Estimation of missing values in aggregate level spatial data

BackgroundData can be missing when a survey fails to collect information from certain regions due feasibility issues, which impose problems while performing spatial analysis.ObjectiveThe present study aims estimate aggregate level public health data by utilizing the neighbouring and accounting for autocorrelation inherently in data.MethodologyData was simulated fixed values of various parameters regression models under low high scenarios dependent independent variables. In variable, 5%–25% were assumed missing. Stochastic imputation using namely lag model, error Durbin model X performed. The performance these also compared Annual Health Survey 2012-13.ResultsThe simulation analysis revealed that any amount data, irrespective whether other variables are spatially autocorrelated or not, if variable with is high, stochastic performed gives accurate estimates values. If low, addition three models, found effective estimating values.ConclusionThe proposed mechanism results optimal yield complete useful professionals interventions.