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Influence of Demographic and Health Survey Point Displacements on Point-in-Polygon Analyses


We use Demographic and Health Survey data to evaluate the impact of random spatial displacements on analyses that involve assigning covariate values from ancillary areal and point feature data. We introduce a method to determine the maximum probability covariate (MPC), and compare this to the naive covariate (NC) selection method with respect to obtaining the true covariate of interest. The MPC selection method outperforms the NC selection method by increasing the probability that the correct covariate is chosen. Proposed guidelines also address how characteristics of ancillary areal and point features contribute to uncertainty in covariate assignment.

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This research was supported in part by Grants from the National Institute of Environmental Health Sciences (T32ES007018, P30ES010126) and the United States Agency for International Development (USAID) through the MEASURE DHS project (Contract No. GPO-C-00-08-00008-00).

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The authors declare that they have no competing interests.

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Correspondence to Joshua L. Warren.

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{\bf Additional file 1 --- Appendix A} Appendix A is a .pdf file which shows how the maximum probability covariate can be determined for both problem types given only the displaced DHS cluster location. 121KB


{\bf Additional file 2 --- Appendix B} Appendix B is a .txt file containing R code which calculates the probability of each observed region for a given displaced DHS cluster location, allowing the researcher to determine the maximum probability covariate. 15.8KB


{\bf Additional file 3 --- Appendix C} Appendix C is a .txt file containing R code which calculates the probability of each possible point resource count within $v$ km of the true DHS cluster location for a given displaced DHS cluster location and value of $v$, allowing the researcher to determine the maximum probability covariate. 13.0KB

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Warren, J.L., Perez-Heydrich, C., Burgert, C.R. et al. Influence of Demographic and Health Survey Point Displacements on Point-in-Polygon Analyses. Spat Demogr 4, 117–133 (2016).

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  • Covariate error
  • Maximum probability covariate
  • Spatial uncertainty