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Inferring the home locations of Twitter users based on the spatiotemporal clustering of Twitter data
Author(s) -
Lin Jie,
Cromley Robert G.
Publication year - 2018
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12297
Subject(s) - cluster analysis , computer science , global positioning system , data mining , geography , support vector machine , cluster (spacecraft) , point (geometry) , position (finance) , function (biology) , point of interest , data science , information retrieval , machine learning , artificial intelligence , telecommunications , mathematics , business , geometry , finance , evolutionary biology , biology , programming language
Residential locations play an important role in understanding the form and function of urban systems. However, it is impossible to release this detailed information publicly, due to the issue of privacy. The rapid development of location‐based services and the prevalence of global position system (GPS)‐equipped devices provide an unprecedented opportunity to infer residential locations from user‐generated geographic information. This article compares different approaches for predicting Twitter users' home locations at a precise point level based on temporal and spatial features extracted from geo‐tagged tweets. Among the three deterministic approaches, the one that estimates the home location for each user by finding the weighted most frequently visited (WMFV) cluster of that user always provides the best performance when compared with the other two methods. The results of a fourth approach, based on the support vector machine (SVM), are severely affected by the threshold value for a cluster to be identified as the home.