Nothing Special   »   [go: up one dir, main page]

Skip to main content

A Study of the Application of Data Mining on the Spatial Landscape Allocation of Crime Hot Spots

  • Conference paper
Geo-Informatics in Resource Management and Sustainable Ecosystem

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 398))

Abstract

This study uses the crime hot spots announced by the Taipei police as an example. With the combination of the geographic information system and data mining technologies, it can effectively find out the association rules between the crime hot spots and spatial landscape, and the distance between them. This paper could provide information to the public-security organizations for enhanced patrol of the potential crime hot spots, but is also served as references of urban renewal. It reduces the crime hot spots by avoiding programming the spatial landscape of crime hot spots, therefore promoting safety and happiness of the society.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Hsu, Introduction to Police Administration. San Min Books, Taipei (1998)

    Google Scholar 

  • Hu, Yi: SQL Server Business Intelligence Bible. Xbook Marketing Co. Ltd., Taipei (2004)

    Google Scholar 

  • Hsieh, C.-Y., Chou, K.-P.: A Spatial Autocorrelation Analysis of Aging Distribution and Transition. Journal of Population Studies 25, 91–119 (2002)

    Google Scholar 

  • Huang, S.-M., Hsu, P.-Y., Chen, C.-C.: Use Spatial Data Mining for Planning Urban Mass Rapid Transit System. Journal of e-Business 10, 491–506 (2005)

    Google Scholar 

  • Tsou, M.-C., Sun, C.-H.: Mining Association Pattern from Spatial Database. Journal of Taiwan Geographic Information Science 3, 27–41 (2005)

    Google Scholar 

  • Tsou, M.-C., Sun, C.-H.: Discerning Chi-Chi Earthquake-induced Landslide Using Data Mining Technique. Journal of Taiwan Geographic Information Science 36, 117–131 (2004)

    Google Scholar 

  • Jung, C.-T., Sun, C.-H.: Spatial Data Mining on Census Data —A Case Study for Location Analysis of Convenience Stores in Taipei City

    Google Scholar 

  • Tseng, et al.: Data Mining. Flag Publishing Co. Ltd., Taipei (2005)

    Google Scholar 

  • Agrawal, R., Imielinske, T., Swami, A.: Mining Association Rules between Sets of Items in Large Database, pp. 207–216. ACM (1993)

    Google Scholar 

  • Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Database. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)

    Google Scholar 

  • Berry, M., Linoff, G.S.: Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley and Sons, New York (1997)

    Google Scholar 

  • Ester, M., Kriegel, H.-P., Sander, J.: Algorithms and applications for spatial data mining. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery. Taylor & Francis, New York (2001)

    Google Scholar 

  • Estivill-Castro, V., Lee, I.: Data Mining Techniques for Autonomous Exploration of Large Volumes of Geo-referenced Crime Data. In: Proceedings 6th International Conference on Geocomputaion (2001)

    Google Scholar 

  • Han, J., Kamber, M., Tung, A.K.H.: Spatial clustering methods in data mining. In: Miller, H.J., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery. Taylor & Francis, New York (2001)

    Google Scholar 

  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)

    Google Scholar 

  • Karasová, V., Krisp, J.M., Virrantaus, K.: Application of Spatial Association Rules for Improvement of a Risk Model for Fire and Rescue Services. In: Proceedings 10th Scandinavian Research Conference on Geographical Information Science, ScanGIS 2005 (2005)

    Google Scholar 

  • Shekhar, S., Chawla, S.: Introduction to Spatial DataMining. In: Shekhar, S., Chawla, S. (eds.) Spatial Databases: A tour. Prentice Hall, London (2003)

    Google Scholar 

  • Zhu, A.-X.: A personal construct-based knowledge acquisition process for natural resource mapping. In: Proceedings Geographical Information Science, vol. 13(2), pp. 119–141 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Huang, SM. (2013). A Study of the Application of Data Mining on the Spatial Landscape Allocation of Crime Hot Spots. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45025-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-45025-9_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45024-2

  • Online ISBN: 978-3-642-45025-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics