Selective omission of road networks in multi-scale representation

Pao Yue-kong Library Electronic Theses Database

Selective omission of road networks in multi-scale representation


Author: Zhou, Qi
Title: Selective omission of road networks in multi-scale representation
Degree: Ph.D.
Year: 2012
Subject: Roads.
Geographic information systems.
Cartography -- Data processing.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Land Surveying and Geo-Informatics
Pages: xiv, 142 leaves : ill., maps ; 30 cm.
Language: English
InnoPac Record:
Abstract: Spatial data can be represented at different scales, which leads to the issue of multi-scale spatial representation. Multi-scale spatial representation has found wide applications in web mapping and small-format mobile devices. So far, there are still two main limitations with current methodologies. First, continuous transformation of spatial representation to any arbitrary scale is still not available; second, spatial data are represented at different scales for different applications and are updated separately. An ideal solution is to automatically transform the spatial representation at the largest scale to that at any smaller scale. This transformation may involve a series of operations such as collapse, selective omission, simplification, smoothing and displacement. This study focuses on the selective omission in a road network, which is one type of the most important features on a map. More specifically, three issues are addressed, i.e., (1) formation of roads in a network from road segments; (2) selective omission of roads; and (3) determination of the percentage of roads to be selected. A road network in the database is normally stored in the form of intersections and segments. However, recognition is normally performed on roads. Thus it is very desirable to build road segments into long individual roads (called strokes). In stroke building, a total of seventeen strategies with geometric, thematic and hybrid parameters as criteria were investigated. In this investigation, Hong Kong maps at different scales were used, a measure (i.e., accuracy rate) was proposed and statistical tests were carried out to detect any significance differences of performance using these strategies. It was found from experimental results that the differences in performance by most of these strategies are not significant. It was also found that the inclusion of thematic attributes (e.g., road class or road name) may sometimes improve the performance.
For selective omission in a road network, two typical existing approaches (i.e., stroke-based and mesh-based) were first evaluated by both quantitative analysis and visual inspection. It was found that the stroke-based approach performs better in a road network with linear patterns; by contrast, the mesh-based approach only performs better in a road network with areal patterns (e,g., grid-like patterns). This inspired us to develop an integrated approach. In this integrated approach, a hierarchical structure for both meshes and linear roads were first built and these two hierarchies were integrated into a single structure. Experimental results showed that this integrated approach performs much better than the two existing approaches. In addition, there also is a need for determining the percentage of roads for selection. This process involves either some scale-related parameters or empirical models (e.g., the classic 'Radical Law') to express the relationship between a map scale and the number of roads to be selected. However, experimental results showed that these parameters may vary between cases, and these models are not suitable for all possible cases. It is therefore very desirable to adaptively determine the percentage required for a representation at a specific scale. The back propagation neural network was adopted to give such a solution. Experimental results verified the feasibility of using this approach to adaptive selective omission. In summary, in this study, at first experimental tests were carried out to evaluate existing methodologies and new methodologies were developed based on the findings from these tests, including an integrated approach and the new application of back propagation neural network. Statistical tests show that these two methodologies work well. However, automated transformation in scales is a rather complex problem and further development is still needed.

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