The problem of periodic spatio-temporal colored hotspot detection (PST-Col-Hotspot) takes the following input: (a) a spatio-temporal-color event framework \(\textbf{E}\) ; (b) a collection of events \(\textbf{A}\) (over \(\textbf{E}\) ), where each event is associated with a set of geo-spatial coordinates, a timestamp and a color. Colors are organized into a hierarchy, \(\mathcal{C}\mathcal{H}\) , with white being the root. Given the input, the goal of PST-Col-Hotspot problem is to determine spatial regions which show high “intensity” of events (of a particular color) at certain periodic intervals. The output of the PST-Col-Hotspot detection problem consists of the following: (a) a collection of spatial regions (which show high intensity of events), (b) their respective time intervals of high activity, periodicity values (e.g., daily, weekday-only, etc.), and color according to the given color hierarchy \(\mathcal{C}\mathcal{H}\) . PST-Col-Hotspot detection poses significant challenge for designing a suitable interest measure. The aim over here is to design a mathematical representation of a PST-Col-Hotspot such that it can differentiate interesting periodic patterns from trivial persistent patterns in the dataset. The current state of the art in the area of spatial and spatio-temporal hotspot detection focus on non-colored and non-periodic patterns. In contrast, our proposed approach is able to determine colored periodic hotspots. We experimentally evaluated our proposed algorithm using both real Azure traffic dataset (from Indian region) and synthetic dataset.