Spatial scan statistic has been widely employed in spatial disease surveillance and spatial cluster detection. However, the over- dispersion and excess of zeros are often presented in real-world data, causing not only the violation of likelihood assumption for the Poisson model, but also excessive Type I error or false alarms. In this study , we propose the Bell scan and the zero-inflated Bell scan statistics which cover the over-dispersion and/or excess of zeros in the data. The proposed scan methods can be potentially applied to the event data in a simple way. Considering zero- inflated models, we compare the Bell, Poisson and binomial scan statistics based on relative risk bias, precision, recall of cluster detection, and power. By our simulations, we show that the Bell scan is a robust and powerful alternative in comparison with the traditional scan models. We finally illustrate the new methodology with two real data scan analyses. On the other hand, the spatial scan statistics based on the Poisson and binomial models rely on Monte-Carlo simulation and they are time-consuming to scan big maps. Hence, we propose some algorithms to detect irregular-shape clusters using Poisson, binomial and Bell models. Then we apply these algorithm on big maps. By simulation, we show that the irregular Bell scan is robust comparing classical models in detection of non-circular clusters. Finally, we find spatial clusters on a medical image.