Spatially integrated estimator of finite population total by integrating data from two independent surveys using spatial information

Abstract

A major goal of survey sampling is finite population inference. In recent years, large-scale survey programs have encountered many practical challenges which include higher data collection cost, increasing non-response rate, increasing demand for disaggregated level statistics and desire for timely estimates. Data integration is a new field of research that provides a timely solution to these above-mentioned challenges by integrating data from multiple surveys. Now, it is possible to develop a framework that can efficiently combine information from several surveys to obtain more precise estimates of population parameters. In many surveys, parameters of interest are often spatial in nature, which means, the relationship between the study variable and covariates varies across all locations in the study area and this situation is referred as spatial non-stationarity. Hence, there is a need of a sampling methodology that can efficiently tackle this spatial non-stationarity problem and can be able to integrate this spatially referenced data to get more detailed information. In this study, a Geographically Weighted Spatially Integrated (GWSI) estimator of finite population total was developed by integrating data from two independent surveys using spatial information. The statistical properties of the proposed spatially integrated estimator were then evaluated empirically through a spatial simulation study. Three different spatial populations were generated having high spatial autocorrelation. The proposed spatially integrated estimator performed better than usual design-based estimator under all three populations. Furthermore, a Spatial Proportionate Bootstrap (SPB) method was developed for variance estimation of the proposed spatially integrated estimator.