Integrating Population Surveys using Spatial Visual Analytics: A Case Study on Nutrition and Health Indicators of Children under Five in India

Abstract

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density becomes challenging. Economizing this activity, multiple surveys with different goals are decentralized and implemented by various agencies. Some of the surveys tend to overlap in outcomes with spatial/temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating both surveys’ outcomes. Malnutrition in children under five is a pertinent global public health problem prevalent in India. Our work shows that such an integrated analysis is beneficial along with preliminary analyses of existing national surveys to find new insights while maintaining their autonomy.