1. Bee-Path: An Experiment ofHuman MobilityM. Gutirrez-Roig, O. Sagarra, J. Perell, A. Daz-Guilera, J. Palmer, A. Oltra, F. Bartumeus 2. 12,000 visitors. Centric park in BarcelonaBarcelonaLab Citizen Science Office 3. Spatial distribution of activities of theScience Fair 4. Framing the Bee-Path experimentPanic situations,interactions betweenindividuals consideredIntercity and intracitymobility (mobile devices) 5. Mobility in animalsModelling animal dispersal along two perspectives: (a)Search/predator efficient strategies and (b) foreaging/migration.Spacial ecology and population redistribution. Codling, Plank,Benhamou, J. R. Soc. Interface (2008)1. Basic approach: Simple Random Walk (RW) or Brownianmotion2. Conditional Random Walk (CRW): Velocity is Markovian sothat there is a persistency. Viswathan et al, Phys Rev E(2005). Codling, Plank, Benhamou, J. R. Soc. Interface (2008)3. Byassed Random Walk (BRW): Mobility is oriented by anexternal force (generally not dependent in space)4. Ways of detecting orientation component. Benhamou,Ecology (2006). Exploring CBRW. 6. Bee-Pathwww.bee-path.netDo you want to know how you move through the activities of theFestival of Science and Technology? Your GPS allows us to know theway you move. We will send you a personalized view of your pathtrough the activities the festival. Be part of this citizen science project inwhich researchers from different disciplines are involved.Your participation is essential to make the experiment a success ! 7. From the book Viswathan, da Luz, Raposo, Stanley (eds) ThePhysics of Foreaging (Cambridge, 2011) 8. Saturday (11h-19h) and Sunday (11h-14h) in a public parkwith about 14 simultaneous activities.450 volunteers 9. We keep about 5,000 GPS valid records 10. 8 metersProportional GPS (6 meters)We get 640 flights and 403 stops 11. Lets propose a model to better understand mobilityand consider some models in the literature. 12. A sort of gravitational modelSize (size of the hotspots, 4 m) and depth (stopping time distribution)Two possible dynamicsConditional Brownian motion.Jumps distance exponentially distributedand angle deviation follows Von Misesdistribution BUT WE ADD POTENTIALWELLSCodling, Plank, Benhamou, J. R. Soc.Interface (2008) 13. Spatial distribution of StopsNearly coincides with empirical dataRadius is proportional to its duration 14. Spatial distribution of flightsNearly coincides with empirical data90%, 70% and 50% home range. Dots are Brownian Bridges 15. With a spatial dependent BRWDouble exponential. Largestabout 7 minutesModel does NOT capturelargest flights 16. With a Conditional Byassed Brownian motionDouble exponential. Shortesttimes not properly describedModel improves 17. Byassed Random WalkBy M. Gutirrez-Roig and O. Sagarra 18. Byassed Conditional Random WalkBy M. Gutirrez-Roig and O. Sagarra 19. Conclusions Experiment using citizen science practices ina context where data availability is scarce Model: Space dependent byassed randomwalk Need of combining byassed and conditionalrandom walk approach for reproducing data Flights (single) and stops (double) areexponentially distributed Tool for improving space distribution 20. In collaboration Domestic Data Streamers and Counterest for the Big Bang Dataexhibition in CCCB (Barcelona, 2014) 21. In collaboration Domestic Data Streamers and Counterest for the Big Bang Dataexhibition in CCCB (Barcelona, 2014) 22. M. Gutirrez-Roig, O. Sagarra,J. Perell, A. Daz-Guilera, J.Palmer, A. Oltra, F. Bartumeus2014, in preparationJosep PerellUniversitat de BarcelonaJosep.perello@ub.edu@josperello