Laurent Etienne's presentation at Geomatics Atlantic 2012 (www.geomaticsatlantic.com) in Halifax, June 2012. More session details at http://lanyrd.com/2012/geomaticsatlantic2012/stbgx/ .

1. Spatio-Temporal Data Mining and
Classification of Ships' Trajectories
Laurent ETIENNE
PhD in geomatics
French Naval Academy Research Institute
Geographic Information Systems Group
Maritime Activity and Risk Investigation Network
Department of Industrial Engineering, Dalhousie University
laurent.etienne@ecole-navale.fr
Halifax, June 2012

2. Introduction
 Movement is an important part of life
 Mobile objects tracking systems
 Large spatio-temporal databases
 Knowledge Discovery from movement
 Real time analysis
 Decision support systems
 Different kind of mobile objects
 Different mobility data interest
 Ecology, Sociology, Transports,
Intelligence...
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3. Research interests
 Knowledge discovery from moving objects
databases (KDD)
 Algorithms for spatial data processing and
modelling
 Advanced visualisation
techniques for
spatial data
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4. Process overview
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5. Spatio-temporal data mining

Extract knowledge from a data warehouse

Cluster groups of trajectories

Main route followed by most trajectories of this group

Main trajectory

Spatial spreading (channel)

Temporal stretching (channel)

Metrics and rules to compare trajectories to main routes
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6. Trajectories comparison

Frechet distance and Dynamic Time Warping

Frechet : Minimise the max distance between pos

DTW : Minimise sum of distances between pos
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7. Group of Similar Trajectories

The model allows trajectories clustering using :

Distance (Fréchet, DTW...)

Density (T-OPTICS)

Zone Graph (Itinerary)
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8. Main trajectory

Median trajectory

Cluster positions (Normalized time, Frechet, DTW)

Compute aggregated median position (K-Mean)
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9. Statistical analysis

Statistical analysis of
points clusters distribution
(distance, time, heading...)

Boxplot visualisation
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10. Spatio-temporal pattern

Median trajectory and spatio-temporal channel

Cluster positions (Frechet matching)
with the main trajectory positions

Compute spatial and temporal
distance to the median position

Sort spatialy (left/right)

Sort temporaly (early/late)

Statistical selection 90%

Normality bounds

∆left / ∆right

∆early / ∆late
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11. Qualification Functional Process
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12. Qualify a Position
 Spatio-temporal channel
 Merge together spatial and temporal channel
 At each relative time of the median trajectory
 Normality bounds
 5 zones defined
 Qualify a position
 How to qualify a trajectory ?
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13. Similarity measurements
 Average, maximum and variability of
spatial/temporal distance between the
trajectory and the spatio-temporal channel (%)
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14. Fuzzy Logic
 Spatio-temporal similarity classification of a trajectory
compared to a pattern
 Using Fuzzy logic :
 Fuzzy sets learned by statistical analysis of
similarity measurements
 Fuzzy rules defined by experts and combining
similarity measurements
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15. Fuzzy Logic (Fuzzy sets)
 Use statistics of similarity measurements
 Min
 20%
 40%
 50%
 60%
 80%
 Max
 Define
fuzzy sets
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16. Fuzzy Logic (Fuzzification)
 Match a trajectory to the spatio-temporal
pattern (Frechet matching)
 Compute the similarity measurements
 Fuzzify similarity measurements
using fuzzy sets
 Value : 145
 75% Medium
 25% High
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17. Fuzzy Logic (Fuzzy Rules)
 Apply fuzzy rules using a fuzzy associative matrix
combining the fuzzified similarity measurements
 Fuzzy rules are activated at different degree of
truth depending on the membership of the similarity
measurements to fuzzy sets
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18. Fuzzy Logic (Defuzzification)
 How to get an human friendly similarity score
combining the similarity ratings measurements ?
 Defuzzify the fuzzy rules sets activated
 Using the « center of gravity » method
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19. Visualisation
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20. Visualisation of spatio-temporal data
 How to display spatio-temporal patterns and
qualified positions/trajectories ?
 3D
space/time
cube ?
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21. Visualisation (spatio-temporal patterns)
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22. Visualisation (2D analysis)
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23. Conclusion
 Model of trajectory, itineraries and matching tools
 General methodology
 Data mining : spatio-temporal patterns
 Position and trajectory classification using fuzzy logic
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24. Future work

Improve statistics analysis (skewness/kurtosis)

Detect multimodal groups of trajectories

Investigate patterns generalization (aggregation ?)

Consider more similarity measurements (heading,
speed)

Extend to trajectories partial matching, data
streams, real time analysis

Improve geovisualisation of outliers

...
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25. Questions ?
 L. Etienne, T. Devogele, A. Bouju. In Shi, Goodchild, Lees & Leung
(Eds.) Advances in Geo-Spatial Information Science, Chap. Modeling
Space and Time, Spatio-temporal Trajectory Analysis of Mobile
Objects Following the same Itinerary. CRC Press, Taylor & Francis
Group, ISPRS Orange book series, ISBN 978-0-415-62093-2, pages
47-58, 2012. 25

26. Plateform programming
 PostgreSQL / PostGIS database
 Model & data integration
(60 Gb of raw AIS data frames from different sources, 6 month )
 PostGIS spatial functions & indexes
 PL/PgSQL, PL/C, PL/Java programming
 Java
 Spatio-temporal pattern extraction & similarity measurements
 Fuzzy logic
 Statistics
 Matlab
 Web
 PHP/HTML/JS/AJAX (Ajax Push Engine)
 GeoServer WFS/WMS Openlayers KML
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27. Related publications

Book chapters

T. Devogele, L. Etienne, C. Ray, and C. Claramunt. In C. Renso, S.
Spaccapietra & E. Zimányi (Eds.) Mobility Data: Modeling, Management,
and Understanding, Chapter Mobility Applications, Maritime Applications.
Cambridge press, to be published in 2012.

L. Etienne, T. Devogele, A. Bouju. In Shi, Goodchild, Lees & Leung (Eds.)
Advances in Geo-Spatial Information Science, Chap. Modeling Space and
Time, Spatio-temporal Trajectory Analysis of Mobile Objects Following the
same Itinerary CRC Press, Taylor & Francis Group, ISPRS Orange book
series, ISBN 978-0-415-62093-2, pages 47-58, 2012.
27

28. Related publications

International conferences

L. Etienne, C. Ray, and G. Mcardle. Spatio-temporal visualisation of
outliers. Proceedings of the international workshop on Maritime Anomaly
Detection (MAD), pages 119–120, 2011.

L. Etienne, T. Devogele, and A. Bouju. Spatio-temporal trajectory analysis
of mobile objects following the same itinerary. Proceedings of the
International Symposium on Spatial Data Handling (SDH), pages 86–91,
2010.

A. Lecuyer, J.M. Burkhardt, and L. Etienne. Feeling bumps and holes
without a haptic interface: the perception of pseudo-haptic textures.
Proceedings of the SIGCHI conference on Human factors in computing
systems, pages 239–246, 2004.
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29. Europe map
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30. Passenger ships
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31. Calais - Dovers
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32. Dover straits
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