Choosing a survey sample when data on the population are limited: a method using Global Positioning Systems and aerial and satellite photographs

Abstract

BackgroundVarious methods have been proposed for sampling when data on the population are limited. However, these methods are often biased. We propose a new method to draw a population sample using Global Positioning Systems and aerial or satellite photographs.ResultsWe randomly sampled Global Positioning System locations in designated areas. A circle was drawn around each location with radius representing 20 m. Buildings in the circle were identified from satellite photographs; one was randomly chosen. Interviewers selected one household from the building, and interviews were conducted with eligible household members.ConclusionsParticipants had known selection probabilities, allowing proper estimation of parameters of interest and their variances. The approach was made possible by recent technological developments and access to satellite photographs.

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

“…One primary barrier preventing researchers from using a probabilistic sample design could be the lack of appropriate methods (Landry and Shen 2005;Shannon, Hutson, Kolbe, Stringer, and Haines 2012;Wampler, Rediske, and Molla 2013;Escamilla, Emch, Dandalo, Miller, Martinson et al 2014;Chen, Yu, Zhou, Zhou, Gong et al 2015). One approach is landline telephone number-based random digit dialing (Kish 1965;Cochran 1977).…”

“…Technological advances in geographic information systems (GIS) and global positioning systems (GPS) have encouraged numerous researchers to develop speedy probabilistic sampling methods with adequate geographic and population coverage, with minimal data requirements (Murray, O'Green, and McDaniel 2003;Landry et al 2005;Galway, Bell, Sae, Hagopian, Burnham et al 2012;Shannon et al 2012;Chen et al 2015). A number of GIS/GPSassisted probability sampling methods have been developed to deal with specific settings, such as sampling in remote rural areas (Wampler et al 2013;Escamilla et al 2014;Kondo, Bream, Barg, and Branas 2014;Haenssgen 2015;Pearson, Rzotkiewicz, and Zwickle 2015), mobile populations (Landry et al 2005;Singh and Clark 2013;Chen et al 2015), and other special conditions (Murray et al 2003;Galway et al 2012).…”

“…Sample size is easy to determine if a study only needs to draw geographic samples (Balch, Drapeau, Bowler, Booth, Goes et al 2004;Chen, Zhao, Gao, Henkelmann, and Schramm 2006;Conway 2006;Daly, Lei, Teixeira, Muir, Castillo et al 2007;Huang, Zhao, Shi, Yu, Zhao et al 2007;Valliant et al 2013b: Pearson et al 2015. However, it is not possible to know exactly how many persons are present in a randomly sampled geographic area before the area is selected (Landry et al 2005;Shannon et al 2012;Valliant et al 2013b;Chen et al 2015). One solution is to enumerate all households in a randomly sampled area after a geographic area is selected.…”

“…Last but not least, geographic sampling weights are difficult to determine because of the lack of clear boundaries between residential and nonresidential areas and lack of information on the number of persons living in sampled geographic units at a specific date and time (Landry et al 2005;Kumar 2007;Shannon et al 2012;Valliant et al 2013b;Kondo et al 2014).…”

Unmanned Aircraft Systems Can Improve Survey Data Collection

“…[15][16][17] However, listing can put field staff at risk for robbery or kidnapping, because it involves walking systematically down all streets, as opposed to interviewing, which often takes place inside respondents' homes. 18 Listing also adds to data collection costs because it requires two trips to the area-one for listing and another for interviewing.…”