Advertisement

Mining Co-location Patterns Between Network Spatial Phenomena

  • Jing Tian
  • Fu-quan Xiong
  • Fen YanEmail author
Chapter
Part of the Advances in Geographic Information Science book series (AGIS)

Abstract

The mining of co-location patterns is a popular issue in the field of spatial data mining. However, little attention has been paid to the co-location patterns between network spatial phenomena. This paper addresses this issue by extending an existing method to mining the co-location patterns between network spatial phenomena. The approach consists of two stages: (1) defining a co-location model on a network space based on skeleton partitioning of a road network to have co-occurrence relationships; (2) computing statistical diagnostics for these co-occurrence relationships. Our method was then applied to a case study regarding the mining of co-location patterns of manufacturing firms in Shenzhen City, China. These co-location patterns were also analyzed qualitatively according to the three mechanisms derived from agglomeration economies. Our method was compared with the existing method and the differences were verified by the network cross K-function.

Keywords

Network spatial phenomena Co-location patterns Manufacturing firms Agglomeration Network cross K-function 

Notes

Acknowledgments

The authors greatly appreciate the helpful comments of two anonymous reviewers. The authors also want to thank Dr. Okabe and his group for providing the program package, SANET, which allowed the calculation of the network cross K-function in this research. The work presented in this paper was supported by National Science Foundation for Fostering Talents in Basic Research of the National Natural Science Foundation of China (Grant No. J1103409) and by Innovation and Entrepreneurship Training Project for College Students of Wuhan University (Grant No.S2014438).

References

  1. Amiti M (2005) Location of vertically linked industries: agglomeration versus comparative advantage. Eur Econ Rev 49(4):809–832CrossRefGoogle Scholar
  2. Arnia G, Espa G, Quah D (2008) A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empirical Economics 34(1):81–103CrossRefGoogle Scholar
  3. Batty M (2005) Network geography: relations, interactions, scaling and spatial processes in GIS. In: Unwin D, Fisher P(eds) Re-presenting geographical information systems. Wiley, Chichester, pp 149–170Google Scholar
  4. Bembenik R, Rybinski H (2009) FARICS: a method of mining spatial association rules and collocations using clustering and Delaunay diagrams. J Intell Inf Syst 33(1):41–64CrossRefGoogle Scholar
  5. Borruso G (2008) Network density estimation: A GIS approach for analysing point patterns in a network space. Trans GIS 12(3):377–402CrossRefGoogle Scholar
  6. General Administration of Quality Supervision, Inspection and Quarantine of The People’s Republic of China (2007) Industrial classification for national economic activities, GB/T 4754-2002. China Zhijian Publishing House, BeijingGoogle Scholar
  7. Guo L, Du SH, Haining R, Zhang LJ (2011) Global and local indicator of spatial association between points and polygons: a study of land use change. Int J Appl Earth Obs Geoinf. doi: 10.1016/j.jag.2011.11.003 Google Scholar
  8. Hu W (2008) Co-location pattern discovery. Encyclopedia of GIS. Springer, Berlin, p 2008Google Scholar
  9. Huang Y, Shekhar S, Xiong H (2004) Discovering co-location patterns from spatial data sets: a general approach. IEEE Trans Knowl Data Eng 16(12):1472–1485CrossRefGoogle Scholar
  10. Huang Y, Pei J, Xiong H (2006) Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(2):239–260CrossRefGoogle Scholar
  11. Koperski K, Han JW (1995) Discovery of spatial association rules in geographic information databases. In: 4th International symposium on large spatial databases. Maine, USA, pp 47–66Google Scholar
  12. Leslie TF, Kronenfeld BJ (2011) The co-location quotient: a new measure of spatial association between categorical subsets of points. Geogr Anal 43(3):306–326CrossRefGoogle Scholar
  13. Li DR, Li DY, Wang SL (2006) Spatial data mining theories and applications. Science Press, BeijingGoogle Scholar
  14. Liu XJ, Ai TH, Liu YL (2009) Road density analysis based on skeleton partitioning for road generalization. Geo-spatial Inf Sci 12(2):110–116CrossRefGoogle Scholar
  15. Liu XJ, Zhan FB, Ai TH (2010) Road selection based on Voronoi diagrams and ‘‘strokes’’ in map generalization. Int J Appl Earth Obs Geoinf 12(2):S194–S202CrossRefGoogle Scholar
  16. Lu YM, Chen XW (2007) On the false alarm of planar K-function when analyzing urban crime distributed along streets. Soc Sci Res 36(2):611–632CrossRefGoogle Scholar
  17. Monseny J, López R, Marsal E (2011) The mechanisms of agglomeration: evidence from the effect of inter-industry relations on the location of new firms. J Urban Econ 70(2–3):61–74CrossRefGoogle Scholar
  18. Okabe A, Yamada I (2001) The K-function method on a network and its computational implementation. Geogr Anal 33(3):271–290CrossRefGoogle Scholar
  19. Okabe A, Boots B, Sugihara K, Chiu SN (eds) (2000) Spatial tessellations: concepts and applications of voronoi diagrams. Wiley, New YorkGoogle Scholar
  20. Okabe A, Okunuki K, Shiode S (2006) SANET: A toolbox for spatial analysis on a network. Geogr Anal 38(1):57–66CrossRefGoogle Scholar
  21. Ruiz M, Lopez F, Paez A (2010) Testing for spatial association of qualitative data using symbolic dynamics. J Geogr Syst 12(3):281–309CrossRefGoogle Scholar
  22. Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: a summary of results. In: Jensen CS et al (eds) SSTD 2001, vol 2121, 2001. LNCS, pp 236–256Google Scholar
  23. Shiode S (2008) Analysis of a distribution of point events using the network-based quadrat method. Geogr Anal 40(4):380–400CrossRefGoogle Scholar
  24. Sierra R, Stephens R (2012) Exploratory analysis of the interrelation between co-located boolean spatial features using network graphs. Int J Geogr Inf Sci 26(3):441–468CrossRefGoogle Scholar
  25. Wang GH (2010) Research on knowledge spillover and the learning of enterprise in industrial clusters. Science Press, BeijingGoogle Scholar
  26. Wu XH (2010) Research on regional cluster of manufacturing in China. Economic Science Press, BeijingGoogle Scholar
  27. Xie ZX, Yan J (2008) Kernel density estimation of traffic accidents in a network space. Comput Environ Urban Syst 32(5):396–406CrossRefGoogle Scholar
  28. Yamada I, Thill J (2004) Comparison of planar and network K-functions in traffic accident analysis. J Transp Geogr 12(2):149–158CrossRefGoogle Scholar
  29. Zhang X, Mamoulis N, Cheung D, Chou Y (2004) Fast mining of spatial collocations. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, USA, pp 384–393Google Scholar
  30. Zhou WL (2010) The concentration, diffusion and policy decisions of manufacturing, evidence from guangdong province. Economic Science Press, BeijingGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

Personalised recommendations