Spatial Data Mining allows users to extract implicit but valuable knowledge from spatial related data. Two main approaches have been used in the literature. The first one applies simple Data Mining algorithms after a spatial pre-processing step. While the second one consists of developing specific algorithms that considers the spatial relations inside the mining process. In this work, we first present a study of existing Spatial Data Mining tools according to the implemented tasks and specific characteristics. Then, we illustrate a new open source Spatial Data Mining platform (EasySDM) that integrates both approaches (pre-processing and dynamic mining). It proposes a set of algorithms belonging to clustering, classification and association rule mining tasks. Moreover and more importantly, it allows geographic visualization of both the data and the results. Either via an internal map display or using any external Geographic Information System.