Predictive Analytics Poster Print Size: Human mobility ...geo-c.eu/data/posters/POSTER_ESR10.pdf ·...
Transcript of Predictive Analytics Poster Print Size: Human mobility ...geo-c.eu/data/posters/POSTER_ESR10.pdf ·...
PosterPrintSize:ThispostertemplateissetupforA0interna7onalpapersizeof1189mmx841mm(46.8”highby33.1”wide).Itcanbeprintedat70.6%foranA1posterof841mmx594mm.
Placeholders:ThevariouselementsincludedinthisposterareonesweoNenseeinmedical,research,andscien7ficposters.Feelfreetoedit,move,add,anddeleteitems,orchangethelayouttosuityourneeds.Alwayscheckwithyourconferenceorganizerforspecificrequirements.
ImageQuality:Youcanplacedigitalphotosorlogoartinyourposterfilebyselec7ngtheInsert,Picturecommand,orbyusingstandardcopy&paste.Forbestresults,allgraphicelementsshouldbeatleast150-200pixelsperinchintheirfinalprintedsize.Forinstance,a1600x1200pixelphotowillusuallylookfineupto8“-10”wideonyourprintedposter.
Topreviewtheprintqualityofimages,selectamagnifica7onof100%whenpreviewingyourposter.Thiswillgiveyouagoodideaofwhatitwilllooklikeinprint.Ifyouarelayingoutalargeposterandusinghalf-scaledimensions,besuretopreviewyourgraphicsat200%toseethemattheirfinalprintedsize.
Pleasenotethatgraphicsfromwebsites(suchasthelogoonyourhospital'soruniversity'shomepage)willonlybe72dpiandnotsuitableforprin7ng.
[Thissidebarareadoesnotprint.]
ChangeColorTheme:Thistemplateisdesignedtousethebuilt-incolorthemesinthenewerversionsofPowerPoint.
Tochangethecolortheme,selecttheDesigntab,thenselecttheColorsdrop-downlist.
Thedefaultcolorthemeforthistemplateis“Office”,soyoucanalwaysreturntothataNertryingsomeofthealterna7ves.
Prin7ngYourPoster:Onceyourposterfileisready,visitwww.genigraphics.comtoorderahigh-quality,affordableposterprint.EveryorderreceivesafreedesignreviewandwecandeliveryasfastasnextbusinessdaywithintheUSandCanada.
Genigraphics®hasbeenproducingoutputfromPowerPoint®longerthananyoneintheindustry;da7ngbacktowhenwehelpedMicrosoN®designthePowerPointsoNware.
USandCanada:1-800-790-4001Interna7onal:+(1)913-441-1410Email:[email protected]
[Thissidebarareadoesnotprint.]
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)