Ayasdi technology is based on the ideas of Topological Data Analysis (TDA), and produces meaningful topological graphs across a multitude of data types (including mixed data types). Given a notion of similarity, Ayasdi will produce topological graphs which show the shape of data, generating insights into the underlying phenomena of the data set. TDA provides a method for studying numerous properties of point cloud data sets. 1. TDA permits novel ways of visualizing and compressing data sets, which facilitate understanding and monitoring of data. 2. TDA quickly identifies interrelationships among disparate data sets. 3. TDA can be conducted at various levels of resolution and scale. This supports rapid and interactive analysis of data. With these basic principles, the shapes within a topological data map generate insights into the data. This work is an implementation of the TDA theory. The data used in this work was simulated to represent moving objects, and we built neighborhood graphs that characterize the local relationships in the data. We will discuss additional graphs that can represent the underlying neighborhood graph in a more readily digested format.