Discovering Spatial Co-location Patterns

1. Discovering Spatial Co-location Patterns Presented By: ReyhanehJeddi & shichaoyu (Group 21) CSci 5707, Principles of Database Systems, Fall 2013  11/26/2013 Relation with the course is chapter 28 (Data mining )

2. Overview • Introduction • spatial data mining • Association Rule • Co-location Miner Algorithm

3. Spatial Data mining is using the Data mining methods for spatial data and reaches some designs in data according to Geography  location, area and any same aspect. Spatial data mining methods : spatial OLAP and spatial data warehousing : Multi dimensional spatial databases Characterization of spatial objects : Compare data distinctive Spatial organization: Rules for city Spatial allocation and indicator : Arrange countries Spatial clustering :Bundling homes Similarity analysis in spatial databases : Similar area • Data mining is finding some methods in large data sets and using stored data from data warehouse to analyze and manage the data to reduce future problems.

4. Spatial data role • Analyzing level  connection and narrowing • Location role space’s phenomena • Spatial databases • large scale and datasets • Spread domain : Ecology, Society safety , Health issues, …. • Map’s images  Various time : 20 to 100 • Ecology Co_accident • Spatial design  Co_location pattern • Ecosystem data sets' spatial pattern : • Local co_location pattern • spatial co_location pattern

5. Example: Association Rule • Association Rule--- analyzing and predicting • An implication expression of the form X  Y, where X and Y are itemsets • Example: {Milk, Diaper}  {Beer} • Rule Evaluation Metrics • Support (s) • Fraction of transactions that contain both X and Y • Confidence (c) • Measures how often items in Y appear in transactions thatcontain X • Given a set of transactions T, the goal of association rule mining is to find all rules having • support ≥ minsupthreshold • confidence ≥ minconfthreshold

6. Limitations of Transactions on Spatial Data -Order sensitive transactions -Support and confidence are ill-defined -May under-count support for a pattern -May over-counter support - Transaction over space - a priori algorithm

7. Overview • Introduction • spatial data mining • Association Rule • Co-location Miner Algorithm

8. From Transactions to Neighborhoods • Transactions -discrete, Independent, disjoint • Neighborhoods -Continuous, Spatial related

9. An Event centric co-location model table instance 3/5 2/3 2/4 2/3 3/4 2/5 2/5 2/4 3/5

10. Illustration: Co-location Miner algorithm • Generate candidate co-locations • Participation indexes calculation • Co-location rule generation

11. Advantage to Other Mining Methods • Event centric co-location model • – Robust in face of overlapping neighborhoods • Co-location Miner algorithm • – Computational efficiency • – High confidence low prevalence co-location patterns • – Validity of inferences

12. references Book: • Introduction to Data Mining, By Pang-Ning Tan; Michael Steinbach; Vipin Kumar 6th Edition Articles : • http://edugi.uji.es/Bacao/Geospatial%20Data%20Mining.pdf • http://www.spatial.cs.umn.edu/paper_ps/sstd01.pdf • http://en.wikipedia.org/wiki/Data_mining • http://www.docstoc.com/docs/121010850/Spatial-Data-Mining---PowerPoint • http://www.spatial.cs.umn.edu/paper_ps/co-location.pdf Pictures: • http://www.spatial-accuracy.org/FromICCSA2008 • http://gcn.com/articles/2008/11/14/the-state-of-spatial-data.aspx • http://www.ec-gis.org/Workshops/7ec-gis/papers/html/gitis/gitis.htm • http://www.spatialdatamining.org/software • http://www.spatialdatamining.org/ • http://www.geocomputation.org/2000/GC059/Gc059.htm

13. THANK YOU