Spatio-temporal sequential pattern mining (STSPM) plays an important role in many applications such as mobile health, criminology, social media, solar events, transportation, etc. Most of the current studies assume the data is located in a centralized database on which a single machine performs mining. Thus, the existing centralized algorithms are not suitable for the big data environment, where data cannot be handled by a single machine. In this paper, our main aim is to find out the Spatio-temporal sequential patterns from the event data set using a distributed framework suitable for mining big data. We proposed two distributed algorithms, namely, MR-STBFM (MapReduce based spatio-temporal breadth first miner), and MR-SPTreeSTBFM (MapReduce based sequential pattern tree spatio-temporal breadth first miner). These are the distributed algorithms for mining spatio-temporal sequential patterns using Hadoop MapReduce framework. A spatio-temporal tree structure is used in MR-SPTreeSTBFM for reducing the candidate generation cost. This is an extension to the proposed MR-STBFM algorithm. The tree structure significantly improves the performance of the proposed approach. Also, the top-most significant pattern approach has been proposed to mine the top-most significant sequential patterns. Experiments are conducted to evaluate the performance of the proposed algorithms on the Boston crime dataset.