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

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. 1. INTRODUCTION Modern empirical sciences in public health and medicine have been established largely with survey data collected from random or probability samples. Design-based statistical inferences require data collected from a probability sample, and results from a sample of participants can be generalized to a large population only when individual participants are selected with a known probability (Kish 1965; Cochran 1977; Chen, Yin, and Peng 1999; Levy and Lemeshow 1999; Groves Fowler, Couper, Lepkowski, and Singer 2009). Despite its importance, a review of the literature indicates that probability sampling is infrequent in studies published in even very prestigious peer-reviewed journals. For example, a review of articles published in Journal of Acquired Immune Deficiency Syndrome and AIDS and Behavior during June–December of 2014, we found only two out of seventy-two (3%) and six out of 109 (6%) of the population-based survey studies, respectively, used a probability sample for data collection. 1.1 Methodology Barrier to Probabilistic Sampling 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). Although this method is very efficient for sampling, incomplete population coverage has been an issue (Groves et al. 2009). Approximately 3–5% of the households in the United States do not have a landline telephone and thus are not included in the sampling frame (Groves et al. 2009). People living in these households are more likely to be low in socioeconomic status, drug users, sex workers, and/or undocumented migrants (Groves et al. 2009; Singh and Clark 2012), all of whom are at increased risk for poor health. More threatening than incomplete coverage is the replacement of landline telephones by wireless communication technologies that makes it impossible to implement the random digital dialing method. Most methods attempt to achieve randomness by using census data with detailed demographic information at the household level to construct the sampling frame (Kish 1965; Cochran 1977; Groves et al. 2009). However, such data are often not available in a timely manner in all developed countries and unavailable in many resource-limited lower- and middle-income countries. In developed countries, census data are collected only for selected years (usually five or ten years apart), and many resource-limited countries do not collect population census data on a regular basis. Even if census data are available, they may fail to count people who are at high risk for poor health, such as temporary and undocumented immigrants (Groves et al. 2009; Singh et al. 2012). In survey research, sometimes a study population can be operationally defined: for example, non-institutional residents; gender; racial/ethnic minority groups in a country; high school students in a state; or hospitalized patients in a region. In this case, methods are readily available to draw probability samples, such as the multi-stage random sampling methods for national in-household surveys supported by census data (Cochran 1977), telephone surveys supported by random digit dialing with published telephone numbers (Groves et al. 2009), and school surveys using system sampling methods with complete lists of schools and classes (Eaton, Kann, Kinchen, Shanklin, Flint et al. 2012). However, sometimes the sampling frame for a study population may be clear conceptually but hard to define operationally. Challenging examples for drawing probability samples include mobile migrants, sex workers, drug users, and persons living with HIV (Groves et al. 2009; Singh et al. 2012). Timing can be another challenge for random sampling when survey studies are needed to address an urgent public health and medical issue (Heeringa and O’Muircheartaigh 2010a; Heeringa and Ziniel 2012). Typical examples include studies of outbreaks and vaccination of infectious diseases, such as HIV/AIDS, severe acute respiratory syndrome (SARS) (Tong 2005; He, Zhuang, Zhao, Dong, Peng et al. 2007), Ebola (Weyer, Grobbelaar, and Blumberg 2015; Boisen, Hartnett, Goba, Vandi, Grant et al. 2016), and Zika (Boeuf, Drummer, Richards, Scoullar, and Beeson 2016; Deseda 2017). Innovative methods have been attempted for timely sampling without using a sampling frame. Well-known examples include venue-day-time sampling, where participants are selected from locations within time ranges when participants are often present (Mansergh, Naorat, Jommaroeng, Jenkins, Jeeyapant et al. 2006); the capture-recapture method derived from agriculture and wild life studies (Tilling 2001); and respondent-driving sampling (RDS), in which study participants are selected by working with a few seed persons to nominate others within their network connections (Heckathorn 1997, 2002). Although these methods allow for timely sampling of study participants, their validity in ensuring probability and representative samples is unclear. 1.2 GIS/GPS-Assisted Methods as an Alternative 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/GPS-assisted 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). A review of the published studies reveals that most GIS/GPS-assisted sampling methods can be characterized as geographically stratified multi-stage sampling. These methods can be summarized in seven steps: Define targeted study population and geographic area, Construct primary sampling frame (PSF) and define residential area to determine the primary sampling units (PSUs), Randomly select PSU with a probabilistic procedure (simple random, proportion to or stratified by population density), Select households from each sampled PSU through random routes or other methods, and enumerate households to construct the secondary sampling frame (SSF), Randomly select a pre-determined number of participants from SSF, Compute sample weights across all sampling stages, Estimate descriptive statistics for the study population, taking into account the sample design and sampling weights. 1.3 Challenges to Implementing a GIS/GPS-Assisted Sampling Method Despite much progress, additional research is needed on GIS/GPS-assisted sampling methods. First, it is challenging to pre-determine the sample size for several reasons (Landry et al. 2005; Singh et al. 2012; Valliant, Dever, and Kreuter 2013b). The method consists of two steps: sample geographic areas, then sample participants in selected areas. 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. This method has often proved infeasible (Landry et al. 2005; Escamilla et al. 2014; Chen et al. 2015) because of high and variable population density, complex residential structure, and presence of high-rise and multi-function buildings in selected geographic areas. Second, GIS/GPS-assisted sampling method needs to distinguish residential from non-residential housing. Methods for distinguishing between the two have proven very time-consuming to implement (Chen et al. 2009; Singh et al. 2012; Escamilla et al. 2014; Kondo et al. 2014; Pearson et al. 2015). Recent methods haven been developed to recognize visually or digitally residential areas/housing with widely available aerial images (Chang et al. 2009; Wampler et al. 2013; Escamilla et al. 2014; Haenssgen 2015; Pearson et al. 2015). These methods are fast, inexpensive, and highly feasible. However, correctly recognizing residential houses remains a big problem even with the assistance of people from local communities. For example, Pearson et al. (2015) conducted a study to determine residential households using aerial images. With assistance of local experts after computerized sampling, five out of 175 determined residential household structures were verified in the field to be nonresidential. Although the error rate is not high, this study was conducted in a semi-nomadic pastoral area, a setting much simpler for random sampling than that of a modern urban area. More research is needed to improve this method for use in sampling complex residential areas (Escamilla et al. 2014; Chen et al. 2015; Haenssgen 2015). Third, stratification has been used in GIS/GPS-assisted sampling to deal with heterogeneities in population density (Kumar 2007; Galway et al. 2012; Valliant et al. 2013b; Kondo et al. 2014). Galway and colleagues have used this approach in their studies and generated promising results (Galway et al. 2012). However, for stratification to be effective, detailed demographic data at the population level by individual grid cells across a jurisdiction are needed (Galway et al. 2012; Valliant et al. 2013b), and often such data are not available in resource-limited, low- and middle-income countries. Night-time satellite images provide information regarding population density, but this approach does not work for rural areas and resource-limited countries, and places with no electricity (Sutton 1998; Schneider, Friedl, and Potere 2009). 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). 1.4 Purpose of This Study In this study, we report on our attempts to overcome the challenges described above. Our goal is to promote the use of GIS/GPS-assisted sampling method in survey studies with probability samples to better address medical and public health questions. 2 METHODS AND MATERIALS 2.1 Spatial Sampling 2.1.1 Principle and geographic data Geographic data for locations where the study population resides (often by country or jurisdictions within a country) can be obtained from different sources, mostly free of charge (e.g., Google Maps and OpenStreetMap) (Haklay and Weber 2008). The area will be divided into mutually exclusive cells (geographic units, or geounits for short) for further sampling. This spatial sampling process is often realized by creating and laying a grid over the target area (figure 1) and then randomly drawing geounits. Figure 1. View largeDownload slide A Target Area Is Divided into Mutually Exclusive Cells with a Grid Network System. Figure 1. View largeDownload slide A Target Area Is Divided into Mutually Exclusive Cells with a Grid Network System. 2.1.2 Determination of the area size of a geounit Determination of the area A of a geounit is critical to create the grid network described in the previous and next sections. In traditional spatial sampling, A is simply calculated using the sampling ratio (Stehman and Overton 1995; Maguire, Batty, and Goodchild 2005; Valliant et al. 2013b). For example, if a researcher plans to sample 8 geounits to cover 0.01% (10−4) of a geographic area with the total area size of 1,000,000 (106) km2, the area for individual geounits is 12.5 km2 (= 106×10-4/8). A more complex method is needed to draw geographic samples for population-based survey studies, because A is determined not by sampling ratio but by the likelihood of covering an appropriate number of households and eligible persons. Using a larger A increases the chance of sampling adequate numbers of households and eligible subjects, but also increases the workload for household enumeration. The appropriate size A can be determined through pilot studies, considering population density and the number of subjects to be recruited per geounit. For example, when conducting a survey targeting the rural-to-urban migrants temporarily living in Wuhan, China, A = 100m  ×100m was determined through intensive pilot tests in the field. This number was estimated by counting all households located in a geographic area with different sizes being measured manually using tape rulers and/or laser scales. This value of A has approximately 80% probability of covering an adequate number of households, ensuring at least 20 subjects per geounit in a city like Wuhan (Chen et al. 2015). 2.1.3 Creation of grid network and selection of geounits After A is determined, a grid network is then created and overlaid on the target geographic area, dividing the area into mutually exclusive cells (with cell size of A). These cells are the primary sampling frame (PSF) for further sampling (figure 1). Different methods are available for grid network creation; it is often simpler to use geographic coordinate systems rather than side length, and the differences between the two approaches are often small for geographic areas on the scale of a city or a state. For studies involving very large geographic areas like a country (e.g., Russia, Canada, China, or the United States), continents, or the globe, distance defined through appropriate projection systems should be employed. After the PSF is created, a pre-determined number of geounits (to be discussed next) are randomly selected from the PSF. Unlike Pearson’s method, which uses a set of randomly scattered points as place marker (Pearson et al. 2015), we randomly sample a set of geounits with size A in the geographic area where the study population resides. Given large variations in population density across the geographic area, a stratified strategy is used to sample geounits with more geounits being allocated to areas with higher population density. This step is conducted following an optimum allocation approach to enhance work efficiency (Cochran 1977). Another issue confronted in practice is that a randomly selected geounit has a sizable likelihood of being located in nonresidential areas such as lakes, bridges, highways, and commercial buildings. To overcome this problem and to enhance feasibility while maintaining a probabilistic sampling process, we devised a semi-automatic, computer-assisted, stepwise algorithm with no replacement procedure for implementing the geounit sampling protocol (figure 2). More details and the R codes for implementation are provided in Appendix 1 of the online supplementary material. Figure 2. View largeDownload slide Algorithm for Geounit Sampling. Figure 2. View largeDownload slide Algorithm for Geounit Sampling. 2.2 Number of Geounits to Be Sampled Before sampling, the number of geounits to be sampled G has to be determined. Obviously G depends on the sample size N and the average number of participants to be sampled per geounit M. There is a lack of efficient methods to determine G in reported studies (Landry et al. 2005; Kondo et al. 2014). To overcome this limitation, we used the same M for all geounits. With M and sample size N determined through conventional statistical power analysis, G can simply be calculated as: G=N/M. (1) For example, assuming a researcher plans to draw a sample of N = 1,200. If twenty subjects are to be sampled from each geounit, based on equation 1, the number of geounits to be sampled: G=1,200/20 = 60. If thirty subjects per geounit are to be sampled, G=1,200/30 = 40. 2.3 Household and Subject Sampling 2.3.1 Locating the sampled geounits After completing spatial sampling steps as described in the previous two sections, detailed geographic data for the selected geounits are available and can be directly uploaded to GPS receivers. In this study, we tested our method using the Oregon 450 GPS from Garmin, but any GPS receiver can be used if it can upload sampled geographic units with coordinates and areal images and is able to track specified areas manually. 2.3.2 Sequential household access and random participant selection After a sampled geounit is located in the field with the assistance of a GPS receiver, data collectors go to all households within the sampled geounit to prepare for subject sampling and recruitment. This procedure is completed in three steps. Step one: Approach individual households sequentially following natural order with the first household being selected randomly from the main entrance of a street in urban areas or the beginning of a village in rural areas. We used this random route approach to ensure each household has a known probability of being selected (de Rada and Martin 2014; Bauer 2016). Step two: List all eligible participants for each selected household. The list for a household constitutes the secondary sampling frame (SSF). Step three: Select participants randomly from the SSF. If only one person in a household is eligible, this person will be included. If more than one is eligible, only one will be randomly selected using the Kish Table or other random digit method (Kish 1949). Households not available at the time of sampling are revisited to reduce missingness. An innovation of our method is that data collectors can determine the number of households to be enumerated for a geounit. This is because data collectors have already been told the number of households Hand the number of subjects per household S to be sampled. Assuming M=20 and only one subject per household is to be sampled ( S=1), approximately twenty households are enumerated ( H=20). In addition to minimizing work load, this approach moves the sampling probability of a geounit closer to being proportionate to the population density. This is because the ratio of H to the total households in a same-sized geounit will be smaller in a more populated area and larger in a less populated area. 2.3.3 Complementary data collection After household enumeration, participant recruitment, and data collection for a given geounit, the following complementary data must be collected. 1) Actual geounit area size Ag for the gth sampled geounit ( g=1,2,…,G) where households are accessed and participants are sampled. Areas where households are not enumerated must be excluded. The actual area size Ag is determined by GPS receiver recorded tracking data. 2) Total number of households Tg in the accessed area Ag and number of households from which participants are sampled Hg. 3) Households and individuals who refused to participate. 2.4 Determination of Residential Areas To estimate the overall sample weights, the true area size R where the target population resides must be determined to estimate geographic sample weights. Although the concept of residential area has no ambiguity, it is often difficult to determine R (Shannon et al. 2012; Singh et al. 2012; Valliant et al. 2013b; Kondo et al. 2014). We have devised two alternative methods for use in different settings. 2.4.1. Method I: estimate residential area with population and geographic sample data If Ag = actual geounit area size for the gth sampled geounit ( g=1,2,…,G) where households are accessed and participants are sampled (described in Section 2.3.3), then the total area of G geounits B=∑gAg. Let R = the total residential area, P = the total population known to reside in the target district, and Q = the total population covered by all G sampled geounits (the total population Qg for gth geounit is estimated using total households Tg and demographic data from the enumerated households Hg). Both B and Q are calculated based on area size and population data obtained from the randomly selected geounits. If G is adequately large (e.g., twenty or more), the ratio of the two provides an unbiased and reliable estimate of the ratio of R over P. That is, RP≈BQ, (2) So we set R=P×BQ. (3) This approximating method relies on the expectation that households in a sampled geounit are associated only with that geounit. In practice, this can be achieved by carefully determining the appropriate grid size A through pilot studies. 2.4.2 Method II: estimate residential area with Monte Carlo method If data for total population P is not available, the residential area R can be estimated using a Monte Carlo method (Metropolis and Ulam 1949; Mathews 1972). The size of a target area D can be obtained through many GIS packages as described in section 2.1. With the Monte Carlo method, a target area is uploaded to computer. A total number of n points (i.e., several hundred) are randomly selected within the whole area. If nr points fall on residential areas, and nnr non-residential areas, then n=nr+nnr. Since all points are randomly selected, nr/n provides an unbiased estimate of R/D. Thus, R=nrnr+nnrD (4) 2.5 Sample Weights The following equation is used to compute sample weights following the principles for stratified, multistage, and disproportionate probability sampling (Kish 1965; Cochran 1977; Groves et al. 2009; Valliant et al. 2013b). Wi=Wg×Wgh×Wghi (5) Where Wg represents the sample weight for gth geounit and equals R/Ag, where R is the size of total residential area and Ag is the size of gth sampled geounit; Wgh represents the sample weight for household h in geounit g and equals Tg/Hg, where Tg = total households in geounit g and Hg = number of households sampled in geounit g; and lastly, Wghi = sample weight for individual subject i from household h and equals Ngh/njh, where Ngh = = total number of eligible persons in household h within geounit g, and ngh = number of persons sampled in the household h, h=1,2,…,Hg. If only one person per household is sampled, ngh = 1 and Wghi=Ngh. Variance estimation methods are needed to correctly account for the variance inflation due to weighting, (Kish 1965) and the design effect attributable to the clustering of observational units within the sampled area PSUs also needs to be considered (Kish 1965; Heeringa, West, and Berglund 2010b; Valliant, Dever, and Kreuter 2013a). The supplementary Appendix 2 online describes a simulation study conducted to validate this method using both the jackknife replication and the bootstrap for variance estimation (Valliant et al. 2013a). 2.6 Practical Testing We tested the integrative GIS/GPS-assisted sampling method in Wuhan, China, when conducting an NIH-funded project (R01 MH086322, PI: Chen X) to investigate the relationship between social capital and HIV risk behaviors among rural-to-urban migrants. Wuhan is the capital of Hubei Province with a total population of approximately ten million, per capita GDP of $12,708, and a large number of rural-to-urban migrants (Statistical Bureau of Wuhan 2012). The field work for sampling and data collection was completed during 2012–2014. Many migrants do not have a permanent urban residence, and all of them are scattered all over the city. In this case, it is not possible to construct a sampling frame using conventional methods. 3. RESULTS 3.1 Geographic Sampling Frame and Geounits Following the procedure described in this study, a district boundary file of Wuhan was obtained using the ArcGIS. Based on pilot studies for field work efficiency, a grid-system with 100m  ×100m cells was created and imposed on the map to divide the geographic area of Wuhan into small and mutually exclusive cells as geounits (see figure 1). These mutual exclusive cells consist of the PSF for further sampling. A total of sixty geounits with residential housing were randomly drawn from the PSF and stratified by population density following the steps described in section 2.1. A sample size of sixty geounits was chosen to achieve a total sample of 1,200 with approximately twenty participants per geounit. Allocation of the sixty geounits to districts was optimized, considering traveling cost and cost for field data collection. Figure 3 shows the geographic distribution of the sampled geounits. Households within each of these sampled geounits were then accessed and participants recruited following the steps described in section 2.3. Actual geounit area size Ag was determined with data collected during sampling (see Appendix 3 in the online supplementary material for more details). Figure 3. View largeDownload slide Distribution of the Sampled Geounits, Wuhan, China. Figure 3. View largeDownload slide Distribution of the Sampled Geounits, Wuhan, China. 3.2 Samples of Households and Participants Overall, sixty sampled geounits covered 12,016 households, of which approximately 10–25% were occupied by rural migrants. Households were selected following their natural order on a street with the beginning of a main entrance street as the start point, and the first household was determined using random numbers. Of the migrant-occupied households, 1,251 were available and agreed to participate at the time of data collection. A total of 1,310 participants were recruited from these households with one participant per gender per household. The total number of households per geounit varied from thirty in least populated areas to 1,600 in the most populated areas with a median [quartile 1, quartile 3] of one hundred [50, 300] and mean (SD) = 200 (242). The number of households agreeing to participate per geounit varied from twelve to forty with median [quartile 1, quartile 3] of twenty [18, 24] and mean (SD) of twenty-one (6). Table 1 shows the detailed results from the sampling. Applying this method to another study conducted in 2012–2014, we estimated that approximately fifty-eight thousand [95% CI: 47,000, 68,000] rural-to-urban migrants in Wuhan were MSM with 3,650 [95% CI: 2,960, 4,282] being tested HIV positive (Chen et al. 2015). Official surveillance data from Wuhan indicated that a total of 3,408 (primarily MSM) persons were living with HIV in 2015 (Wuhan Center for Disease Prevention and Control (CDC) 2016). The observed result is within the estimated 95% CI, and the relatively small difference provides some evidence supporting the validity of our method. Table 1. Results of GIS/GPS-Assisted Sampling of Rural-to-Urban Migrants, Wuhan, China Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 Table 1. Results of GIS/GPS-Assisted Sampling of Rural-to-Urban Migrants, Wuhan, China Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 4. DISCUSSION AND RECOMMENDATIONS In this study, we reported a geographically stratified 3-stage (geographic unit, household, and participants) GIS/GPS-assisted sampling method. This method is developed by integrating various reported GIS/GPS-assisted sampling methods (Chang et al. 2009; Wampler et al. 2013; Escamilla et al. 2014; Haenssgen 2015; Pearson et al. 2015), particularly the methods with a stratified cluster sampling approach (Cochran 1977; Groves et al. 2009). Innovations include methods to determine residential area and methods for sample weight calculation. Our method enhances existing approaches to drawing probability samples for local, national, cross-national, and global survey studies (Heeringa et al. 2010a; Heeringa et al. 2012). 4.1. Strengths of Our Method Our method is based on sound theories for population and geographic sampling, and has minimal data requirements. Conventional stratified sampling strategies can be used in optimizing geounit allocation to deal with large variations in population density and to increase field-work efficiency (Cochran 1977). The size of geounits can be determined through pilot testing to ensure adequate household/participant coverage, while taking work efficiency into account (Chen et al. 2015). The random route method (Bauer 2016) can be used to ensure an equal probability household sample. Data collected using our method can be analyzed with design-based survey methods (Kish 1965; Cochran 1977; Lohr 1999; Groves et al. 2009; Heeringa et al. 2010b; Valliant et al. 2013b). These methods are available in many software packages, including SUDAAN, SAS, STATA (survey module), SPSS, and “survey” package in R. Many of the sampling tasks of our method can be implemented on computer with open-source software R and free Google imagery data. A more detailed discussion of the application of our methods is provided in Appendix 3 of the online supplementary material. In addition to general survey studies, the increased efficiency may make our method an option to draw probability samples for studying sudden outbreaks of a disease, such as SARS, Ebola, and Zika. 4.2 Recommendation for Application GIS/GPS-assisted sampling methods are becoming increasingly available. If a target study population is located in sparsely populated and less developed rural areas, methods with satellite images to identify households for random sampling are a better choice. Typical examples include methods reported by Haenssgen (Haenssgen 2015), Wampler (Wampler et al. 2013), and Escamilla and colleagues (Escamilla et al. 2014). However, if a researcher wants to conduct studies in highly developed urban settings with more complicated residential arrangements, our method would be a better choice than many other methods to ensure probability samples (Landry et al. 2005; Galway et al. 2012; Kondo et al. 2014). To ensure successful application of our method in drawing a probability sample to represent a study population, researchers must pay additional attention to the following three aspects. The first aspect is related to variations in population density. The fundamental mechanism of our method is to link geographic area with varying population density to households using numerous small geounits for further sampling. Therefore, one natural approach to deal with varying population density is application of the classic stratified sampling strategy to optimize geounit allocation (Cochran 1977), as have been commonly used in this and other studies (Galway et al. 2012; Chen et al. 2015). Our method also offers other possibilities to deal with varying population density issues. For example, instead of using a fixed geounit size and sampling grid, with our method researchers can determine the geounit size disproportionate to population density after randomly selecting the pre-determined number of geounits to be selected. Although determination of population density could remain be a challenge resource-limited areas, we may be able to deal with it with satellite imagery that is widely available. The second aspect is the determination of area size of a geounit. Larger sizes have greater probability of covering an adequate number of households for sampling. However, if a large-sized geounit is randomly selected in a highly populous area, it will prevent researchers from completing the sampling due to high costs of time and money (Landry et al. 2005). We recommend that researchers conduct adequate pilot studies to determine geounit size, considering variations in population density, time, and resources available for sampling. The third aspect is household selection within a sampled geounit. Although each selected geounit is not large in area size with a relatively fewer number of households, household arrangement can still be complex. In this study, we used the random route approach (Bauer 2016), by randomly selecting one household as starting point and then following natural order to select other households until the pre-determined number of households was reached. However, our method may lead to biased estimates of parameters that are related to physical distance. This can happen even with carefully planned and well-tested instructions (Bauer 2016). If conditions permit, an ideal approach would be to list all households in a sampled geounit first and then randomly select the pre-determined number of household for further sampling. 4.3 Limitations and Further Research In this study, we only demonstrate our method in sampling rural migrants in urban China. A full assessment of the value of our approach requires its application to different populations in diverse geographic and residential settings. Like any multistage sampling method, it is a challenge to ensure an equal probability sample of households. The random route provides a good option, but attention must be paid to instructions to the data collectors and random selection of the starting household (Bauer 2016). Data on the size of a geographic unit is often not directly available, and can be obtained only through repeated pilot tests. Given large variations in household and population density in urban settings, large variations in estimated sample weights are anticipated. 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For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Survey Statistics and Methodology Oxford University Press

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

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Abstract

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. 1. INTRODUCTION Modern empirical sciences in public health and medicine have been established largely with survey data collected from random or probability samples. Design-based statistical inferences require data collected from a probability sample, and results from a sample of participants can be generalized to a large population only when individual participants are selected with a known probability (Kish 1965; Cochran 1977; Chen, Yin, and Peng 1999; Levy and Lemeshow 1999; Groves Fowler, Couper, Lepkowski, and Singer 2009). Despite its importance, a review of the literature indicates that probability sampling is infrequent in studies published in even very prestigious peer-reviewed journals. For example, a review of articles published in Journal of Acquired Immune Deficiency Syndrome and AIDS and Behavior during June–December of 2014, we found only two out of seventy-two (3%) and six out of 109 (6%) of the population-based survey studies, respectively, used a probability sample for data collection. 1.1 Methodology Barrier to Probabilistic Sampling 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). Although this method is very efficient for sampling, incomplete population coverage has been an issue (Groves et al. 2009). Approximately 3–5% of the households in the United States do not have a landline telephone and thus are not included in the sampling frame (Groves et al. 2009). People living in these households are more likely to be low in socioeconomic status, drug users, sex workers, and/or undocumented migrants (Groves et al. 2009; Singh and Clark 2012), all of whom are at increased risk for poor health. More threatening than incomplete coverage is the replacement of landline telephones by wireless communication technologies that makes it impossible to implement the random digital dialing method. Most methods attempt to achieve randomness by using census data with detailed demographic information at the household level to construct the sampling frame (Kish 1965; Cochran 1977; Groves et al. 2009). However, such data are often not available in a timely manner in all developed countries and unavailable in many resource-limited lower- and middle-income countries. In developed countries, census data are collected only for selected years (usually five or ten years apart), and many resource-limited countries do not collect population census data on a regular basis. Even if census data are available, they may fail to count people who are at high risk for poor health, such as temporary and undocumented immigrants (Groves et al. 2009; Singh et al. 2012). In survey research, sometimes a study population can be operationally defined: for example, non-institutional residents; gender; racial/ethnic minority groups in a country; high school students in a state; or hospitalized patients in a region. In this case, methods are readily available to draw probability samples, such as the multi-stage random sampling methods for national in-household surveys supported by census data (Cochran 1977), telephone surveys supported by random digit dialing with published telephone numbers (Groves et al. 2009), and school surveys using system sampling methods with complete lists of schools and classes (Eaton, Kann, Kinchen, Shanklin, Flint et al. 2012). However, sometimes the sampling frame for a study population may be clear conceptually but hard to define operationally. Challenging examples for drawing probability samples include mobile migrants, sex workers, drug users, and persons living with HIV (Groves et al. 2009; Singh et al. 2012). Timing can be another challenge for random sampling when survey studies are needed to address an urgent public health and medical issue (Heeringa and O’Muircheartaigh 2010a; Heeringa and Ziniel 2012). Typical examples include studies of outbreaks and vaccination of infectious diseases, such as HIV/AIDS, severe acute respiratory syndrome (SARS) (Tong 2005; He, Zhuang, Zhao, Dong, Peng et al. 2007), Ebola (Weyer, Grobbelaar, and Blumberg 2015; Boisen, Hartnett, Goba, Vandi, Grant et al. 2016), and Zika (Boeuf, Drummer, Richards, Scoullar, and Beeson 2016; Deseda 2017). Innovative methods have been attempted for timely sampling without using a sampling frame. Well-known examples include venue-day-time sampling, where participants are selected from locations within time ranges when participants are often present (Mansergh, Naorat, Jommaroeng, Jenkins, Jeeyapant et al. 2006); the capture-recapture method derived from agriculture and wild life studies (Tilling 2001); and respondent-driving sampling (RDS), in which study participants are selected by working with a few seed persons to nominate others within their network connections (Heckathorn 1997, 2002). Although these methods allow for timely sampling of study participants, their validity in ensuring probability and representative samples is unclear. 1.2 GIS/GPS-Assisted Methods as an Alternative 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/GPS-assisted 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). A review of the published studies reveals that most GIS/GPS-assisted sampling methods can be characterized as geographically stratified multi-stage sampling. These methods can be summarized in seven steps: Define targeted study population and geographic area, Construct primary sampling frame (PSF) and define residential area to determine the primary sampling units (PSUs), Randomly select PSU with a probabilistic procedure (simple random, proportion to or stratified by population density), Select households from each sampled PSU through random routes or other methods, and enumerate households to construct the secondary sampling frame (SSF), Randomly select a pre-determined number of participants from SSF, Compute sample weights across all sampling stages, Estimate descriptive statistics for the study population, taking into account the sample design and sampling weights. 1.3 Challenges to Implementing a GIS/GPS-Assisted Sampling Method Despite much progress, additional research is needed on GIS/GPS-assisted sampling methods. First, it is challenging to pre-determine the sample size for several reasons (Landry et al. 2005; Singh et al. 2012; Valliant, Dever, and Kreuter 2013b). The method consists of two steps: sample geographic areas, then sample participants in selected areas. 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. This method has often proved infeasible (Landry et al. 2005; Escamilla et al. 2014; Chen et al. 2015) because of high and variable population density, complex residential structure, and presence of high-rise and multi-function buildings in selected geographic areas. Second, GIS/GPS-assisted sampling method needs to distinguish residential from non-residential housing. Methods for distinguishing between the two have proven very time-consuming to implement (Chen et al. 2009; Singh et al. 2012; Escamilla et al. 2014; Kondo et al. 2014; Pearson et al. 2015). Recent methods haven been developed to recognize visually or digitally residential areas/housing with widely available aerial images (Chang et al. 2009; Wampler et al. 2013; Escamilla et al. 2014; Haenssgen 2015; Pearson et al. 2015). These methods are fast, inexpensive, and highly feasible. However, correctly recognizing residential houses remains a big problem even with the assistance of people from local communities. For example, Pearson et al. (2015) conducted a study to determine residential households using aerial images. With assistance of local experts after computerized sampling, five out of 175 determined residential household structures were verified in the field to be nonresidential. Although the error rate is not high, this study was conducted in a semi-nomadic pastoral area, a setting much simpler for random sampling than that of a modern urban area. More research is needed to improve this method for use in sampling complex residential areas (Escamilla et al. 2014; Chen et al. 2015; Haenssgen 2015). Third, stratification has been used in GIS/GPS-assisted sampling to deal with heterogeneities in population density (Kumar 2007; Galway et al. 2012; Valliant et al. 2013b; Kondo et al. 2014). Galway and colleagues have used this approach in their studies and generated promising results (Galway et al. 2012). However, for stratification to be effective, detailed demographic data at the population level by individual grid cells across a jurisdiction are needed (Galway et al. 2012; Valliant et al. 2013b), and often such data are not available in resource-limited, low- and middle-income countries. Night-time satellite images provide information regarding population density, but this approach does not work for rural areas and resource-limited countries, and places with no electricity (Sutton 1998; Schneider, Friedl, and Potere 2009). 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). 1.4 Purpose of This Study In this study, we report on our attempts to overcome the challenges described above. Our goal is to promote the use of GIS/GPS-assisted sampling method in survey studies with probability samples to better address medical and public health questions. 2 METHODS AND MATERIALS 2.1 Spatial Sampling 2.1.1 Principle and geographic data Geographic data for locations where the study population resides (often by country or jurisdictions within a country) can be obtained from different sources, mostly free of charge (e.g., Google Maps and OpenStreetMap) (Haklay and Weber 2008). The area will be divided into mutually exclusive cells (geographic units, or geounits for short) for further sampling. This spatial sampling process is often realized by creating and laying a grid over the target area (figure 1) and then randomly drawing geounits. Figure 1. View largeDownload slide A Target Area Is Divided into Mutually Exclusive Cells with a Grid Network System. Figure 1. View largeDownload slide A Target Area Is Divided into Mutually Exclusive Cells with a Grid Network System. 2.1.2 Determination of the area size of a geounit Determination of the area A of a geounit is critical to create the grid network described in the previous and next sections. In traditional spatial sampling, A is simply calculated using the sampling ratio (Stehman and Overton 1995; Maguire, Batty, and Goodchild 2005; Valliant et al. 2013b). For example, if a researcher plans to sample 8 geounits to cover 0.01% (10−4) of a geographic area with the total area size of 1,000,000 (106) km2, the area for individual geounits is 12.5 km2 (= 106×10-4/8). A more complex method is needed to draw geographic samples for population-based survey studies, because A is determined not by sampling ratio but by the likelihood of covering an appropriate number of households and eligible persons. Using a larger A increases the chance of sampling adequate numbers of households and eligible subjects, but also increases the workload for household enumeration. The appropriate size A can be determined through pilot studies, considering population density and the number of subjects to be recruited per geounit. For example, when conducting a survey targeting the rural-to-urban migrants temporarily living in Wuhan, China, A = 100m  ×100m was determined through intensive pilot tests in the field. This number was estimated by counting all households located in a geographic area with different sizes being measured manually using tape rulers and/or laser scales. This value of A has approximately 80% probability of covering an adequate number of households, ensuring at least 20 subjects per geounit in a city like Wuhan (Chen et al. 2015). 2.1.3 Creation of grid network and selection of geounits After A is determined, a grid network is then created and overlaid on the target geographic area, dividing the area into mutually exclusive cells (with cell size of A). These cells are the primary sampling frame (PSF) for further sampling (figure 1). Different methods are available for grid network creation; it is often simpler to use geographic coordinate systems rather than side length, and the differences between the two approaches are often small for geographic areas on the scale of a city or a state. For studies involving very large geographic areas like a country (e.g., Russia, Canada, China, or the United States), continents, or the globe, distance defined through appropriate projection systems should be employed. After the PSF is created, a pre-determined number of geounits (to be discussed next) are randomly selected from the PSF. Unlike Pearson’s method, which uses a set of randomly scattered points as place marker (Pearson et al. 2015), we randomly sample a set of geounits with size A in the geographic area where the study population resides. Given large variations in population density across the geographic area, a stratified strategy is used to sample geounits with more geounits being allocated to areas with higher population density. This step is conducted following an optimum allocation approach to enhance work efficiency (Cochran 1977). Another issue confronted in practice is that a randomly selected geounit has a sizable likelihood of being located in nonresidential areas such as lakes, bridges, highways, and commercial buildings. To overcome this problem and to enhance feasibility while maintaining a probabilistic sampling process, we devised a semi-automatic, computer-assisted, stepwise algorithm with no replacement procedure for implementing the geounit sampling protocol (figure 2). More details and the R codes for implementation are provided in Appendix 1 of the online supplementary material. Figure 2. View largeDownload slide Algorithm for Geounit Sampling. Figure 2. View largeDownload slide Algorithm for Geounit Sampling. 2.2 Number of Geounits to Be Sampled Before sampling, the number of geounits to be sampled G has to be determined. Obviously G depends on the sample size N and the average number of participants to be sampled per geounit M. There is a lack of efficient methods to determine G in reported studies (Landry et al. 2005; Kondo et al. 2014). To overcome this limitation, we used the same M for all geounits. With M and sample size N determined through conventional statistical power analysis, G can simply be calculated as: G=N/M. (1) For example, assuming a researcher plans to draw a sample of N = 1,200. If twenty subjects are to be sampled from each geounit, based on equation 1, the number of geounits to be sampled: G=1,200/20 = 60. If thirty subjects per geounit are to be sampled, G=1,200/30 = 40. 2.3 Household and Subject Sampling 2.3.1 Locating the sampled geounits After completing spatial sampling steps as described in the previous two sections, detailed geographic data for the selected geounits are available and can be directly uploaded to GPS receivers. In this study, we tested our method using the Oregon 450 GPS from Garmin, but any GPS receiver can be used if it can upload sampled geographic units with coordinates and areal images and is able to track specified areas manually. 2.3.2 Sequential household access and random participant selection After a sampled geounit is located in the field with the assistance of a GPS receiver, data collectors go to all households within the sampled geounit to prepare for subject sampling and recruitment. This procedure is completed in three steps. Step one: Approach individual households sequentially following natural order with the first household being selected randomly from the main entrance of a street in urban areas or the beginning of a village in rural areas. We used this random route approach to ensure each household has a known probability of being selected (de Rada and Martin 2014; Bauer 2016). Step two: List all eligible participants for each selected household. The list for a household constitutes the secondary sampling frame (SSF). Step three: Select participants randomly from the SSF. If only one person in a household is eligible, this person will be included. If more than one is eligible, only one will be randomly selected using the Kish Table or other random digit method (Kish 1949). Households not available at the time of sampling are revisited to reduce missingness. An innovation of our method is that data collectors can determine the number of households to be enumerated for a geounit. This is because data collectors have already been told the number of households Hand the number of subjects per household S to be sampled. Assuming M=20 and only one subject per household is to be sampled ( S=1), approximately twenty households are enumerated ( H=20). In addition to minimizing work load, this approach moves the sampling probability of a geounit closer to being proportionate to the population density. This is because the ratio of H to the total households in a same-sized geounit will be smaller in a more populated area and larger in a less populated area. 2.3.3 Complementary data collection After household enumeration, participant recruitment, and data collection for a given geounit, the following complementary data must be collected. 1) Actual geounit area size Ag for the gth sampled geounit ( g=1,2,…,G) where households are accessed and participants are sampled. Areas where households are not enumerated must be excluded. The actual area size Ag is determined by GPS receiver recorded tracking data. 2) Total number of households Tg in the accessed area Ag and number of households from which participants are sampled Hg. 3) Households and individuals who refused to participate. 2.4 Determination of Residential Areas To estimate the overall sample weights, the true area size R where the target population resides must be determined to estimate geographic sample weights. Although the concept of residential area has no ambiguity, it is often difficult to determine R (Shannon et al. 2012; Singh et al. 2012; Valliant et al. 2013b; Kondo et al. 2014). We have devised two alternative methods for use in different settings. 2.4.1. Method I: estimate residential area with population and geographic sample data If Ag = actual geounit area size for the gth sampled geounit ( g=1,2,…,G) where households are accessed and participants are sampled (described in Section 2.3.3), then the total area of G geounits B=∑gAg. Let R = the total residential area, P = the total population known to reside in the target district, and Q = the total population covered by all G sampled geounits (the total population Qg for gth geounit is estimated using total households Tg and demographic data from the enumerated households Hg). Both B and Q are calculated based on area size and population data obtained from the randomly selected geounits. If G is adequately large (e.g., twenty or more), the ratio of the two provides an unbiased and reliable estimate of the ratio of R over P. That is, RP≈BQ, (2) So we set R=P×BQ. (3) This approximating method relies on the expectation that households in a sampled geounit are associated only with that geounit. In practice, this can be achieved by carefully determining the appropriate grid size A through pilot studies. 2.4.2 Method II: estimate residential area with Monte Carlo method If data for total population P is not available, the residential area R can be estimated using a Monte Carlo method (Metropolis and Ulam 1949; Mathews 1972). The size of a target area D can be obtained through many GIS packages as described in section 2.1. With the Monte Carlo method, a target area is uploaded to computer. A total number of n points (i.e., several hundred) are randomly selected within the whole area. If nr points fall on residential areas, and nnr non-residential areas, then n=nr+nnr. Since all points are randomly selected, nr/n provides an unbiased estimate of R/D. Thus, R=nrnr+nnrD (4) 2.5 Sample Weights The following equation is used to compute sample weights following the principles for stratified, multistage, and disproportionate probability sampling (Kish 1965; Cochran 1977; Groves et al. 2009; Valliant et al. 2013b). Wi=Wg×Wgh×Wghi (5) Where Wg represents the sample weight for gth geounit and equals R/Ag, where R is the size of total residential area and Ag is the size of gth sampled geounit; Wgh represents the sample weight for household h in geounit g and equals Tg/Hg, where Tg = total households in geounit g and Hg = number of households sampled in geounit g; and lastly, Wghi = sample weight for individual subject i from household h and equals Ngh/njh, where Ngh = = total number of eligible persons in household h within geounit g, and ngh = number of persons sampled in the household h, h=1,2,…,Hg. If only one person per household is sampled, ngh = 1 and Wghi=Ngh. Variance estimation methods are needed to correctly account for the variance inflation due to weighting, (Kish 1965) and the design effect attributable to the clustering of observational units within the sampled area PSUs also needs to be considered (Kish 1965; Heeringa, West, and Berglund 2010b; Valliant, Dever, and Kreuter 2013a). The supplementary Appendix 2 online describes a simulation study conducted to validate this method using both the jackknife replication and the bootstrap for variance estimation (Valliant et al. 2013a). 2.6 Practical Testing We tested the integrative GIS/GPS-assisted sampling method in Wuhan, China, when conducting an NIH-funded project (R01 MH086322, PI: Chen X) to investigate the relationship between social capital and HIV risk behaviors among rural-to-urban migrants. Wuhan is the capital of Hubei Province with a total population of approximately ten million, per capita GDP of $12,708, and a large number of rural-to-urban migrants (Statistical Bureau of Wuhan 2012). The field work for sampling and data collection was completed during 2012–2014. Many migrants do not have a permanent urban residence, and all of them are scattered all over the city. In this case, it is not possible to construct a sampling frame using conventional methods. 3. RESULTS 3.1 Geographic Sampling Frame and Geounits Following the procedure described in this study, a district boundary file of Wuhan was obtained using the ArcGIS. Based on pilot studies for field work efficiency, a grid-system with 100m  ×100m cells was created and imposed on the map to divide the geographic area of Wuhan into small and mutually exclusive cells as geounits (see figure 1). These mutual exclusive cells consist of the PSF for further sampling. A total of sixty geounits with residential housing were randomly drawn from the PSF and stratified by population density following the steps described in section 2.1. A sample size of sixty geounits was chosen to achieve a total sample of 1,200 with approximately twenty participants per geounit. Allocation of the sixty geounits to districts was optimized, considering traveling cost and cost for field data collection. Figure 3 shows the geographic distribution of the sampled geounits. Households within each of these sampled geounits were then accessed and participants recruited following the steps described in section 2.3. Actual geounit area size Ag was determined with data collected during sampling (see Appendix 3 in the online supplementary material for more details). Figure 3. View largeDownload slide Distribution of the Sampled Geounits, Wuhan, China. Figure 3. View largeDownload slide Distribution of the Sampled Geounits, Wuhan, China. 3.2 Samples of Households and Participants Overall, sixty sampled geounits covered 12,016 households, of which approximately 10–25% were occupied by rural migrants. Households were selected following their natural order on a street with the beginning of a main entrance street as the start point, and the first household was determined using random numbers. Of the migrant-occupied households, 1,251 were available and agreed to participate at the time of data collection. A total of 1,310 participants were recruited from these households with one participant per gender per household. The total number of households per geounit varied from thirty in least populated areas to 1,600 in the most populated areas with a median [quartile 1, quartile 3] of one hundred [50, 300] and mean (SD) = 200 (242). The number of households agreeing to participate per geounit varied from twelve to forty with median [quartile 1, quartile 3] of twenty [18, 24] and mean (SD) of twenty-one (6). Table 1 shows the detailed results from the sampling. Applying this method to another study conducted in 2012–2014, we estimated that approximately fifty-eight thousand [95% CI: 47,000, 68,000] rural-to-urban migrants in Wuhan were MSM with 3,650 [95% CI: 2,960, 4,282] being tested HIV positive (Chen et al. 2015). Official surveillance data from Wuhan indicated that a total of 3,408 (primarily MSM) persons were living with HIV in 2015 (Wuhan Center for Disease Prevention and Control (CDC) 2016). The observed result is within the estimated 95% CI, and the relatively small difference provides some evidence supporting the validity of our method. Table 1. Results of GIS/GPS-Assisted Sampling of Rural-to-Urban Migrants, Wuhan, China Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 Table 1. Results of GIS/GPS-Assisted Sampling of Rural-to-Urban Migrants, Wuhan, China Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 Geounit Id Actual area ( m2) Weight Wg Total households Accessed households Weight Wgh Participants recruited Total 1,812,600 3453.99 12,016 1,251 9.61 1,310 M017 10,000 108.99 400 25 16.00 25 M023 20,000 54.55 50 21 2.38 21 M024 28,750 37.89 80 14 5.71 20 M025 10,000 108.99 528 25 21.12 25 M026 15,000 72.66 200 24 8.33 24 M033 45,000 11.22 87 28 3.11 28 M037 15,000 33.77 312 21 14.86 22 M038 77,500 6.56 300 22 13.64 23 M039 135,000 3.78 110 23 4.78 26 M045 10,000 50.66 60 12 5.00 12 M047 20,000 25.33 50 19 2.63 20 M049 10,000 50.66 274 13 21.08 19 M050 27,500 18.45 70 23 3.04 23 M056 21,250 23.89 100 29 3.45 31 M057 40,000 24.44 500 24 20.83 25 M058 15,000 65.22 600 19 31.58 21 M070 17,500 55.88 260 25 10.40 25 M072 32,500 30.11 180 45 4.00 27 M076 10,000 97.77 140 17 8.24 18 M078 23,300 42.00 50 20 2.50 20 M079 25,000 40.66 30 18 1.67 18 M082 36,250 28.11 100 18 5.56 19 M083 36,250 28.11 400 40 10.00 40 M086 28,750 35.44 55 12 4.58 15 M087 22,500 45.22 100 20 5.00 20 M094 80,000 12.78 50 26 1.92 26 M095 12,500 81.44 40 18 2.22 20 M100 11,250 90.44 500 26 19.23 26 M102 18,250 55.77 45 21 2.14 21 M106 13,500 42.55 1,600 18 88.89 21 M111 53,300 10.78 348 20 17.40 21 M114 15,000 38.33 400 26 15.38 29 M117 35,000 16.44 300 25 12.00 25 M126 15,500 37.11 400 20 20.00 21 M128 47,500 12.11 50 21 2.38 21 M134 75,000 7.67 500 21 23.81 23 M138 46,200 12.44 40 11 3.64 18 M144 65,000 8.89 60 19 3.16 19 M148 49,000 11.78 50 19 2.63 21 M152 27,500 29.33 70 18 3.89 19 M153 20,000 40.22 40 19 2.11 21 M156 20,000 40.22 92 15 6.13 20 M159 37,500 21.44 50 19 2.63 18 M160 25,000 32.22 40 22 1.82 25 M166 21,250 37.89 60 18 3.33 21 M171 16,250 49.55 50 20 2.50 21 M172 52,500 15.33 421 19 22.16 19 M189 25,000 32.22 300 27 11.11 27 M194 46,250 17.44 147 25 5.88 27 M198 26,250 997.23 256 18 14.22 20 M199 37,500 32.11 80 20 4.00 20 M204 25,000 48.22 250 20 12.50 21 M207 15,000 80.33 40 19 2.11 20 M211 20,000 60.22 212 20 10.60 21 M221 14,500 83.10 100 16 6.25 16 M227 14,500 83.10 60 14 4.29 14 M229 15,000 80.33 50 17 2.94 19 M252 23,750 50.77 60 24 2.50 24 M258 37,500 32.11 69 14 4.93 19 M260 23,300 51.77 150 19 7.89 19 Median 23,525 37.89 100 20 5 21 IQR 15,000-37,500 22.05-55.47 50-300 18-24 2.71-13.36 19-25 Mean 30,210 57.57 200.27 20.85 9.63 21.83 SD 21,900 126.13 241.81 5.75 12.58 4.34 4. DISCUSSION AND RECOMMENDATIONS In this study, we reported a geographically stratified 3-stage (geographic unit, household, and participants) GIS/GPS-assisted sampling method. This method is developed by integrating various reported GIS/GPS-assisted sampling methods (Chang et al. 2009; Wampler et al. 2013; Escamilla et al. 2014; Haenssgen 2015; Pearson et al. 2015), particularly the methods with a stratified cluster sampling approach (Cochran 1977; Groves et al. 2009). Innovations include methods to determine residential area and methods for sample weight calculation. Our method enhances existing approaches to drawing probability samples for local, national, cross-national, and global survey studies (Heeringa et al. 2010a; Heeringa et al. 2012). 4.1. Strengths of Our Method Our method is based on sound theories for population and geographic sampling, and has minimal data requirements. Conventional stratified sampling strategies can be used in optimizing geounit allocation to deal with large variations in population density and to increase field-work efficiency (Cochran 1977). The size of geounits can be determined through pilot testing to ensure adequate household/participant coverage, while taking work efficiency into account (Chen et al. 2015). The random route method (Bauer 2016) can be used to ensure an equal probability household sample. Data collected using our method can be analyzed with design-based survey methods (Kish 1965; Cochran 1977; Lohr 1999; Groves et al. 2009; Heeringa et al. 2010b; Valliant et al. 2013b). These methods are available in many software packages, including SUDAAN, SAS, STATA (survey module), SPSS, and “survey” package in R. Many of the sampling tasks of our method can be implemented on computer with open-source software R and free Google imagery data. A more detailed discussion of the application of our methods is provided in Appendix 3 of the online supplementary material. In addition to general survey studies, the increased efficiency may make our method an option to draw probability samples for studying sudden outbreaks of a disease, such as SARS, Ebola, and Zika. 4.2 Recommendation for Application GIS/GPS-assisted sampling methods are becoming increasingly available. If a target study population is located in sparsely populated and less developed rural areas, methods with satellite images to identify households for random sampling are a better choice. Typical examples include methods reported by Haenssgen (Haenssgen 2015), Wampler (Wampler et al. 2013), and Escamilla and colleagues (Escamilla et al. 2014). However, if a researcher wants to conduct studies in highly developed urban settings with more complicated residential arrangements, our method would be a better choice than many other methods to ensure probability samples (Landry et al. 2005; Galway et al. 2012; Kondo et al. 2014). To ensure successful application of our method in drawing a probability sample to represent a study population, researchers must pay additional attention to the following three aspects. The first aspect is related to variations in population density. The fundamental mechanism of our method is to link geographic area with varying population density to households using numerous small geounits for further sampling. Therefore, one natural approach to deal with varying population density is application of the classic stratified sampling strategy to optimize geounit allocation (Cochran 1977), as have been commonly used in this and other studies (Galway et al. 2012; Chen et al. 2015). Our method also offers other possibilities to deal with varying population density issues. For example, instead of using a fixed geounit size and sampling grid, with our method researchers can determine the geounit size disproportionate to population density after randomly selecting the pre-determined number of geounits to be selected. Although determination of population density could remain be a challenge resource-limited areas, we may be able to deal with it with satellite imagery that is widely available. The second aspect is the determination of area size of a geounit. Larger sizes have greater probability of covering an adequate number of households for sampling. However, if a large-sized geounit is randomly selected in a highly populous area, it will prevent researchers from completing the sampling due to high costs of time and money (Landry et al. 2005). We recommend that researchers conduct adequate pilot studies to determine geounit size, considering variations in population density, time, and resources available for sampling. The third aspect is household selection within a sampled geounit. Although each selected geounit is not large in area size with a relatively fewer number of households, household arrangement can still be complex. In this study, we used the random route approach (Bauer 2016), by randomly selecting one household as starting point and then following natural order to select other households until the pre-determined number of households was reached. However, our method may lead to biased estimates of parameters that are related to physical distance. This can happen even with carefully planned and well-tested instructions (Bauer 2016). If conditions permit, an ideal approach would be to list all households in a sampled geounit first and then randomly select the pre-determined number of household for further sampling. 4.3 Limitations and Further Research In this study, we only demonstrate our method in sampling rural migrants in urban China. A full assessment of the value of our approach requires its application to different populations in diverse geographic and residential settings. Like any multistage sampling method, it is a challenge to ensure an equal probability sample of households. The random route provides a good option, but attention must be paid to instructions to the data collectors and random selection of the starting household (Bauer 2016). Data on the size of a geographic unit is often not directly available, and can be obtained only through repeated pilot tests. Given large variations in household and population density in urban settings, large variations in estimated sample weights are anticipated. 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Journal of Survey Statistics and Methodology – Oxford University Press

Published: Jan 23, 2018

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