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Predictive Analytics – Human mobility patterns investigation from social networks
Fernando Santa
Universidade Nova de Lisboa
1. Kuwahara,M.,&Tanaka,S.(2008).Urbantransportdatafusionandadvancedtrafficmanagementforsustainablemobility.InY.Sadahiro(Ed.),Spa$aldatainfrastructureforurbanregenera$on(pp.75–102)
2. Pan,G.,Qi,G.,Zhang,W.,Li,S.,Wu,Z.,&Yang,L.(2013).Traceanalysisandminingforsmartci7es:issues,methods,andapplica7ons.IEEECommunica$onsMagazine,121
ReferencesConsorCum
ThecontributorsgratefullyacknowledgefundingfromtheEuropeanUnionthroughtheGEO-Cproject(H2020-MSCA-ITN-2014,GrantAgreementNumber642332,
hqp://www.geo-c.eu/).
Acknowledgements
Context
ChallengesWhat kind of methodologies are adequate to standardize
mobility information coming from different sources?
Source:Panetal.(2013)
How can mobility data from different sources be integrated to find patterns of human mobility?
Source:Kuwahara&Tanaka(2008)
What types of methods are optimal to identify spatio–temporal patterns of human mobility?
Source:Kuwahara&Tanaka(2008)
AcConsData sources Methods
Software
ResultsCollected data “Spatio–temporal point patterns analysis of
geolocated tweets to characterise urban dynamics”
• Develop models to analyse origin – destination data from Lisbon’s metro.
• Develop models to analyse trajectories based on call detail records.
• Evaluate alternatives to make data integration.
Human mobility: dimensions, aggregation levels, spatial scales, and models”
ScalingUp Impact
Source:Panetal.(2013)
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