Abstract
BACKGROUND: Robust household sampling, commonly applied for population-based investigations, requires sampling frames or household lists to minimize selection bias. We have applied Google Earth Pro satellite imagery to constitute structure-based sampling frames at sites in Pikine, Senegal; Pietermaritzburg, South Africa; and Wad-Medani, Sudan. Here we present our experiences in using this approach and findings from assessing its applicability by determining positional accuracy. METHODS: Printouts of satellite imagery combined with Global Positioning System receivers were used to locate and to verify the locations of sample structures (simple random selection; weighted-stratified sampling). Positional accuracy was assessed by study site and administrative subareas by calculating normalized distances (meters) between coordinates taken from the sampling frame and on the ground using receivers. A higher accuracy in conjunction with smaller distances was assumed. Kruskal-Wallis and Dunn multiple pairwise comparisons were performed to evaluate positional accuracy by setting and by individual surveyor in Pietermaritzburg. RESULTS: The median normalized distances and interquartile ranges were 0.05 and 0.03-0.08 in Pikine, 0.09 and 0.05-0.19 in Pietermaritzburg, and 0.05 and 0.00-0.10 in Wad-Medani, respectively. Root mean square errors were 0.08 in Pikine, 0.42 in Pietermaritzburg, and 0.17 in Wad-Medani. Kruskal-Wallis and Dunn comparisons indicated significant differences by low- and high-density setting and interviewers who performed the presented approach with high accuracy compared to interviewers with poor accuracy. CONCLUSIONS: The geospatial approach presented minimizes systematic errors and increases robustness and representativeness of a sample. However, the findings imply that this approach may not be applicable at all sites and settings; its success also depends on skills of surveyors working with aerial data. Methodological modifications are required, especially for resource-challenged sites that may be affected by constraints in data availability and area size.