Graph Based Recognition of Grid Pattern in Street Networks

Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

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.

Keywords

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.

Notes

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.

References

  1. Batagelj V, Mrvar A (2008) Pajek: Program for analysis and visualization of large networks. Version 1.22Google Scholar
  2. Diestel R (2006) Graph theory, 3rd edn. Springer, BerlinGoogle Scholar
  3. 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 233–253CrossRefGoogle Scholar
  4. 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, SpainGoogle Scholar
  5. 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 253–267Google Scholar
  6. Iglin S (2003). grTheory-graph theory toolbox, http://www.mathworks.com/matlabcentral/fileexchange/4266. Accessed 30 Sep 2009
  7. Ip H, Wong WH (1997) Detecting perceptually parallel curves: criteria and force-friven optimization. Comput Vis Image Und 68(2):190–208CrossRefGoogle Scholar
  8. Jiang B, Claramunt C (2004) A structural approach to the model generalization of urban street network. GeoInformatica 8(2):157–173CrossRefGoogle Scholar
  9. 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/CanadaGoogle Scholar
  10. 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 1–10Google Scholar
  11. Marshall S (2005) Streets and patterns. Spon Press, New YorkGoogle Scholar
  12. Porta S, Crucitti P, Latora V (2006) The network analysis of urban streets: a dual approach. Physica A 369:853–866CrossRefGoogle Scholar
  13. Sarkar S, Boyer L (1994) A computational structure for preattentive perceptual organization: graphical enumeration and voting methods. IEEE Trans Man Cybern 24(2):246–266CrossRefGoogle Scholar
  14. Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Elsevier, AmsterdamGoogle Scholar
  15. 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, OttawaGoogle Scholar
  16. Wang JY et al (1993) Principles of cartographic generalization. Surveying and Mapping Press, BeijingGoogle Scholar
  17. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(4):440–442CrossRefGoogle Scholar
  18. Xie F, Levinson D (2007) Measuring the structure of road networks. Geogr Anal 39(3):336–356CrossRefGoogle Scholar
  19. Yang BS, Luan XC, Li QQ (2010) An adaptive method for idenepsying the spatial patterns in road networks. Comput Environ Urban 34(1):40–48CrossRefGoogle Scholar
  20. Zhang QN (2004). Modeling structure and patterns in road network generalization. In: ICA workshop on generalization and multiple representation, LeicesterGoogle Scholar

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

Personalised recommendations