Inferring human mobility using communication patterns

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Palchykov, Vasyl
dc.contributor.author Mitrovic, Marija
dc.contributor.author Jo, Hang-Hyun
dc.contributor.author Saramäki, Jari
dc.contributor.author Pan, Raj Kumar
dc.date.accessioned 2017-05-11T09:05:00Z
dc.date.available 2017-05-11T09:05:00Z
dc.date.issued 2014
dc.identifier.citation Palchykov , V , Mitrovic , M , Jo , H-H , Saramäki , J & Pan , R K 2014 , ' Inferring human mobility using communication patterns ' SCIENTIFIC REPORTS , vol 4 , 6174 , pp. 1-6 . DOI: 10.1038/srep06174 en
dc.identifier.issn 2045-2322
dc.identifier.other PURE UUID: b491459b-4477-4abb-823e-92a40b38ff80
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/inferring-human-mobility-using-communication-patterns(b491459b-4477-4abb-823e-92a40b38ff80).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/12913864/srep06174.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25809
dc.description.abstract Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data alone. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call data is required, the model helps to avoid potential privacy problems. en
dc.format.extent 1-6
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries SCIENTIFIC REPORTS en
dc.relation.ispartofseries Volume 4 en
dc.rights openAccess en
dc.title Inferring human mobility using communication patterns en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science en
dc.identifier.urn URN:NBN:fi:aalto-201705114184
dc.identifier.doi 10.1038/srep06174
dc.type.version publishedVersion


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