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The geographical decision-making chain: formalization and application to maritime risk analysis
 

The geographical decision-making chain: formalization and application to maritime risk analysis

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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 ...

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.

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  • To achieve this integration of data mining into maritime surveillance systems, we formalized the use of geo-decision-making tools in the form of a chain (using the analogy of the decision-making chain).

The geographical decision-making chain: formalization and application to maritime risk analysis The geographical decision-making chain: formalization and application to maritime risk analysis Presentation Transcript

  • The geographical decision-making chain: formalization andapplication to maritime risk analysisCentre de recherche sur les Risques et les CrisesBilal IDIRI, Aldo NAPOLIMINES ParisTechCentre for Research on Risk and CrisisThe 6th International Workshop on Information Fusion and Geographic Information Systems (IF&GIS 2013)St Petersburg, Russia, May, 2013
  • • 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 7500tonnes in 2008 (Cedre, 2009)Context2• 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 contextA 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 implementedIntroduction > Proposition > Application > Conclusion
  • Maritime tracking systems3Definition: 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 surveillanceoperatorData acquisitioninfrastructure Improvement of these systems: Increase detection capabilities: new sensors (FMCW radar for small boats, waves of longrange surface) Integrate new databases to analyze weather, oceanographic context, etc. Integrate the decision aid tools to post-hoc analysis and identify risk behaviorsRisk behavior: movement (s) + conditions describing a risk• Movement: spatio-temporal (position change)• Conditions: vessel characteristics, environment, etc.Introduction > Proposition > Application > Conclusion
  • Problematic4 The maritime surveillance systems display operational data difficult to exploit fordecision-making These data are not saved to a post-hoc analysis Decision-making functions of collecting multi-source data to presentation tousers are not supported The problem of maritime surveillance can be seen as a problem ofspatiotemporal decision aid Operators should monitor ships as mobile objects moving in a spatiotemporalopen space and make decisions on risky behaviorsOperationalOperational Decision supportDecision supportDataData Immediate HistoricalDetailed AggregatedInternal to the system Multiple sourcesNormalised De-normalisedInterfaceInterface Complex IntuitiveQueriesQueries Predefined user queries Open-endedSlow response to aggregated queriesFrequent updates.Rapid response toaggregated queriesNo update.Introduction > Proposition > Application > Conclusion
  • Approaches to decision support5 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 dataIntroduction > Proposition > Application > Conclusion
  • Geo-decision-making chain6Spatial dataminingSpatial OnLineAnalyticalProcessing 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 allGeo-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
  • The post-hoc analysis of maritime risks7The data repository (or SDW) that records the evolution of maritime activity servesas a tool for automatic (SDM) data mining and visual (SOLAP) data mining that leadsto knowledge discovery on risk behaviors.Definition:Spatial Data Mining is the non-trivial extraction of implicit and potentially usefulknowledge from data supplied by the spatial database (Krzysztof et al., 1995)The Spatial OLAP has been defined by Bédard as, “a visual platform specificallydesigned to support a rapid and efficient spatiotemporal analysis through amultidimensional approach that includes mapping, graphical and tabular levels ofaggregation” (Bédard , 1997)Introduction > Proposition > Application > Conclusion
  • Application to the maritime8 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 > ConclusionThe databases
  • Spatial data mining9Eps 20Km ~ 0.18°MinPts 14 casRules linking vessel characteristics, the environment and thedifferent types of marine accidentsRules linking vessel characteristics, the environment and thedifferent types of marine accidentsDiscovery of accident-prone areasDiscovery of accident-prone areasIdentification of behaviors do not like the behavior of nearbyvesselsIdentification of behaviors do not like the behavior of nearbyvesselsIntroduction > Proposition > Application > Conclusion
  • Spatial OnLine Analytical Processing10T. LASVENES and V. BENEDETTI, 2012Line identification of dangerous goods by visual data miningLine identification of dangerous goods by visual data miningIntroduction > Proposition > Application > Conclusion
  • Conclusion11 Results Formalize a paradigm that we call geo-decision-making chain Apply the geo-decision-making chain to the domain of maritime surveillancethat supports all decision-making functions, from data collection to itspresentation to decision-makers Automatic and visual extraction of risky behaviors from dataIntroduction > Proposition > Application > Conclusion Future works Apply other data mining algorithms (periodic patterns, convoy, etc.) toextract risky behaviors Design and develop a software workshop integrating automatic methodsfor data mining and visual aid for analysis and subsequent identification ofrisk behaviors
  • Centre de recherche sur les Risques et les CrisesThanks for attentionPersonal page and publications http://www.mines-paristech.fr/Services/Annuaire/aldo-napoliAldo NAPOLIPhone: +33 (0) 4 93 67 89 15 Fax. : +33 (0) 4 93 95 75 81E-mail: : aldo.napoli@mines-paristech.fr?