Exploring human mobility patterns based on geotagged Flickr photos
Insinööritieteiden korkeakoulu | Master's thesis
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AbstractPredicting human mobility behaviour has long been a topic of scientiﬁc interest. Such studies generally rely on tracking human movements through a range of data collection methodologies such as using GPS trackers, cellular network data etc. Some of this data may be conﬁdential or hard to acquire. This thesis explores if existing publicly available data on online photo sharing platforms can be used to determine human mobility patterns with reasonable accuracy. We choose the Flickr website as the data collection medium as it has an extensive user base actively sharing photos many of which, have geo tags embedded in them which are preserved by Flickr. Our analysis reveals that while the data from Flickr is sparse and discontinuous making it unsuitable for reliable mobility prediction, typical human mobility trends based on time of day, day of week and month of the year can still be extracted. Such interesting patterns could be potentially used in traﬃc engineering domains or for user proﬁling purposes. More speciﬁcally, we describe how to obtain a subset of frequent active users and their information from Flickr, and the sliding window mechanism to ﬁlter the active periods of the users. Later we explain the various statistical methods applied on the ﬁltered subset of data to identify the categories in which users could be classiﬁed, mainly short distance travellers and long distance travellers. The short distance travellers are considered for mobility trends prediction.
Thesis advisorOtt, Jorg
pattern mining, movement patterns, micro-mobility, spatial-temporal analysis, Flickr geotagged images