Skip to main content

Co-location Patterns, Interestingness Measures

  • Reference work entry
  • First Online:
Encyclopedia of GIS

Synonyms

Association Measures; Co-location Patterns; Interestingness Measures; Selection Criteria; Significance Measures

Definition

Interestingness measures for spatial co-location patterns are needed to select from the set of all possible patterns those that are in some (quantitatively measurable) way, characteristic for the data under investigation, and, thus, possibly, provide useful information.

Ultimately, interestingness is a subjective matter, and it depends on the user’s interests, the application area, and the final goal of the spatial data analysis. However, there are properties that can be objectively defined, such that they can often be assumed as desirable. Typically, these properties are based on the frequencies of pattern instances in the data.

Spatial association rules, co-location patterns and co-location rules were introduced to address the problem of finding associations in spatial data, and in a more general level, they are applications of the problem of finding freq...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,599.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,999.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  • Agrawal R, Ramakrishnan S (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, Santiago, 12–15 Sept, pp 487–499

    Google Scholar 

  • Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman, Harlow

    Google Scholar 

  • Diggle PJ (1983) Statistical analysis of spatial point patterns. Mathematics in biology. Academic, London

    MATH  Google Scholar 

  • Huang Y, Xiong H, Shekhar S, Pei J (2003) Mining confident co-location rules without a support threshold In: Proceedings of the 2003 ACM symposium on applied computing (ACM SAC March, 2003), Melbourne, pp 497–501

    Google Scholar 

  • Koperski K, Han J (1995) Discovery of spatial association rules in geographic information databases. In: Proceedings of 4th international symposium on large spatial databases (SSD95), Portlane, pp 47–66

    Google Scholar 

  • Leino A, Mannila H, Pitkänen R (2003) Rule discovery and probabilistic modeling for onomastic data. In: Lavrac N, Gamberger D, Todorovski L, Blockeel H (eds) Knowledge discovery in databases: PKDD 2003. Lecture notes in artificial intelligence, vol 2838. Springer, Heidelberg, pp 291–302

    Chapter  Google Scholar 

  • Malerba D, Esposito F, Lisi FA (2001) Mining spatial association rules in census data. In: Proceedings of 4th international seminar on new techniques and technologies for statistics (NTTS 2001), Crete

    Google Scholar 

  • Mannila H, Toivonen H, Verkamo AI (1995) Discovering frequent episodes in sequences. In: First international conference on knowledge discovery and data mining (KDD’95, August), pp. 210–215, Montreal. AAAI Press

    Google Scholar 

  • Morimoto Y (2001) Mining frequent neighboring class sets in spatial databases. In: International proceedings of the 7th ACM SIGKDD conference on knowledge and discovery and data mining, San Francisco, pp 353–358

    Google Scholar 

  • Salmenkivi M (2006) Efficient mining of correlation patterns in spatial point data. In: Fürnkranz J, Scheffer T, Spiliopoulou M (eds) Knowledge discovery in databases: PKDD-06, Berlin, Proceedings. Lecture notes in computer science, vol 4213. Springer, Berlin, pp 359–370

    Google Scholar 

  • Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: a summary of results. In: Proceedings of 7th international symposium on advances in spatial and temporal databases (SSTD 2001), Redondo Beach

    Google Scholar 

  • Shekhar S, Ma X. GIS subsystem for a new approach to accessing road user charges

    Google Scholar 

  • Xiong H, Shekhar S, Huang Y, Kumar V, Ma X, Yoo JS (2004) A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proceedings of the fourth SIAM international conference on data mining (SDM04), Lake Buena Vista

    Google Scholar 

  • Zaki MJ (2002) Efficiently mining frequent trees in a forest. In: Proceedings of 8th ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton

    Google Scholar 

Recommended Reading

  • Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Disc 1(3):241–258

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Copyright information

© 2017 Springer International Publishing AG

About this entry

Cite this entry

Salmenkivi, M. (2017). Co-location Patterns, Interestingness Measures. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_153

Download citation

Publish with us

Policies and ethics