Finding similar neighborhoods across cities by mining human urban activity

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Mathioudakis, Michael
dc.contributor.author Le Falher, Géraud
dc.date.accessioned 2014-08-29T06:59:15Z
dc.date.available 2014-08-29T06:59:15Z
dc.date.issued 2014-08-21
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/13900
dc.description.abstract We propose a method to match similar neighborhoods across different cities. That is, we give ourselves a measure of similarity between urban regions, as well as one region in one city. Our goal is then to find the region in some other cities which minimize the distance with the query region. Furthermore, we seek to do it efficiently, as it is prohibitive to evaluate the distance of all possible candidate regions. First, we collect trace of activities in 20 European and American cities from location aware social platforms Foursquare and Flickr. A thorough exploration of this dataset leads us to describe individual venues by relevant features including their aggregate activity across time, their visitors and overall popularity, and the typology of their surrounding. Then we learned several measures of venue similarity in a semi-supervised setting and evaluate their performance on two information retrieval tasks. After gathering human ground truth about neighborhoods, we evaluate different metrics between sets of venues and find out that Earth Mover’s Distance is best suited at assessing neighborhood similarity. Finally, we address the computational efficiency problem of finding the most similar neighborhood given a query. We devise a heuristic search strategy and show that it provides results of comparable quality while being orders of magnitude faster. This work has application in touristic recommendation and urban planning, as it provides a similarity measure between urban areas. en
dc.format.extent 52+6
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Finding similar neighborhoods across cities by mining human urban activity en
dc.type G2 Pro gradu, diplomityö en
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword smart cities en
dc.subject.keyword metric learning en
dc.subject.keyword clustering en
dc.subject.keyword geolocation en
dc.subject.keyword neighborhood en
dc.subject.keyword urban computing en
dc.identifier.urn URN:NBN:fi:aalto-201408292551
dc.programme.major Machine Learning and Data Mining fi
dc.programme.mcode SCI3015 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Gionis, Aristides
dc.programme Master’s Programme in Machine Learning and Data Mining (Macadamia) fi


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