Nowadays, an abundance of sensors are used to collect very large da-tasets containing spatial points which can be mined and analyzed to extract meaningful patterns. In this article, we focus on different techniques used to summarize and visualize 2D point clusters and discuss their relative strengths. This article focuses on patterns which describe the dispersion of data around a central tendency. These techniques are particularly beneficial for detecting out-liers and understanding the spatial density of point clusters.