基态修正时空数据模型的优化研究

分析已有基于基态修正模型的7种数据存储方式,在此基础上提出基态存储优化的多基态多级差文件修正模型。与已有方法相比,该方法具有节省存储空间,数据检索、恢复效率高,受历史长短影响小,效率受基态阈值影响小等特点。     

一种多基态修正时空数据模型改进的方法

本文分析现有的7种基态修正模型,发现多基态修正模型中的基态均以快照方式存储,当历史久远时需要创建的基态数较多,所占用的存储空间较大,如果不建立那么多基态,两个基态之间的差文件过多,就会使检索效率低下,所以说现有的多基态修正模型并不能真正做到不受历史久远的影响。作者提出为基态建立索引的方法可以解决该问题,该方法不仅可减少多基态修正模型中基态所占用的存储空间,并且可以提高时空数据操作的效率,真正做到不受历史久远的影响。     

基于基态修正模型的矢量数据增量更新机制研究

针对矢量数据库的数据更新及更新后历史数据维护等一系列问题, 设计实现了一种基于基态修正扩展模型的增量更新方法, 以当前最新矢量数据库的状态作为基态, 用增量信息来记录变化信息并驱动基态更新, 以多级差文件形式保存新旧基态间的数据差异, 形成历史数据以供检索回溯。在此基础上, 进行了增量更新存储模型和技术流程设计, 给出了增量信息和差文件对应的数据结构, 开发了矢量数据库更新原型系统, 较好地实现了矢量数据库的增量更新。     

一种基于动态基态方法的时空数据模型扩展

分析现有时空数据基态修正模型研究的进展,提出一种基于动态基态的时空数据基态修正方法,引入动态基态数据与动态数据存储区域的概念,解决重复检索历史数据以及对时间段的检索效率低下的问题,具有基态数据占用空间较小、数据检索速度快的特点,且当历史年代久远时,存储空间最小。     

基于动态基态方法的基态修正时空数据模型

本文在分析现有时空数据基态修正模型的基础上,提出一种基于动态基态的时空数据基态修正方法,引入了动态基态数据与动态数据存储区域的概念。解决了重复检索历史数据以及对时间段的检索效率低下的问题,具有基态数据占用空间较小,数据检索速度快的特点,且当历史年代久远时,存储空间最小。     

地形图建筑物时空数据模型研究

论述地形图更新和建立地形图时空数据库的必要性,总结时空数据模型的几种类型,认为城市大比例尺地形图时空数据库的建设采用基态修正模型是适宜的.分析城市建筑物更新的几种类型并将其分解为3种基本类型.给出基态修正模型中基态值和差值的实现方法.实验表明用基态修正模型派生得到的地形图与外业修测得到的地形图是一致的,可以通过基态和差值实现历史数据的恢复.     

基态修正的GIS数据库增量更新建模

为了对GIS数据库增量更新中的增量信息、版本数据库进行组织和管理,本文采用基态修正模型,将版本数据作为基态、版本数据对应的增量信息作为修正,分别设计了其存储的数据结构,并给出了基于该模型的增量更新流程。以1∶2000地形图数据的增量更新为例,开发了空间数据库增量更新原型系统,实现了多个增量信息文件的组织存储、快速集成到历史数据中,能够满足GIS数据库增量更新的需求。     

基于事件-状态的地理时空数据模型研究

分析了事件与状态的关系,对基态修正模型加以改进,根据事件发生的频繁程度及每次数据量更新的大小,建立历史基态,对于历史基态间的地理实体数据只记录其相对于历史基态的变化部分.既克服了基于快照时空数据模型产生的大量冗余数据,又克服了基态修正模型随着修正次数的增加,历史重构需要大量的复杂计算的缺陷,具有历史数据恢复快的优势.     

面向对象的基态修正模型改进及查询方法

空间和属性随时间变化规律的研究是时态GIS核心问题。引入面向对象方法,把地理实体抽象成为具有空间、属性和时间特性的空间对象,以空间对象的变化状态为基础,探讨了地理实体时空信息连续变化对基态修正模型的影响。通过引入基态距影响因子研究了基态修正模型改进方法,重点研究了基于面向对象基态修正模型的基态距确定、基态的建立过程和模型的存储;并具体设计了基于时间点和时间段的查询方法,为空间对象的时空数据存储、查询和历史回溯提供了基础。     

基于基态距优化的改进基态修正时空数据模型研究

传统的基态修正时空数据模型对历史数据的存储和检索效率较低,本文以对象变化临界指数确定基态距的方法,对传统的基态修正模型进行改进,提高了历史数据的存储和检索效率,并针对改进后的基态修正模型分析了时空数据的检索流程,实现了多种方案的时空数据检索,通过在广州市公共交通规划管理地理信息系统中的应用,对模型的实用性进行了验证。     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

利用GPS轨迹二次聚类方法进行道路拥堵精细化识别

针对当前在精细识别道路拥堵时空范围方面研究的不足,提出一种利用GPS轨迹的二次聚类方法,通过快速识别大批量在时间、空间上差异较小且速度相近的轨迹段,反映出道路交通状态及时空变化趋势,并根据速度阈值确定拥堵状态及精细时空范围。首先将轨迹按采样间隔划分成若干条子轨迹,针对子轨迹段提出相似队列的概念,并设计了基于密度的空间聚类的相似队列提取方法,通过初次聚类合并相似子轨迹段,再利用改进的欧氏空间相似度度量函数计算相似队列间的时空距离,最后以相似队列为基本单元,基于模糊C均值聚类的方法进行二次聚类,根据聚类的结果进行交通流状态的识别和划分。以广州市主干路真实出租车GPS轨迹数据为例,对该方法进行验证。实验结果表明,该二次聚类方法能够较为精细地反映城市道路的拥堵时空范围,便于管理者精准疏散城市道路拥堵,相比直接聚类方法可以有效提升大批量轨迹数据的计算效率。     

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

基于空间加权距离的自适应Fuzzy C-Means算法研究

空间聚类不仅应考虑GIS对象属性特征的相似性,还应考虑对象的空间邻近性。不同属性、位置特征在聚类中起到的作用不同。采用信息熵方法计算空间距离中各属性距离、位置距离的权重,权值大小用于度量相应特征在fuzzy c-means隶属度计算时的作用大小,并引入相似性指标,当两个聚类之间的相似度高于某个合并阈值时,则对应的一对聚类进行合并,从而克服需预先设置聚类类数的问题。通过应用实例的聚类有效性分析,与普通空间距离相比,基于空间加权距离的FCM算法具有稳定性和有效性。     

时空数据模型的研究

分析了现有的时空数据模型,包括时间——空间立方体模型、序列快照模型、基态修正模型、时空复合模型、基于事件的时空模型、面向对象的时空模型和图谱模型等,对各种模型进行了比较和深入探讨。