Introduction. – Spatial sampling is increasingly used in health surveys as it provides a simple way torandomly select target populations on sites where reliable and complete data on the general populationare not available. However, the previously implemented protocols have been poorly detailed, makingreplication difficult or even impossible. To our knowledge, ours is the first document describing step-bystep an efficient spatial sampling method for health surveys. Our objective is to facilitate the rapidacquisition of the technical skills and know-how necessary for its deployment.Methods. – The spatial sampling design is based on the random generation of geocoded points in thestudy area. Afterwards, these points were projected on the satellite view of Google Earth ProTM softwareand the identified buildings were selected for field visits. A detailed formula of the number of pointsrequired, considering non-responses, is proposed. Density of buildings was determined by drawingcircles around points and by using a replacement strategy when interviewing was unachievable. Themethod was implemented for a cross-sectional study during the April-May 2016 period in Cotonou(Be´nin). The accuracy of the collected data was assessed by comparing them to those of the Cotonounational census.Result. – This approach does not require prior displacement in the study area and only 1% of identifiedbuildings with Google Earth ProTM were no longer extant. Most of the measurements resulting from thegeneral census were within the confidence intervals of those calculated with the sample data.Furthermore, the range of measurements resulting from the general census was similar to thosecalculated with the sample data. These include, for example, the proportion of the foreign population(unweighted 8.9%/weighted 9% versus 8.5% in census data), the proportion of adults over 17 years of age(56.7% versus 57% in census data), the proportion of households whose head is not educated(unweighted 21.9%/weighted 22.8% versus 21.1% in census data).