We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.
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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).
Confilct of interests
The authors declare that they have no competing interests.
Electronic supplementary material
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Appendix A Appendix A is a .pdf file which shows that the random displacement of DHS cluster locations leads to a classical measurement error problem when a distance-based covariate is used.(PDF 84KB)
Appendix B Appendix B is a .txt file which provides R code to randomly displace DHS survey cluster locations based on the DHS Program geographic displacement procedure.(TXT 8KB)
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Warren, J.L., Perez-Heydrich, C., Burgert, C.R. et al. Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses. Spat Demogr 4, 155–173 (2016). https://doi.org/10.1007/s40980-015-0014-0
- Global Position System
- Mean Square Error
- Global Position System Data
- True Distance
- Random Displacement