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A grid-based sample design framework for household surveys

A grid-based sample design framework for household surveys
A grid-based sample design framework for household surveys
Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k-means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.

Keywords: Demography, Household Surveys, Sample Design, Spatial Sampling, Gridded Population, Democratic Republic of the Congo
Demography, Household Surveys, Sample Design, Spatial Sampling, Gridded Population, Democratic Republic of the Congo
Gates Open Research
Boo, Gianluca
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Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Thomson-Browne, Dana, Renee
c6aa22a0-9ee2-4d86-9bd4-b3a8487eb15b
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Thomson-Browne, Dana, Renee
c6aa22a0-9ee2-4d86-9bd4-b3a8487eb15b
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Boo, Gianluca, Darin, Edith, Thomson-Browne, Dana, Renee and Tatem, Andrew (2020) A grid-based sample design framework for household surveys Gates Open Research 16pp. (doi:10.12688/gatesopenres.13107.1).

Record type: Monograph (Working Paper)

Abstract

Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k-means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.

Keywords: Demography, Household Surveys, Sample Design, Spatial Sampling, Gridded Population, Democratic Republic of the Congo

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8d6e4a3e-eed7-4e1f-bfc6-bcbde196c8d4_13107_-_gianluca_boo - Author's Original
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More information

In preparation date: 27 January 2020
Keywords: Demography, Household Surveys, Sample Design, Spatial Sampling, Gridded Population, Democratic Republic of the Congo

Identifiers

Local EPrints ID: 438457
URI: http://eprints.soton.ac.uk/id/eprint/438457
PURE UUID: 1062a2fb-1448-4946-81bd-312eab0337da
ORCID for Gianluca Boo: ORCID iD orcid.org/0000-0002-4078-8221
ORCID for Edith Darin: ORCID iD orcid.org/0000-0002-8176-092X
ORCID for Dana, Renee Thomson-Browne: ORCID iD orcid.org/0000-0002-9507-9123
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 10 Mar 2020 17:32
Last modified: 04 Aug 2020 01:52

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