Discovering Spatial Interaction Communities from Mobile Phone Data

In the age of Big Data, the widespread use of location‐awareness technologies has made it possible to collect spatio‐temporal interaction data for analyzing flow patterns in both physical space and cyberspace. This research attempts to explore and interpret patterns embedded in the network of phone‐call interaction and the network of phone‐users’ movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman‐Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial‐interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone‐call interaction, which indicates that people tend to communicate within a spatial‐proximity community. Furthermore, we discover that a high correlation exists between phone‐users’ movements in physical space and phone‐call interaction in cyberspace. Our approach presents a combined qualitative‐quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks.     

Integrated data processing method for GPS and INS field test over Rocky Mountain

The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS outputs and the offset of GPS and INS, state equations and observations are designed to perform the calculation and improve the navigation accuracy. An example shows that with the method the reliable navigation parameters have been obtained.     

刍议《重庆历史地图集·第一卷古地图》的设计思想

介绍了图集的基本情况及当前编纂出版历史地图集的不足,以此为基础提出了本图集内容选录的特点。重点对《重庆历史地图集·第一卷古地图》中涉及的内容、结构、文字符号化、版式及各元素设计思想作了全面的阐述,并将这些设计思想应用于本图集,取得了较好的效果。最后,总结了图集设计的创新点,为今后其他省市编制相关的图集作参考。     

警用地理信息平台中三维全景系统建设研究

针对当前警用地理信息平台中空间信息表达直观性欠缺的缺憾,提出了三维全景系统的解决方案。结合警用地理信息平台建设的要求,简述了三维全景技术的原理,详细介绍了在PGIS平台中三维全景系统的系统构架、数据体系、系统功能等,并探讨了系统实现的关键技术。通过实际应用,验证了系统建设的合理性和可操作性,为各地公安三维全景系统的建设提供了较好的参考和借鉴。     

地理国情普查质量控制体系构建及效果分析

地理国情普查是采用空间化的方法反映自然要素与人文要素的空间分布、基本特征及其相互关系,为政府提升宏观决策的科学性及优化各类资源的配置提供依据。完善的质量控制体系是获取真实、准确、可靠的地理国情成果的有力保障。本文介绍了地理国情普查质量控制体系设计出发点、内容及其效果,对未来开展地理国情常态化监测的质量控制具有一定借鉴意义。     

结合模糊分割和遗传算法的航空影像车辆提取方法

利用高分辨率遥感影像实现道路车辆监测在交通管理系统中占有重要地位,然而受成像条件及车辆本身颜色多样性的影响,很难从遥感影像中直接提取道路车辆。为此,本文提出了一种结合遗传算法和数学形态学的遥感影像道路车辆提取方法。首先利用模糊聚类方法建立影像分割模型,并利用遗传算法对其进行求解,从而实现影像中道路、车辆及环境的精确分割;然后对分割结果中不同层进行赋值,实现分割结果的二值化;最后利用数学形态学操作从分割的道路-车辆的二值结果中剔除道路,从而实现道路车辆的提取。利用本文方法对航空影像进行了道路车辆提取试验。试验结果表明,本文方法不仅能够准确识别地面车辆位置,还能精确提取其轮廓线。     

基于Spark Streaming的实时流数据处理模型化研究与实现

通过研究与分析,选取Spark Streaming技术实现对P实时流数据的处理.同时,研究出一套模型化的方式,实现动态装配软件的执行过程;并通过具体的实例展示了两者结合后,在数据处理的易用性、性能及吞吐量方面,都得到了大幅提升.     

地铁隧道结构变形监测信息管理系统的开发

地铁隧道结构变形监测的特殊性、周期性和长期性,使其信息量非常庞大.信息管理是地铁隧道结构变形监测中一项重要的工作,现有的管理方式效率很低.为了高效、准确地管理监测信息,及时分析预报地铁隧道结构的稳定状况,本文结合南京地铁运营期隧道结构变形监测实例,开发了一套具有变形监测资料存储、预处理、管理分析、可视化分析、预测预报及限值预警等功能的信息管理系统,保证了准确及时快速的数据处理和信息反馈,具有良好的运用和推广前景.     

Algorithm for Harmonic Tidal Analysis along T/P Tracks： Taking Differences of Observed Sea Surface Heights at Adjacent Points as Observations

An algorithm （differential mode） is presented for the improvement of harmonic tidal analysis along T/P tracks, in which the differences between the observed sea surface heights at adjacent points are taken as observations. Also, the observation equations are constrained with the results of the crossover analysis; the parameter estimations are performed at 0.1° latitude intervals by the least squares. Cycle 10 to 330 T/P altimeter data covering the China Sea and the Northwest Pacific Ocean （2°-50° N,105°-150° E） are adopted for a refined along-track harmonic tidal analysis, and harmonic constants of 12 constituents in 8 474 points are obtained, which indicates that the algorithm can efficiently remove non-tidal effects in the altimeter observations, and improve the precision of tide parameters. Moreover, parameters along altimetry tracks represent a smoother distribution than those obtained by traditional algorithms. The root mean squares of the fitting errors between the tidal height model and the observations reduce from 11 cm to 1.3 cm.     

一种随机增量式地形建模算法设计

Delaunay三角网是DEM主要表示方式，一直以来，因其数据结构和构造算法的复杂性导致应用受限。本文结合随机增量式算法理论，提出一种全新的有向无环图（directed acyclic graph，DAG）查找数据结构（search data structure）和随机增量式Delaunay三角网构造算法，并对算法作了简要证明和深入分析。该算法最大的特点是具有随机特性，能够支持地形特征数据的动态更新。