Key points are not available for this paper at this time.
There is an urgent need for more efficient and effective design, targeting and implementation of interventions to reduce regional imbalances in development. To do so, development agencies and practitioners need to articulate uneven regional development as regional inequalities in, and patterns of, development. The widespread popularity of composite indices like the Human Development Index has led to the acceptance of regional inequalities as a basis for intervention. However, in computing composite indices of development like the Human Development Index, information that could be of great utility to planners is lost. This is especially important when planners work on smaller spaces and several indicators of development. There is then a need to also articulate patterns of development for optimal intervention. Unfortunately, the conventional statistical methods to discern patterns in development are complex and have not found widespread acceptance like composite indices. Artificial intelligence, in particular the Kohonen Self-Organizing Map, is a user-friendly tool for development planners and practitioners to explore patterns in development. An application with several indicators over 399 Indian districts illustrates the need to study development patterns. This paper also makes clear the versatility of the Kohonen Self-Organizing Map technique in exploring these regional patterns of development.
Sivramkrishna et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: