Pattern discovery is an important task in time series mining. A pattern that occurs a significant num- ber of times in a time series is called a motif. Several approaches have been developed to discover motifs in time series. However, we can observe a clear gap in exploring the spatial-time series data. It is challenging to understand and characterize the meaning of the motif obtained concerning the data domain, comparing different approaches and analyzing the quality of the results obtained. We propose STMotif Explorer, a spatial-time motif analysis system that aims to interactively discover and visualize spatial-time motifs in different domains, offering insight to users. STMotif Explorer enables users to use and implement novel spatiotemporal motif detection techniques and then run this across various domains. Besides, STMotif Explorer offers the users a set of interactive resources where it is possible to visualize and analyze the discovered motifs and compare the results from different techniques. We show the features of our system with different approaches using real data.