Probability Sampling by Connecting Space with Households Using GIS/GPS Technologies

Abstract

Sampling methods for survey studies are challenged by the replacement of landline telephones with mobile phones, the lack of timely census data, and the growing need for studies to address new health challenges. GIS/GPS-assisted methods provide a promising alternative, but these methods need further improvement. We established a stratified 3-stage GIS/GPS-assisted sampling method in which residential areas of a target population are divided into mutually exclusive cells – geographic units (geounits) as the primary sampling frame (PSF). Geounits with residential households were randomly selected from the PSF with a semi-automatic algorithm implemented in R. Novel methods were used to sample households and participants. Simulations and application studies indicated adequate feasibility, efficiency and validity of the method in sampling rural-to-urban migrants from a large city with complex residential arrangements. With this method, researchers can determine sample size and number of geounits, households and participants to be sampled; optimally allocate geounits; determine area size of sampled geounits and estimate sample weights; and complete sampling for field data collection in a short period. Our method adds an integrative approach for GIS/GPS-assisted random sampling with a de facto population assumption. Additional evaluation studies are needed to assess the utility of this method in different settings.