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Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
Data-in-the-Cloud City
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Data-in-the-Cloud City

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  • 1. Data-in-the-Cloud City Proactive Analysis of Digital Information about the city ! ! Gonzalo A. ARANDA-CORRAL Alejandro BLANCO-ESCUDERO Universidad de Huelva Yaco Sistemas Department of Information Technology alejandro.b.e@gmail.com gonzalo.aranda@dti.uhu.es Joaquín BORREGO-DÍAZ Manuel GOMAR-ACOSTA Universidad de Sevilla Elelog S.L. Dept. of Computer Science and AI mangomaco@gmail.com jborrego@us.es
  • 2. IndexMotivation & GoalsData in the WWW and associated servicesSimulating extreme dynamicsMultiagent ArchResultsConclusions and Future Work
  • 3. Motivation (I): Context eCompleXcityEmergent concepts in complex systems. Applicationsto Urban environments and Cultural ComplexityExcellence Project. Junta de Andalucía. Spain
  • 4. Motivation (II): Digital Information So c ia 0 lM 2. Marketing ed eb ia W Heterogeneity Different nature Architecture Urbanism Goal-driven Different information flows ns Lo tio Media Art c ¿Reusable? ic a at se ion B rv un ice ase s d m com le Te
  • 5. Motivation (III) Urban dynamics simulated from WWW data he nt r Multiagent Systems a i fo at d D (MAS) for simulating C lou an Complex Behaviour from U rb cs? mining WWW information na mi Dy Limits of MAS simulation from Data about cities
  • 6. City as a Complex SystemDifferent views: Data city Social Network city City as a ground of cyberinfrastructureLocal Interaction versusGlobal interaction !
  • 7. Emergent Research LineCollect and processdata for new applications, services, and planning Analysis of urban behaviourOpen Data initiativesfacilitate R&D initiatives
  • 8. Some questions...How are WWW data about acity?What about the quality?Are they useful?Can they be improved?
  • 9. pre-Digital Cities versusSmart Citiespre-Digital City: ¿Able to consume data?Smart City: To produce and consume its own data
  • 10. Data Flows I2U U2U Op ial ks Da en oc or S w ta N et y ilit rab U Info rban pe ero rm I2I Int atic s U2I
  • 11. Data flows about cities in WWW(I): Institutions to User (I2U) Essential to understand some urban process (dynamics) Historical data and analisys Main support of Opendata.
  • 12. Data flows about cities inWWW (II): User to user U2U (entre usuarios): P2P Mobile devices and Social Web Information quality.
  • 13. Data flows about cities inWWW (III): User to InstitutionsU2IStrong GrowthWeb 2.0 & Urban informatics
  • 14. Data flows about cities in WWW(IV): Institutions to Institutions Unavailable to users Goverment (& enterprises) interoperability Increasing
  • 15. Different data sources forMAS simulation Extreme Urban dynamics Explore every WWW information about both Urban evolution under the city and the event exceptional circunstances Data comsumption by MAS pre-Digital city: New Orleans Extreme dynamics: Katrina hurricane (2005)
  • 16. Why this event?First, Katrina is one of the There exists a big amountmost destructive of data source and Webhurricane suffered by a services associated (ordeveloped country, USA consumable by) Geographic InformationThe extent of damage Systems with publicinvites for a macroscopic accessanalysis of the incident
  • 17. Bounding the scopeIn order to evaluate the In some cases aquality, accessibility and reparation of defficentusefulness of I2U data is necessaryIt considers only I2U Mainly, data from globalaccessible by WWW, information systems (orInternet or deep Internet U.S. Agencies)( that is, accessible via more specific data maysearch forms) limit the reusability.
  • 18. Why MAS?MAS based simulationmethodology allows toestimate how affect dataquality to each module of I2U main flow for thisthe system: simulation Statistical results from surveys useful for agents- citizens behaviour.
  • 19. I2U about New Orleans,Katrina and its effects (I) U.S. Geological Survey (http://www.usgs.gov/) National Elevation Dataset (http://ned.usgs.gov/). Precisions ~ 3 meters Open Street Maps (OSM, http:// ! www.openstreetmap.org/),
  • 20. Geographical Area !Main Area Divided into 3 !
  • 21. Agents ModellingAgentification Process3 kind of agents Environment Water Citizens
  • 22. Environment agentsInformation about terrain Discretized in hexagonsUpdate water information(by WaterAgent request)Citizen Agents askinformation toenvironment agents
  • 23. Water agents (I) Potential energy: Reactive agent Direction Speed Unaffordable information River is initial agent state
  • 24. Water agents (II) Future; Buildings geometry ! ! http://sketchup.google.com/ Complemented by OSM.
  • 25. Citizen agents Papers about social Also design groups of behaviour in critical agents situations Based on published Fundamental to citizen information agents design MAS level Patterns of behaviour Evacuation paths from the surveys of survivors Group behaviour in panic situations Prevent riskies situations
  • 26. Visualization Based on OSM and Google Maps/Earth Some extra data: Disaster scope Survivors / zone etc... http://www.youtube.com/watch?v=pTKhrpl9jZc
  • 27. Population, demography, flooding
  • 28. ConclusionsI2U information flows is used, in this work, to simulateurban phenomenaSimulation needs digital information from cities and itsown feedbackPrevious and exhaustive information analysis andclasification its fundamental to start any kind of urbancloud computing
  • 29. Future WorkUse Complex Systemsmethodologies to analyse andto compare events andsimulationsTo detect emergentphenomena in digital cities bymeans of simulation and Datamining
  • 30. Data-in-the-Cloud City Proactive Analysis of Digital Information about the city ! ! Thanks for your attention

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