Maritime traffic monitoring needs tools for spatiotemporal decision support. The operators responsible (e.g. the Coast Guard) must monitor vessels that are represented as objects moving in space and time. Operators use maritime tracking systems to follow the evolution of traffic and make decisions about the risks of a situation. These systems are based on Geographic Information Systems (GIS) and OnLine Transaction Processing (OLTP) approaches, which are prohibitively expensive, very slow and produce operational data unsuited to decision-making. Instead, operators require summarized data that is easier for them to produce and use. Therefore, we propose the definition of a geographical decision-making chain that adds a decision-making dimension to current systems. It consists of a carefully assembled set of tools that can automate the three phases of Business Intelligence, namely data loading, modelling and analysis.

1. The geographical decision-making chain: formalization and
application to maritime risk analysis
Centre de recherche sur les Risques et les Crises
Bilal IDIRI, Aldo NAPOLI
MINES ParisTech
Centre for Research on Risk and Crisis
The 6th International Workshop on Information Fusion and Geographic Information Systems (IF&GIS 2013)
St Petersburg, Russia, May, 2013

2. • Worldwide, there are still many thousands of maritime accidents each year,
• 445 acts of piracy recorded (+8.5% in one year) and 1181 marine taken hostages in 2010 (BMI, 2010),
• 54 700 tonnes of oil and hazardous substances accidentally discharged in 2009 against 7500
tonnes in 2008 (Cedre, 2009)
Context
2
• 90% of international trade,
• 80% of energy transport,
• 1.19 billion Deadweight tons (dwt) in 2009, 6.7% more compared to 2008 (CNUCED, 2009).
 Maritime activity: risks generator context
A risk is the eventuality of event that may cause harmful effects (Boisson, 1998)
• Risks related to safety: collision, grounding, etc.
• Risks related to security: piracy, illicit goods, etc.
 The need to use means of monitoring, control and repression
• Organisms responsible for safety and security,
• Regulations (ENC, legislative packages Erika I, II, III, etc.),
• Systems of navigational aid (NavTrack, Marine GIS, ex-Trem, etc..),
• Maritime tracking systems (Spationav, SIVE, SYTAR, etc..)
 The importance of maritime activity
 Risk always important despite means implemented
Introduction > Proposition > Application > Conclusion

3. Maritime tracking systems
3
Definition: they allow the retrieval and fusion of information on vessels (position,
heading, speed, etc.) for monitoring traffic on a display device (screen, touch table,
etc.) .
Source (DenisGouin, 2010)
Control interface Maritime surveillance
operator
Data acquisition
infrastructure
 Improvement of these systems:
 Increase detection capabilities: new sensors (FMCW radar for small boats, waves of long
range surface)
 Integrate new databases to analyze weather, oceanographic context, etc.
 Integrate the decision aid tools to post-hoc analysis and identify risk behaviors
Risk behavior: movement (s) + conditions describing a risk
• Movement: spatio-temporal (position change)
• Conditions: vessel characteristics, environment, etc.
Introduction > Proposition > Application > Conclusion

4. Problematic
4
 The maritime surveillance systems display operational data difficult to exploit for
decision-making
 These data are not saved to a post-hoc analysis
 Decision-making functions of collecting multi-source data to presentation to
users are not supported
 The problem of maritime surveillance can be seen as a problem of
spatiotemporal decision aid
 Operators should monitor ships as mobile objects moving in a spatiotemporal
open space and make decisions on risky behaviors
OperationalOperational Decision supportDecision support
DataData Immediate Historical
Detailed Aggregated
Internal to the system Multiple sources
Normalised De-normalised
InterfaceInterface Complex Intuitive
QueriesQueries Predefined user queries Open-ended
Slow response to aggregated queries
Frequent updates.
Rapid response to
aggregated queries
No update.
Introduction > Proposition > Application > Conclusion

5. Approaches to decision support
5
 Many ways to improve decision support in the maritime domain
 Approaches based on advanced spatial analysis (Claramunt et al., 2007)
 Knowledge representation (Vandecasteele et al.,2012)
 Automatic risk identification (Idiri et al., 2012)
 Knowledge extraction about risk behavior based on historical data
Introduction > Proposition > Application > Conclusion

6. Geo-decision-making chain
6
Spatial data
mining
Spatial OnLine
Analytical
Processing
 Formalize the use of geo-decision-making tools in the form of a chain
(using the analogy of the decision-making chain) with the aim of support all
Geo-decision-making functions
 Spatial data mining and Spatial OnLine Analytical Processing allows the extraction of:
 Patterns (spatial and temporal unusual frequent, periodic, groups, etc.)
 Relationships (association rules, sequential, etc.).
Introduction > Proposition > Application > Conclusion

7. The post-hoc analysis of maritime risks
7
The data repository (or SDW) that records the evolution of maritime activity serves
as a tool for automatic (SDM) data mining and visual (SOLAP) data mining that leads
to knowledge discovery on risk behaviors.
Definition:
Spatial Data Mining is the non-trivial extraction of implicit and potentially useful
knowledge from data supplied by the spatial database (Krzysztof et al., 1995)
The Spatial OLAP has been defined by Bédard as, “a visual platform specifically
designed to support a rapid and efficient spatiotemporal analysis through a
multidimensional approach that includes mapping, graphical and tabular levels of
aggregation” (Bédard , 1997)
Introduction > Proposition > Application > Conclusion

8. Application to the maritime
8
 MAIB data:
 Historical accidents/incidents between 1991 and 2009,
 14,900 accidents and incidents,
 16,230 ships.
 MERRA data:
 Provides meteorological data from the period 1991-2009,
 Regular download (1 time/day) to supply the facts.
 AIS data:
 Historical data since ~ 4 months,
 Continuous flow of ship movements.
Introduction > Proposition > Application > Conclusion
The databases

9. Spatial data mining
9
Eps 20Km ~ 0.18°
MinPts 14 cas
Rules linking vessel characteristics, the environment and the
different types of marine accidents
Rules linking vessel characteristics, the environment and the
different types of marine accidents
Discovery of accident-prone areasDiscovery of accident-prone areas
Identification of behaviors do not like the behavior of nearby
vessels
Identification of behaviors do not like the behavior of nearby
vessels
Introduction > Proposition > Application > Conclusion

10. Spatial OnLine Analytical Processing
10
T. LASVENES and V. BENEDETTI, 2012
Line identification of dangerous goods by visual data miningLine identification of dangerous goods by visual data mining
Introduction > Proposition > Application > Conclusion

11. Conclusion
11
 Results
 Formalize a paradigm that we call geo-decision-making chain
 Apply the geo-decision-making chain to the domain of maritime surveillance
that supports all decision-making functions, from data collection to its
presentation to decision-makers
 Automatic and visual extraction of risky behaviors from data
Introduction > Proposition > Application > Conclusion
 Future works
 Apply other data mining algorithms (periodic patterns, convoy, etc.) to
extract risky behaviors
 Design and develop a software workshop integrating automatic methods
for data mining and visual aid for analysis and subsequent identification of
risk behaviors

12. Centre de recherche sur les Risques et les Crises
Thanks for attention
Personal page and publications http://www.mines-paristech.fr/Services/Annuaire/aldo-napoli
Aldo NAPOLI
Phone: +33 (0) 4 93 67 89 15 Fax. : +33 (0) 4 93 95 75 81
E-mail: : aldo.napoli@mines-paristech.fr
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