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。     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

改进的最小生成树自适应空间点聚类算法

针对传统的最小生成树聚类算法存在使用全局不变阈值确定噪声边,聚类需要用户根据经验确定初始化聚类参数,如“边权值倍数容差”,“边长变化因子”等,聚类不能发现局部噪声的问题,本文提出了一种改进的最小生成树自适应空间点聚类算法。该算法在无需用户输入参数的前提下,克服主观因素的影响,根据最小生成树边长的数理统计特征定义裁剪因子。算法首先从宏观层面对最小生成树进行首轮删枝操作,消除全局环境下的噪声边,进而根据各子树的边长统计情况,自适应设定局部裁剪因子,进行第二轮删枝操作,消除局部环境下的噪声边。最后,采用1个模拟数据和1个实际应用验证算法的有效性,结果表明本文提出的改进算法在无需人为提供经验参数的环境下能够发现任意形状、不同密度的簇,能够准确的识别出空间点中的噪声数据,从而能够实现空间点数据背后隐藏信息的自动挖掘。     

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。     

A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

不同草地类型净初级生产力(NPP)模拟及其敏感性分析

气候变化问题作为人类社会可持续发展面临的重大挑战,受到国际社会越来越强烈的关注。全球气候变化深刻影响着草地生态系统,定量评估区域和不同类型草地生态系统的生产力,研究其对气候变化的敏感性可以为草地生态系统适应未来气候变化提供基础数据和理论依据。草原综合顺序分类系统(CSCS)将天然草原分为42类(其中中国包含41类),并将其聚合为10个类组。研究利用改进Carnegie-Ames-Stanford Approach(CASA)模型模拟分析中国天然草地2004—2008年的净初级生产力(NPP)并进行系统聚类,分析了草地NPP与影响因子的相关性和敏感程度。结果表明:2004—2008年中国10个草地类组年均NPP均呈现增长趋势,其中亚热带森林草地增长最快,增长率达38.2%。温带湿润草地增长最慢,增长率为14.3%。通过聚类分析将中国41类草原的年均NPP分为3类:第1类NPP值较小,其湿润度级较低,而热量级较高;第2类NPP值较大,其热量级和湿润度级均较高,水热比适宜植被的生长;其余为第3类,其NPP值介于上述两者之间。可见,不同草地类型NPP分布规律与CSCS划分草地类型的湿润度级和热量级密切相关。草地年均NPP与>0℃年积温(Σθ)、降水量、湿润度(K)和NDVI的相关性强,与太阳辐射的相关性弱。草地类组NPP平均值对NDVI最敏感,其次为Σθ、K和降水量,敏感性最弱的为太阳辐射。草地NPP对CSCS量化分类的标准Σθ和K较为敏感,改进CASA模型在一定程度上实现了CSCS与草地生产力的耦合。     

签到位置数据的密度峰值快速搜索与聚类方法

位置签到数据蕴含了城市居民活动变化。由于客户端位置候选问题,不同的签到行为以同一候选位置签到时会产生位置重复现象。针对现有密度聚类方法在签到数据聚类上存在的问题,以快速搜索和查找密度峰值聚类算法(CFSFDP)为基础,提出了签到位置数据的密度峰值快速搜索与聚类方法。首先,引入位置重复频率来表达签到位置重复,然后,对原始签到位置数据点统计位置重复频率并重新设计数据结构,以新的空间点要素为研究对象寻找密度峰值点;最后,构建了峰值点密度簇聚类算法,在点要素集聚类过程中考虑密度连通性来保证峰值密度簇的连续与完整。试验表明,所提出的聚类方法有效避免了重复度较高的离群位置对象选为峰值并聚类的情况,并具有良好的空间适应性。所提取的密度峰值点不仅可以用来表示热区的中心,还能够反映热区的集中趋势,进而可以帮助探索热区的动态变化情况。     

一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法

从LiDAR数据中高精度地提取建筑物屋顶面是构建屋顶面拓扑关系、实现建筑物三维模型重建的关键。本文针对现有算法提取复杂建筑物屋顶面适应性较差、精度较低等问题,提出了一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法。通过主成分分析计算点云特征,构建特征直方图,选取可靠种子点;利用提出的局部点云法向量分布密度聚类算法聚类种子点,快速准确地提取初始屋顶面片;构建基于邻域信息的投票模型,有效地解决屋顶面竞争现象。试验结果表明,本文方法可自动、高精度地提取屋顶面,对不同复杂程度的建筑物具有较好的适应性,能为建筑物三维模型重建提供可靠的屋顶面信息。     

一种融合超像素与最小生成树的高分辨率遥感影像分割方法

影像分割是面向对象高分辨率遥感影像分析的基础与关键。针对传统影像分割方法易受噪声影响,且难以确定合适的影像分割尺度的问题,本文提出了一种融合超像素与最小生成树的高分辨率遥感影像分割方法。首先用简单线性迭代聚类算法对影像进行过分割生成超像素;然后初始设定影像分割数,采用区域动态约束聚类算法对超像素进行合并,获得分割数-方差和、分割数-局部方差、分割数-局部方差变化率指标图,依据3个指标图确定合适的影像分割数;最后根据确定的合适影像分割数,采用区域动态约束聚类算法对超像素重新合并得到分割结果。定性对比试验和定量评价结果表明,本文方法可以有效地克服影像噪声对分割结果的影响,获得良好的影像分割结果。