Abstract
Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudinal data. The performance of the model for known clusters is tested in terms of how well it estimates the regression parameters and how well it captures the true spatial process. These results are presented and compared with the conditional auto-regressive model which is the most frequently used spatial model. In the simulation study, the proposed generalized estimating equations approach yields better results than the popular conditional auto-regressive model from the both perspectives of parameter estimation and spatial process capturing. A real life data on the vitamin A supplement coverage among postpartum women in Bangladesh is then analyzed for demonstration of the method. The existing divisional clustering behavior of vitamin A supplement coverage in Bangladesh is identified more accurately by the proposed approach than that by the conditional auto-regressive model.
Highlights
Most development indicators of any country are measured and reported at national scale
Looking at the values of SE(β1), it is seen that the efficiency of the estimated parameter is lower for conditional auto-regressive (CAR) model relative to that observed for the proposed method
Sites within a cluster are dependent but between clusters they are independent, sites which are in the border of a cluster are not dependent on a site which belongs to another cluster even though they are neighbors
Summary
Most development indicators of any country are measured and reported at national scale. Limited attention to urban versus rural differences is apparent, local-area variations have not been paid so much attention to. For monitoring and evaluation purpose, planners often require regionally disaggregated indicators. These, too, are expected to be smoothed for the spatial effect because geographic or administrative regions are, in reality, not independent of its nearest neighbors. Spatial thinking and the use of regionally disaggregated data in the geological and physical sciences have grown rapidly, their implementation in the development research has lagged. In recent years, has become very popular due to a rapid development of current knowledge regarding innovations in geospatial data, spatial statistical methods, the integration of data and models and the advances in technology
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