A random spatial sampling method in a rural developing nation
1 United States Department of Agriculture—Forest Service, Northern Research Station, 100 North 20th St Suite 205, Philadelphia, PA 19103, USA
2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Blockley Hall, Philadelphia, PA 19104-6021, USA
3 Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, 141-2 Anatomy and Chemistry, 3620 Hamilton Walk, Philadelphia, PA 19104, USA
BMC Public Health 2014, 14:338 doi:10.1186/1471-2458-14-338Published: 10 April 2014
Nonrandom sampling of populations in developing nations has limitations and can inaccurately estimate health phenomena, especially among hard-to-reach populations such as rural residents. However, random sampling of rural populations in developing nations can be challenged by incomplete enumeration of the base population.
We describe a stratified random sampling method using geographical information system (GIS) software and global positioning system (GPS) technology for application in a health survey in a rural region of Guatemala, as well as a qualitative study of the enumeration process.
This method offers an alternative sampling technique that could reduce opportunities for bias in household selection compared to cluster methods. However, its use is subject to issues surrounding survey preparation, technological limitations and in-the-field household selection. Application of this method in remote areas will raise challenges surrounding the boundary delineation process, use and translation of satellite imagery between GIS and GPS, and household selection at each survey point in varying field conditions. This method favors household selection in denser urban areas and in new residential developments.
Random spatial sampling methodology can be used to survey a random sample of population in a remote region of a developing nation. Although this method should be further validated and compared with more established methods to determine its utility in social survey applications, it shows promise for use in developing nations with resource-challenged environments where detailed geographic and human census data are less available.