Adding the missing third and fourth dimensions to trajectory analysis in carbonate systems

We developed a seismic geomorphology-based procedure to enhance traditional trajectory analysis with the ability to visualize and quantify lateral variability along carbonate prograding-margin types (ramps and rimmed shelves) in 3D and 4D. This quantitative approach analysed the shelf break geometric evolution of the Oligo-Miocene carbonate clinoform system in the Browse Basin and delineated the feedback between antecedent topography and carbonate system response as controlling factor on shelf break rugosity. Our geometrical analysis identified a systematic shift in the large-scale average shelf break strike direction over a transect of 10 km from 62° to 55° in the Oligo-Miocene interval of the Browse Basin, which is likely controlled by far-field allogenic forcing from the Timor Trough collision zone. Plotting of 3D shelf break trajectories represents a convenient way to visualize the lateral variability in shelf break evolution. Shelf break trajectories that indicate contemporaneous along-strike progradation and retrogradation correlate with phases of autogenic slope system re-organization and may be a proxy for morphological stability of the shelf break. Shelf break rugosity and shelf break trajectory rugosity are not inherited parameters and antecedent topography does not dictate long-term differential movement of the shelf margin through successive depositional sequences. The autogenic carbonate system response to antecedent topography smooths high-rugosity areas by filling accommodation and maintains a relatively constant shelf break rugosity of ~150 m. Color-coding of the vertical component in the shelf break trajectory captures the creation and filling of accommodation, and highlights areas of the transect that are likely to yield inconsistent 2D sequence stratigraphic interpretations.     

基于时空聚类的出租车载客热点区域挖掘研究

针对出租车运营过程缺少路径优化指导造成运营能力分布不均、空载率高的问题,本文以成都市安装有GPS设备的出租车所采集的轨迹数据为研究对象,以提高出租车效益为目标,采用了一种基于网格的出租车载客热点聚类算法,通过对出租车GPS轨迹数据进行处理和聚类分析,充分挖掘出租车载客热点区域,从而为出租车的运营者和管理者提供信息决策服务。     

移动轨迹数据可视化方法总结

作为移动社交网络的主体,人们移动带来的位置轨迹不仅记录了人的行为历史,也记录了人与社会的交互活动信息。移动社交网络中位置轨迹数据的分析与利用为解决城市问题提供了一种新的思路。本文概述了轨迹数据可视分析中的几种方法,总结了轨迹数据可视分析研究中存在的问题和面临的挑战。     

2003-2017年北京市地表热力景观时空分异特征及演变规律

利用2003-2017年MODIS地表温度数据,分别从数量、形状和结构角度揭示北京市不同季相和昼夜间地表热力景观时空分异特征,并进一步通过热力等级变化图谱及质心迁移轨迹揭示城市热力景观空间演变规律,探究热力景观等级转换生态过程。结论如下：① 城市地表热力景观季节和昼夜空间特征差异显著;② 中温区在城市热环境中占主导地位。白天中温区是最不稳定的热力景观等级;夜间次低温区和次高温区不稳定性增加;③ 地表热力景观等级变化以稳定型占主,反复变化型和前期变化型次之。地表热力景观等级通常呈现逐级递增或递减规律,山区呈现逐级降温趋势,北部城郊—山地交错地带表现出次低温和中温的反复转向,南部地区有一定的升温趋势;④ 2003-2017年高温区面积增大且质心向城市中心集中,低温区质心向城市外围扩散。生态涵养发展区对北京市地表热力景观质心迁移贡献率最高。热力景观时空分异特征及演变规律可为有效缓解城市热岛效应提供管理决策支持。     

一种高光谱遥感图像快速谱聚类算法

为获得分类效果更优良的遥感图像分类方式并解决高光谱遥感图像分类运算速度缓慢的问题,集成Lanczos算法与谱聚类算法,探讨了高光谱遥感图像谱聚类算法应用于遥感图像分类的可行性,提出了一种面向高光谱遥感图像的快速谱聚类算法;通过对比美国圣地亚哥机场高光谱遥感图像K-均值算法与谱聚类算法的分类结果,发现面向高光谱遥感图像的谱聚类算法易于识别线性地物,且分类的速度能得到较大提升。     

一种顾及道路复杂度的增量路网构建方法

针对传统路网采集和更新需要昂贵的实地测量以及大量的后续内业处理问题,提出了一种从大规模粗糙轨迹数据中自动生成路网的方法。该方法包含轨迹滤选和路网增量构建两步:第1步通过构建空间、时间、逻辑约束的规则模型,在消除数据中的噪音和冗余的同时,将原始轨迹进行合理分割,滤选形成规范轨迹集合;第2步基于信息熵计算轨迹点周围道路的复杂度,据此自动调节道路分割参数,不断将新产生的路段加入到路网,同时计算道路平均交通流量和速度等路况信息,遍历各规范轨迹的定位点重复以上处理过程,最终得到完整路网。通过昆明市200辆出租车采集的约6851万条轨迹数据进行路网构建试验,并与OpenStreetMap数据比较,证明了本文方法的有效性。与已有典型方法比较,本文方法能用更少节点提取更高质量的路网。     

模糊聚类定权法对SLR定轨精度的影响

针对卫星激光测距(satellite laser ranging,SLR)精密定轨过程中存在的测站观测数据合理定权问题,将一种改进的模糊聚类算法引入到SLR观测数据定权中。基于国际激光测距服务(International Laser Ranging Service,ILRS)提供的全球SLR测站性能报告,对测站进行近实时滑动分类定权,改变SLR数据处理中权重的经验或者随意性选取模式。经过LAGEOS1卫星2014年1月至2016年12月3年全球SLR实测数据处理的测试。结果表明,当考虑LAGEOS标准点总数、LAGEOS标准点RMS值以及LAGEOS标准点合格率这3项测站质量控制因素确定的测站权值能最大限度地提高卫星定轨精度和观测数据的使用效率,对参与计算的365个3d弧段数据,91.46%弧段精度得到提高,平均提高约3.7mm,且每个测站的定轨残差RMS也得到了降低。这对于正在迈向毫米级测量精度的SLR技术至关重要。     

Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.