Modeling Spatial and Spatio-temporal Co-occurrence Patterns

1. Modeling Spatial and Spatio-temporal Co-occurrence Patterns Mete Celik Spatial Database / Data Mining Group Department of Computer Science University of Minnesota mcelik@cs.umn.edu Advisor: Shashi Shekhar

2. MDCOP Motivating Example : Input •Manpack stinger (2 Objects) • M1A1_tank (3 Objects) •M2_IFV (3 Objects) •Field_Marker (6 Objects) • T80_tank (2 Objects) • BRDM_AT5 (enemy) (1 Object) • BMP1 (1 Object)

3. MDCOP Motivating Example : Output •Manpack stinger (2 Objects) • M1A1_tank (3 Objects) •M2_IFV (3 Objects) •Field_Marker (6 Objects) • T80_tank (2 Objects) • BRDM_AT5 (enemy) (1 Object) • BMP1 (1 Object)

4. Real Dataset Description • Vehicle movement dataset • 15 time slots, x and y coordinates are in meter • 22 distinct vehicle types and their instances • Minimum instance number 2, maximum instance number 78 • Average instance number 19 Output: Spatio-temporal Co-occurrence Pattern (Manpack_stinger <M1, M2> , fire cover (e,g., Bradley tank <T1, T2>)) Example Input from Spatio-temporal Dataset

5. Spatio-temporal Co-occurrence Pattern Taxonomy http://upload.wikimedia.org/wikipedia/en/c/cd/Original_distribution_of_wolf_subspecies.GIF Ecology – zonal co-location pattern Game (tactics) – mixed-drove pattern Emerging Infectious Diseases Sustained emerging co-occurrence patterns http://www.argentinapurses.com/football/formLabel.gif 1. Spatial co-location • Global and zonal co-location patterns, etc. 2. Co-occurrence patterns of moving objects • Flock pattern, mixed-drove pattern, follow pattern, moving clusters, etc. 3. Emerging or vanishing co-occurrence patterns • Emerging pattern: Interest measure getting stronger by the time • Vanishing pattern: Interest measure getting weaker by the time 4. Co-evolving patterns 5. Periodic co-occurrence patterns 6. Spatio-temporal cascade patterns . . . • ICDM05 - Discovering co-evolving spatio-temporal event sets • TKDE08 and ICDM06 - Mixed-Drove Spatio-Temporal Co-occurrence Pattern Mining • ICDE-STDM07 - Mining At Most Top-K% Mixed-drove Spatio-temporal Co-occurrence Patterns • ICDM07 – Zonal Co-location Pattern Mining • ICDM05 – Joinless Approach for Co-location Pattern Mining • ICTAI06 - Sustained Emerging Spatio-temporal Co-occurrence Pattern Mining

6. Chapter 2- Zonal Co-location Pattern Discovery Given: different object types of spatial events and zone boundaries Find : Co-located subset of event types specific to zones Method: A novel algorithm by using an indexing structure. 1 2 4 3 Zones 2,4 Zone 3

7. Chapter 4 - Sustained Emerging ST Co-occurrence Pattern Discovery • Given: A set P of Boolean ST object-types over a common ST framework • Find: Sustained emerging spatio-temporal co-occurrence patterns whose prevalence measure increase over time. • Method: Developing novel algorithms by defining monotonic interest measures.

8. Future Work – Short Term • Spatial co-location • Interest measure: participation index • Global and zonal co-location patterns, etc. • Co-occurrence patterns of moving objects • Flock pattern, mixed-drove pattern, follow pattern, cross pattern, moving clusters, etc. • Emerging or vanishing co-occurrence patterns • Emerging pattern: Interest measure getting stronger by the time • Vanishing pattern: Interest measure getting weaker by the time • Co-evolving patterns • Periodic co-occurrence patterns • Spatio-temporal cascade patterns • Efficient methods • Comparison of int. measures with statistical int. measures

9. Future Work – Long Term • Spatial and Spatio-temporal Pattern Mining Design • Crime Analysis, GIS, Epidemiology • Challenges • discovering patterns and anomalies from enormous frequently updated spatial and spatio-temporal datasets, • developing an ontological framework for spatial and spatio-temporal analysis, • integrating spatial and spatio-temporal data from multiple agencies, distributed data, and multi-scale data

10. Acknowledgements • Adviser: Prof. Shashi Shekhar • Committee: Prof. Jaideep Srivastava, Prof. Arindam Banerjee, and Prof. Sudipto Banerjee • Spatial Databases and Data Mining Group • TEC collaborators: James P. Rogers, James A. Shine • Dept. of Computer Science

11. References [1] J. Gudmundsson, M. v. Kreveld, and B. Speckmann, Efficient Detection of Motion Patterns in Spatio-Temporal Data Sets, ACM-GIS,250-257, 2004. [2] P. Laube and S. Imfeld, Analyzing relative motion within groups of trackable moving point objects, in In GIScience, number 2478 in Lecture notes in Computer Science. Berlin: Springer, pp. 132-144, 2002. [3] P. Kalnis, N. Mamoulis, and S. Bakiras, On Discovering Moving Clusters in Spatio-temporal Data, 9th Int'l Symp. on Spatial and Temporal Databases (SSTD), Angra dos Reis, Brazil, 2005. [4] Y. Huang, S. Shekhar, and H. Xiong, Discovering Co-location Patterns from Spatial Datasets: A General Approach, IEEE Trans. on Knowledge and Data Eng. (TKDE), vol. 16(12), pp. 1472-1485, 2004. [5] M. Hadjieleftheriou, G. Kollios, P. Bakalov, and V. J. Tsotras, Complex Spatio-Temporal Pattern Queries, VLDB, pp. 877-888, 2005. [6] C. du Mouza and P. Rigaux, Mobility Patterns, GeoInformatica, 9(4), 297-319, 2005. [7] J. S. Yoo and S. Shekhar, A Join-less Approach for Mining Spatial Co-location Patterns, IEEE Trans. on Knowledge and Data Eng. (TKDE), Vol.18, No.10, 2006.