In IGG, the 2-D lattice of the SOM is gradually grown one node at a time as part of the self-organizing process. The resulting network structure will also represent both the clusters in the data and their topology, and unlike with other growing SOM methods, it is planar (i.e. drawable). These properties make it a useful tool for visualizing high-dimensional data. We have applied the method to visualizing word semantics and human genetics, leading to potentially useful insights in these domains.