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An imprecise region is referred to as a geographical area without aclearly-defined boundary in the literature. Previous clustering-basedapproaches exploit spatial information to find such regions. However, the priorstudies suffer from the following two problems: the subjectivity in selectingclustering parameters and the inclusion of a large portion of the undesirableregion (i.e., a large number of noise points). To overcome these problems, wepresent DIR-ST$^2$, a novel framework for delineating an imprecise region byiteratively performing density-based clustering, namely DBSCAN, along with notonly spatio--textual information but also temporal information on social media.Specifically, we aim at finding a proper radius of a circle used in theiterative DBSCAN process by gradually reducing the radius for each iteration inwhich the temporal information acquired from all resulting clusters areleveraged. Then, we propose an efficient and automated algorithm delineatingthe imprecise region via hierarchical clustering. Experiment results show thatby virtue of the significant noise reduction in the region, our DIR-ST$^2$method outperforms the state-of-the-art approach employing one-class supportvector machine in terms of the $\mathcal{F}_1$ score from comparison withprecisely-defined regions regarded as a ground truth, and returns apparentlybetter delineation of imprecise regions. The computational complexity ofDIR-ST$^2$ is also analytically and numerically shown.

DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio--Temporal--Textual Information