Spatial Analysis of the Indian Subcontinent: the Complexity Investigated through Neural Networks

Giovanni Fusco (1) , Joan Perez (1, 2)

1 ESPACE - Études des Structures, des Processus d’Adaptation et des Changements de l’Espace
2 AU - Avignon Université

India is a very complex space for geographical analysis, above all when the focus of the research is on the rapid transformation of the Indian space, related to urbanization and socioeconomic development. This paper adopts an inductive approach using a database specifically conceived for describing the 640 administrative districts of India between 2001 and 2011. Neu-ral Networks SOM and superSOM approaches are used to cluster districts. Different model options will be presented and a few key points like the importance of prior variable clustering and robust initialization will be highlighted. These key points can be considered as essential prerequisites for any spatial analysis using Neural Networks. The results of the models show that the Indian space can be meaningfully segmented into a limited number of district profiles, corresponding to particular sub-spaces. Our results show a complex and heterogeneous country, with sub-spaces possessing logics of their own and far away from any cliché.

Mots clés: Spatial Clustering; Neural Networks; Self-Organizing Maps.; SOM; Socioeconomic Development; Urbanization; India

Domaines: Machine Learning [stat.ML]; Géographie

Giovanni Fusco, Joan Perez. Spatial Analysis of the Indian Subcontinent: the Complexity Investigated through Neural Networks. 14th International Conference on Computers in Urban Planning and Urban Management (CUPUM 2015), Jul 2015, Cambridge, MA, United States. pp.287. ⟨hal-01303057⟩