The Algorithms of Geometry Similarity Measurement and Experimental Analysis for Linear Spatial Data Transmission

  • 1. Shanxi Coal Tonsportation and sales Group Datong Co., LTD. Datong 037004, China;
    2. Department of Mining Engineering, University of Shanxi Datong, Datong 037003, China;
    3. Department of Surveying and Geo-informatics, Central South University, Changsha 410083, China

Received date: 2011-05-03

  Revised date: 2011-08-31

  Online published: 2011-10-25


Transmission and expression of multi-scale spatial data is of greatly significance for the quality of the evaluation of progressive transmission of spatial information by geometric similarity measurement, which has plagued GIS researchers for a long time. The same feature from different sources or different scales is usually similar on the map, so the similarity measurement of geometry shape is conducive to the preparation, query, match and update of the map. The line features on the map are in many high proportions, so this paper proposes the line features geometry similarity measurement models of spatial data on the basis of the predecessors. And then, a reclassification of geometric similarity measurement is made from the consideration of distance, shape, length. The corresponding geometric similarity measurements are further developed, including: (1) differences in distance as the similarity, considering shape differences and relative positions of spatial objects; (2) the fractal dimension as a similarity of shape similarity, to a large extent, being able to express morphological characteristics of spatial objects; (3) the length or girth size as the similarity. Relative to (3) length similarity, (1) position similarity and (2) shape similarity are both considered the overall statistical methods and local geometry structure. To complete the linear multi-scale geometric similarity measurement experiments of spatial data, and compare the relationship of different transmission volume and geometric similarity measurement by multi-scale linear features as the experimental data, the experimental results show that location similarity based on generalized Hausdorff distance model is feasibility to progressive transmission of spatial data. The change for location similarity measurement is smaller than those with other methods based on the same amounts of data transmission, indicating that the algorithm (generalized Hausdorff distance model) solves the similarity problem of the same feature. Finally, this paper summarizes main findings, and highlights further research directions in the near future, such as match, query, update and other issues for multi-scale spatial data.

Cite this article

SUN Jinli, CHEN Jie, DENG Min . The Algorithms of Geometry Similarity Measurement and Experimental Analysis for Linear Spatial Data Transmission[J]. Journal of Geo-information Science, 2011 , 13(5) : 701 -706 . DOI: 10.3724/SP.J.1047.2011.00701


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