Spatiotemporal Data-Adaptive Clustering Algorithm: An Intelligent Computational Technique for City Big Data

Abstract

The emerging paradigm of the modern city demands big data analytics in urban studies. Data mining techniques for big data have the potential to ascertain strategic insights for current and future cities. Clustering is increasingly growing in data mining, serving as a knowledge discovery tool. This article proposes a spatiotemporal clustering method, called spatiotemporal data-adaptive clustering (STDAC), to cope with clustering challenges, such as feature type, parameter setting, and treating spatial and temporal dimensions as equals. The proposed algorithm takes a dual-structure approach to obtain high-quality clusters and discover temporal changes not detected by other techniques. STDAC refers to the feature type and uses k-fold cross-validation (KCV) to replace the user-defined parameters with the data-driven values. The data-driven threshold from KCV performs as the endogenous variable, thus not requiring a priori assumptions or parameter settings. This article used the bus, taxi passengers, and de facto population data as illustrative case studies. The clustering performances were evaluated based on validation indexes, such as the Davies–Bouldin index (DBI) and Dunn index (DI), where STDAC generally had lower DBI and greater DI. The findings showed that STDAC could yield better clustering performances than other established algorithms. Hence, the proposed method would be promising to future studies requiring spatiotemporal big data analytics in urban studies, administrative management, and other fields.