Thanks to its visual coding Sociomapping engages our evolved skills for spatial orientation and movement detection, thus making the interpretation of complex data easy and accessible for everyone.
The sociomapping method was developed in 1993–1994 by R. Bahbouh as a tool that would facilitate understanding of data about social relations and help preventing conflicts within teams of military professionals.
The algorithm for data-transformation, developed by C. Höschl jr., is a dimensionality-reduction technique, such as PCA, and its goodness of fit can be measured by Spearman correlation between the map-distances and data-distances.
Sociomapping of small systems produces similar results to social network analysis with additional visualization features.
Besides the small systems analysis based on various relational data, Sociomapping can be used to visualize the profiles of unrelated subjects.
Data used for these type of maps are rectangular matrices, where for each subject there is a preference vector of selected objects (such as political parties, brands, products, and so on).
In this sense, Large systems Sociomapping is a data mining approach based on visual pattern recognition).