Spatial temporal mining is an important research area with many interesting topics. Most spatial temporal databases are updating incrementally with time. Some discovered topological patterns may be invalidated and some new topological patterns may be introduced by the evolution of databases. However, the existing static algorithms are usually inefficient and not feasible to maintain topological patterns in an incremental environment. In this paper, we develop an efficient algorithm, Inc-TMiner (Incremental Topology Miner) to incrementally maintain topological patterns in spatial-temporal databases. The experimental results indicate that Inc-TMiner significantly outperforms state-of-the-art algorithms in execution time and possesses graceful scalability.