Learning Semantic Relationships of Geographical Areas Based on Trajectories

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dc.contributor.advisorPapangelis, Emmanouil
dc.contributor.authorMehmood, Saim
dc.date.accessioned2020-08-11T12:42:07Z
dc.date.available2020-08-11T12:42:07Z
dc.date.copyright2020-03
dc.date.issued2020-08-11
dc.date.updated2020-08-11T12:42:06Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractMining trajectory data to find interesting patterns is of increasing research interest due to a broad range of useful applications, including analysis of transportation systems, location-based social networks, and crowd behavior. The primary focus of this research is to leverage the abundance of trajectory data to automatically and accurately learn latent semantic relationships between different geographical areas (e.g., semantically correlated neighborhoods of a city) as revealed by patterns of moving objects over time. While previous studies have utilized trajectories for this type of analysis at the level of a single geographical area, the results cannot be easily generalized to inform comparative analysis of different geographical areas. In this work, we study this problem systematically. First, we present a method that utilizes trajectories to learn low-dimensional representations of geographical areas in an embedded space. Then, we develop a statistical method that allows to quantify the degree to which real trajectories deviate from a theoretical null model. The method allows to (a) distinguish geographical proximity to semantic proximity, and (b) inform a comparative analysis of two (or more) models obtained by trajectories defined on different geographical areas. This deep analysis can improve readers understanding of how space is perceived by individuals and inform better decisions of urban planning. Our experimental evaluation aims to demonstrate the effectiveness and usefulness of the proposed statistical method in two large-scale real-world data sets coming from the New York City and the city of Porto, Portugal, respectively. The methods we present are generic and can be utilized to inform a number of useful applications, ranging from location-based services, such as point-of-interest recommendations, to finding semantic relationships between different cities.
dc.identifier.urihttp://hdl.handle.net/10315/37711
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectGeographic information science
dc.subject.keywordsTrajectory data mining
dc.subject.keywordsNetwork representation learning
dc.subject.keywordsSpatial databases
dc.subject.keywordsMachine learning
dc.subject.keywordsStatistical inference
dc.titleLearning Semantic Relationships of Geographical Areas Based on Trajectories
dc.typeElectronic Thesis or Dissertation

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