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

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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)

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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)

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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

Example Input from Spatio-temporal DatasetOutput: Spatio-temporal Co-occurrence Pattern (Manpack_stinger <M1, M2> , fire cover (e,g., Bradley tank <T1, T2>))

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http://upload.wikimedia.org/wikipedia/en/c/cd/Original_distribution_of_wolf_subspecies.GIF

Ecology – zonal co-location pattern ICDM05 - Discovering co-evolving spatio-temporal event setshttp://www.argentinapurses.com/football/formLabel.gif

Game (tactics) – mixed-drove patternEmerging Infectious Diseases

Sustained emerging co-occurrence patterns

5. Periodic co-occurrence patterns

6. Spatio-temporal cascade patterns

. . .

2. Co-occurrence patterns of moving objects Flock pattern, mixed-drove pattern, follow pattern, moving clusters, etc.

Spatio-temporal Co-occurrence Pattern Taxonomy

1. Spatial co-location Global and zonal co-location patterns, etc.

ICDM07 – Zonal Co-location Pattern Mining ICDM05 – Joinless Approach for Co-location Pattern Mining

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

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

ICTAI06 - Sustained Emerging Spatio-temporal Co-occurrence Pattern Mining

4. Co-evolving patterns

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Chapter 2- Zonal Co-location Pattern Discovery

Zones 2,4 Zone 3

3

1 2

4

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.

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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.

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Future Work – Short Term

1. Spatial co-location Interest measure: participation index Global and zonal co-location patterns, etc.

2. Co-occurrence patterns of moving objects Flock pattern, mixed-drove pattern, follow

pattern, cross 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 patterns6. Spatio-temporal cascade patterns

• Efficient methods

• Comparison of int. measures with statistical int. measures

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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

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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

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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.