Mining knowledge from result comparison between spatial clustering themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.     

二维空间关系定性表达模型的研究

通过对几种典型的定性表达模型的分析及比较,论述了单一模型实现空间查询所存在的局限,提出利用组合模型来表达空间关系的方法。最后结合空间查询中的实例,通过介绍如何同时运用二值拓扑关系模型和符号空间索引模型来实现同时包含拓扑关系和方向关系的复杂空间查询来说明这种方法。     

A data‐mining approach for assessing consistency between multiple representations in spatial databases

When different spatial databases are combined, an important issue is the identification of inconsistencies between data. Quite often, representations of the same geographical entities in databases are different and reflect different points of view. In order to fully take advantage of these differences when object instances are associated, a key issue is to determine whether the differences are normal, i.e. explained by the database specifications, or if they are due to erroneous or outdated data in one database. In this paper, we propose a knowledge‐based approach to partially automate the consistency assessment between multiple representations of data. The inconsistency detection is viewed as a knowledge‐acquisition problem, the source of knowledge being the data. The consistency assessment is carried out by applying a proposed method called MECO. This method is itself parameterized by some domain knowledge obtained from a second method called MACO. MACO supports two approaches (direct or indirect) to perform the knowledge acquisition using data‐mining techniques. In particular, a supervised learning approach is defined to automate the knowledge acquisition so as to drastically reduce the human‐domain expert's work. Thanks to this approach, the knowledge‐acquisition process is sped up and less expert‐dependent. Training examples are obtained automatically upon completion of the spatial data matching. Knowledge extraction from data following this bottom‐up approach is particularly useful, since the database specifications are generally complex, difficult to analyse, and manually encoded. Such a data‐driven process also sheds some light on the gap between textual specifications and those actually used to produce the data. The methodology is illustrated and experimentally validated by comparing geometrical representations and attribute values of different vector spatial databases. The advantages and limits of such partially automatic approaches are discussed, and some future works are suggested.     

Discrete displacement analysis for geographic linear features and the application to glacier termini

The extent of glacier terminus displacement is instrumental in investigations of natural or artificial geographic changes. Its importance to earth science and engineering is reflected in the considerable efforts that have been devoted to the development of several boundary displacement analysis methods. Among the methods, the buffering-based approach compares favorably with other approaches in objectivity and robustness. However, it does not consider the relative positions of boundaries, because its buffering operation cannot determine features' relative directions. This limitation incurs inaccurate calculation results – underestimation of mean shifts and overestimation of shape variations, especially when the two compared boundaries intersect. Discrete displacement analysis (DDA), an alternative method that considers given geographic objects as a set of a finite number of points, is proposed here. In a series of tests carried out, including Jakobshavn glacier's calving front, DDA was found to correctly calculate mean shift and shape variation even in cases where the conventional buffering-based method failed. Moreover, this approach is independent of the dimension of space in which it is implemented, and thus is expected to be utilized for analysis of 3D geographic object displacement.     

Integration of linear and areal hierarchies for continuous multi-scale representation of road networks

Spatial data can be represented at different scales, and this leads to the issue of multi-scale spatial representation. Multi-scale spatial representation has been widely applied to online mapping products (e.g., Google Maps and Yahoo Maps). However, in most current products, multi-scale representation can only be achieved through a series of maps at fixed scales, resulting in a discontinuity (i.e., with jumps) in the transformation between scales and a mismatch between the available scales and users' desired scales. Therefore, it is very desirable to achieve smoothly continuous multi-scale spatial representations. This article describes an integrated approach to build a hierarchical structure of a road network for continuous multi-scale representation purposes, especially continuous selective omission of roads in a network. In this hierarchical structure, the linear and areal hierarchies are constructed, respectively, using two existing approaches for the linear and areal patterns in a road network. Continuous multi-scale representation of a road network can be achieved by searching in these hierarchies. This approach is validated by applying it to two study areas, and the results are evaluated by both quantitative analysis with two measures (i.e., similarity and average connectivity) and visual inspection. Experimental results show that this integrated approach performs better than existing approaches, especially in terms of preservation of connectivity and patterns of a road network. With this approach, efficient and continuous multi-scale selective omission of road networks becomes feasible.     

Fuzzy ontology-based foreshore identification from digital terrain models and very high-resolution airborne imagery through GEOBIA multi-scale analysis

ABSTRACT

Coastal mapping provides information for coastal zone protection. Land foreshore-border mapping is important, since it is related to land and property management, raising the need for an automated foreshore delineation framework. This research develops a multi-scale GEographic Object-Based Image Analysis method for foreshore identification from single-date very high-resolution imagery and DTM. Foreshore interpretation criteria were provided by the Greek Cadastral Office; however, they defined the foreshore-borders in an ill-defined manner. Thus, an ontology was designed and implemented to formalize the implicit spectral, geometric, and spatial relationships described in the interpretation criteria, and employ them during identification. The final ontology was delivered after three design phases: specification, conceptualization, and knowledge formalization. Imagery preprocessing through anisotropic morphological levelings reduced undesired spatial detail and noise. A three-level segmentation and ontological classification approach was developed to compute proper objects and assign them into semantic categories. Class definitions were created through fuzzy restrictions, representing value ranges of the employed properties. Evaluation of the extracted foreshore area with human-interpreted reference data showed a 77.9% quality, which was satisfactory. Evaluation by comparing the extracted foreshore border to the reference data showed an average deviation of 2.4 m, which is within Greek Cadastral Office specifications.     

Approximation approach to the SRM based on root decomposition in the simulation of spatially varying ground motions

The spectral representation method(SRM) is widely used to simulate spatially varying ground motions.This study focuses on the approximation approach to the SRM based on root decomposition,which can improve the efficiency of the simulation.The accuracy of the approximation approach may be affected by three factors: matrix for decomposition,distribution of frequency interpolation nodes and elements for interpolation.The influence of these factors on the accuracy of this approach is examined and the following conclusions are drawn.The SRM based on the root decomposition of the lagged coherency matrix exhibits greater accuracy than the SRM based on the root decomposition of the cross spectral matrix.The equal energy distribution of frequency interpolation nodes proposed in this study is more effective than the counter pith with an equal spacing.Elements for interpolation do not have much of an effect on the accuracy,so interpolation of the elements of the decomposed matrix is recommended because it is less complicated from a computational efficiency perspective.     

The incidence of internal waves onto a thin submerged barrier

Abstract

The problem of oblique incidence of internal ocean waves on a thin submerged ocean barrier is considered when the ocean has exponential density stratification. A Wiener-Hopf approach is used combined with numerical evaluation of series. Results for the reflected energy are obtained and reveal a complex dependence on incidence and barrier height. Application of this model to waves incident on the Mid-Atlantic ridge suggests that the ridge almosts isolates first mode energy on one side of the ocean from the other side. In certain circumstances there, is a surprising appearance of “barrier” waves. These waves are closely confined to the barrier and propagate along it.