Graph Based Recognition of Grid Pattern in Street Networks

  • Jing Tian
  • Tinghua Ai
  • Xiaobin Jia
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Pattern recognition is an important step in map generalization. Pattern recognition in a street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines roughly at right angles. Inspired by object recognition in image processing, this paper presents an approach to grid recognition in street network based on graph theory. Firstly, bridges and isolated points of the network are idenepsied and repeatedly deleted. Secondly, a similar orientations graph is created, in which the vertices represent street segments and the edges represent similar orientation relationships between streets. Thirdly, the candidates are extracted through graph operators such as the finding of connected components, finding maximal complete sub-graphs, joins and intersections. Finally, the candidates are repeatedly evaluated by deleting bridges and isolated lines, reorganizing them into stroke models, changing these stroke models into street intersection graphs in which vertices represent strokes and edges represent strokes intersecting each other. The average clustering coefficient of these graphs is then calculated. Experimental results show that the proposed approach is valid in detecting the grid pattern in lower degradation situations.


Street Network Parallel Line Cluster Coefficient Street Segment Intersection Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the National High-Tech Research and Development Plan of China under the grant No.2009AA121404, and the China Postdoctoral Science Foundation funded project under the grant No. 20100480863. Special thanks are due to an anonymous reviewer for constructive comments which substantially improve quality of the paper. We also thank Lawrence W. Fritz for polishing up our English.


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Resource and Environment ScienceWuhan UniversityWuhanChina
  2. 2.Key Laboratory of Geographic Information System, Ministry of EducationWuhan UniversityWuhanChina

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