Application of Analytical Hierarchy Process (AHP) algorithm to income insecurity susceptibility mapping - A study in the district of Purulia, India

Multi-criteria based prediction models are gradually finding places in the social and economic sciences to assess, locate, and address the complicated socio-economic issues arising around the globe. The incidence of any such issues may be treated as an output of complex interactions between a range of variables linked with ambient physical, socio-cultural, economic as well as the political system. The income insecurity is associated with the malnutrition, economic inequality, poverty, and several other socioeconomic hazards. At present, several studies are aiming to develop the 'tools and techniques' of demarcating the areas with some degree of vulnerability to a particular socioeconomic hazard and to examine the internal functions of the interactive variables linked with the hazard. In the present study, we tried to apply the algorithm of Analytical Hierarchy Process (AHP) in demarcating the areas susceptible to income insecurity in the district of Purulia, which is a backward district in the state of West Bengal, India, in terms of the overall level of human development achieved so far. The training dataset for developing the AHP model is based on the available secondary data. The model has been validated by running the modeled algorithm on a test dataset and applying the correlation and test of significance between the model output and the collected primary field data. The present model uses fifteen variables and is applicable to most of the subsistence agro-economic systems in tropical areas. The modification of the range of the variables and addition or alteration of variables within the similar structural framework will allow the model getting befitted with the other social and economic systems. (c) 2017 Elsevier Ltd. All rights reserved.