Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Batagelj V, Mrvar A (2008) Pajek: Program for analysis and visualization of large networks. Version 1.22
Diestel R (2006) Graph theory, 3rd edn. Springer, Berlin
Heinzle F, Ander KH (2007) Characterising space via pattern recognition techniques: idenepsying patterns in road networks. In: Mackaness WA, Ruas A, Sarjakoski LT (eds) Generalisation of geographic information: cartographic modelling and applications. Elsevier, Amsterdam, pp 233–253
Heinzle F, Ander KH, Sester M (2005) Graph based approaches for recognition of patterns and implicit information in road networks. In: Proceedings of 22nd international cartographic conference, A Coruna, Spain
Heinzle F, Ander KH, Sester M (2006) Pattern recognition in road networks on the example of circular road detection. In: Raubal M, Miller HJ, Frank AU, Goodchild MF (eds) Geographic information science, GIScience2006, vol 4197. LNCS, Munster, pp 253–267
Iglin S (2003). grTheory-graph theory toolbox, http://www.mathworks.com/matlabcentral/fileexchange/4266. Accessed 30 Sep 2009
Ip H, Wong WH (1997) Detecting perceptually parallel curves: criteria and force-friven optimization. Comput Vis Image Und 68(2):190–208
Jiang B, Claramunt C (2004) A structural approach to the model generalization of urban street network. GeoInformatica 8(2):157–173
Mackaness W, Edwards G (2002) The importance of modelling pattern and structures in automated map generalization. In: Joint ISPRS/ICA workshop multi-scale representation of spatial data, Ottawa/Canada
Mackaness WA (2007) Understanding Geographic Space. In: Mackaness WA, Ruas A, Sarjakoski LT (eds) Generalisation of Geographic Information: Cartographic Modelling and Applications, Elsevier, Amsterdam, pp 1–10
Marshall S (2005) Streets and patterns. Spon Press, New York
Porta S, Crucitti P, Latora V (2006) The network analysis of urban streets: a dual approach. Physica A 369:853–866
Sarkar S, Boyer L (1994) A computational structure for preattentive perceptual organization: graphical enumeration and voting methods. IEEE Trans Man Cybern 24(2):246–266
Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Elsevier, Amsterdam
Thomson RC, Richardson DE (1999). The “Good continuation” principle of perceptual organization applied to the generalization of road networks. In: Proceedings of 19th international cartographic conference, Ottawa
Wang JY et al (1993) Principles of cartographic generalization. Surveying and Mapping Press, Beijing
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(4):440–442
Xie F, Levinson D (2007) Measuring the structure of road networks. Geogr Anal 39(3):336–356
Yang BS, Luan XC, Li QQ (2010) An adaptive method for idenepsying the spatial patterns in road networks. Comput Environ Urban 34(1):40–48
Zhang QN (2004). Modeling structure and patterns in road network generalization. In: ICA workshop on generalization and multiple representation, Leicester
Acknowledgement
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Tian, J., Ai, T., Jia, X. (2012). Graph Based Recognition of Grid Pattern in Street Networks. In: Yeh, A., Shi, W., Leung, Y., Zhou, C. (eds) Advances in Spatial Data Handling and GIS. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25926-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-25926-5_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25925-8
Online ISBN: 978-3-642-25926-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)