纹理部分分离,并将纹理部分看成是由若干"文档"组成的训练样本;运用概率潜在语义分析提取"文档"的潜在语义特征,从而确定"文档"所属的"主题"。在此基础上,针对每个主题所对应的图像块,采用改进的K-SVD方法训练若干适用于不同主题的高低分辨率字典对,从而可以运用这些字典对,通过稀疏编码实现测试图像相应主题块的超分辨率重建。实验结果表明,重建图像在视觉效果和PSNR等指标上均优于传统方法。     

关于遥感影像处理智能系统的若干问题

当前,研究开发遥感影像处理智能系统已经是一项紧迫的任务。讨论研究了这类系统的几个重要的问题。这一系统应以影像分割形成的像斑作为分析处理的对象,为此,需要采取多尺度分割方法。针对每一地物类别选择最佳尺度或尺度组合,而不是对所有地物类别采用统一的所谓最佳尺度。系统应以地域性地物分布为其特点构建知识库,而不是要求所谓的通用性。采用地物分布语义网的方式,在其节点上建立起与相应地物类型相关的数据、算法的联系,包括影像特征选择、算法选择,并在此基础上讨论了有关数据库、算法库、知识库的问题以及人机协同问题。     

Building Extraction from LIDAR Based Semantic Analysis

IntroductionThanks to a large number of applications ,suchas map updating, urban planning or land useanalysis ,extraction of artificial objects ,such asbuildings ,roads fromthei mages has been an ac-tive research field for many years . Since build-ings ar…