DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2020 No. 176

Mahmoud Elkasabi

Ruilin Ren

September 2020

This document was produced for review by the United States Agency for

International Development.

Gridded Population Sampling and the

Demographic and Health Surveys

DHS Working Papers No. 176 

Gridded Population Sampling and the 

Demographic and Health Surveys 

Mahmoud Elkasabi 

Ruilin Ren 

The DHS Program 

ICF 

Rockville, Maryland, USA 

September 2020 

Corresponding author: Mahmoud Elkasabi, The DHS Program, ICF, 530 Gaither Road, Suite 500, 

Rockville, MD 20850, USA; phone: +1 301-572-0484; fax: +1 301-407-6501; email: 

Mahmoud.Elkasabi@icf.com 
Editor: Diane Stoy 

Document Production: Chris Gramer 

This study was conducted with support from the United States Agency for International Development (USAID) 

through The DHS Program (#720-OAA-18C-00083). The views expressed are those of the authors and do not 

necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate population, 

health, and nutrition programs. Additional information about The DHS Program can be obtained from ICF, 530 

Gaither Road, Suite 500, Rockville, MD 20850 USA; telephone: +1 301-572-0200, fax: +1 301-572-0999, email: 

info@DHSprogram.com, Internet: www.DHSprogram.com. 

Recommended citation: 

Elkasabi, Mahmoud and Ren, Ruilin. 2020. Gridded population sampling and the Demographic and Health Surveys. 

DHS Working Papers No. 176. Rockville, Maryland, USA: ICF. 
iii 

CONTENTS 

TABLES ........................................................................................................................................................ v 

ABSTRACT ................................................................................................................................................ vii 

ACRONYMS AND ABBREVIATIONS ........................................................................................................ ix 

1 BACKGROUND ............................................................................................................................... 1 

2 SAMPLING DESIGN IN DHS SURVEYS ........................................................................................ 3 

2.1 Sampling Frame .................................................................................................................. 3 

2.2 Sampling Stages ................................................................................................................. 3 

2.3 Fieldwork ............................................................................................................................. 4 

2.4 Survey Weights ................................................................................................................... 4 

3 THE GRIDDED POPULATION SAMPLING .................................................................................... 5

3.1 Sampling Frame .................................................................................................................. 5

3.2 Sampling Stages ................................................................................................................. 5

3.3 Fieldwork ............................................................................................................................. 5

3.4 Survey Weights ................................................................................................................... 6

4 COMPARISON BETWEEN THE GRIDDED POPULATION SAMPLING AND THE 

TRADITIONAL SAMPLING DESIGN OF DHS SURVEYS ............................................................. 7

5 DISCUSSION ................................................................................................................................... 9

REFERENCES ............................................................................................................................................ 11

v 

TABLES 

Table 1 Comparison of a traditional sampling design with a gridded population 

sampling design for a DHS survey ............................................................................ 7 

vii 

ABSTRACT 

This paper addresses gridded population sampling as opposed to the traditional procedures of sampling 

for population-based household surveys. The paper explores the possibility that gridded population 

datasets could replace the traditional census frames for sampling design. To evaluate the strengths and 

weaknesses of gridded population sampling designs, this paper contrasts the major sample selection steps 

and design features of gridded population sampling to traditional area probability sampling, and compares 

a sampling design for a Demographic and Health Survey (DHS) survey as an example of a traditional area 

sample design, with a gridded population sampling design for the same survey. 

Key words: area sampling, DHS, gridded population sampling, multistage sampling design 

ix 

ACRONYMS AND ABBREVIATIONS 

DHS Demographic and Health Surveys 

EA enumeration area 

GIS geographic information system 

GPS global positioning system 

MOS measure of size 

PPES probability proportionate to estimated population size 

PPS proportional to size 

PSU primary sampling unit 

SSU secondary selection unit 

1 

1 BACKGROUND 

Demographic and Health Surveys (DHS) are nationally representative, population-based household 

surveys designed to produce a wide range of data on demographic and health topics. In all DHS surveys, 

multistage stratified probability samples are selected. In a typical DHS survey, a two-stage stratified 

sampling design is used. Census enumeration areas (EAs) are selected in the first stage as primary 

sampling units (PSUs) with probability proportional to size (PPS). In the second stage, residential 

households are randomly selected from each selected PSU after a household listing operation has 

generated an updated, complete list of residential households in the selected PSU (ICF International 

2012). In the past few years, gridded population sampling methods have been used for population-based 

household surveys in settings where census frames were either outdated or not available (Elsey et al. 

2016; Galway et al. 2012; Muñoz and Langeraar 2013; Thomson et al. 2012). Advancements in gridded 

population sampling have increased interest in adopting these methods in DHS surveys. The objective of 

this paper is to compare gridded population sampling methods with the traditional area sampling method, 

which is the standard DHS sampling approach. The paper will also address the DHS Program’s position 

on adopting methods of gridded population sampling in DHS surveys. 

Gridded population sampling is not a sampling approach that is distinct from area-based probability 

sampling. In fact, gridded population sampling designs are area probability sampling designs that are 

GIS/GPS-assisted, especially in the construction of the gridded sampling frames. The designs might also 

use GIS/GPS to operationalize the procedures required for developing a probability sample of the target 

population (Heeringa 2018). This paper addresses gridded population sampling through a comparison of 

GIS/GPS-assisted and traditional procedures. We explore the possibility that GIS/GPS-assisted gridded 

population designs could replace the traditional census frames for sampling design. To evaluate the 

strengths and weaknesses of gridded population sampling designs, this paper contrasts the major sample 

selection steps and design features of gridded population sampling to traditional area probability 

sampling. 

3 

2 SAMPLING DESIGN IN DHS SURVEYS 

2.1 Sampling Frame 

A sampling frame is a complete list of all sampling units that entirely cover the target population. A 

sampling frame allows for a probability selection of sampling units. The availability of a suitable 

sampling frame determines the feasibility of conducting a DHS. In a typical DHS survey, the list of 

enumeration areas (EAs)1

 from the most recently completed population census is the sampling frame. In 

some surveys, an existing master sample is used instead2

. The frame usually covers the entire geographic 

area of a country, without omission or overlap. Maps usually exist for each EA with clearly defined 

boundaries. Such maps are usually digitalized and supported by GIS technology. Each EA typically has a 

unique identification code or a series of codes, and at least one measure of size (MOS) estimate 

(population and/or number of households), based on the most recent census or updates from the field. 

Before using the census frames for the sampling design, the quality of the frame is examined by checking 

attributes such as frame coverage and population distribution, identification and coding, MOS and 

consistency (ICF International 2012). 

When a census frame or a master sample is not available for DHS surveys, alternative frames with full 

coverage for the target population are potential options.3

 The options are evaluated by a number of factors 

such as (1) Does the frame completely cover the target population? (2) Are the PSUs too large or too 

small for a DHS survey? (3) Are there cartographic materials that allow for the boundaries of PSUs to be 

precisely identified in the field? and (4) Are geographic/administrative variables available that can be 

used in sample stratification? 

2.2 Sampling Stages 

Most DHS surveys use a two-stage stratified sampling design, in which EAs are selected in the first stage 

with PPS selection. In the second stage, residential households are selected with equal probability from an 

updated household listing of each selected EA. The DHS samples are typically stratified by regions and 

urban/rural residence. In the first stage of sampling, EAs are selected according to the allocation of the 

EA sample over the sample strata. After the selection of the EAs, a household listing operation is 

conducted in every selected EA and a complete list of households is constructed. In the second stage, a 

fixed number of households is selected from each EA. From the selected households, all women age 15–

49 who are usual members of the household or who slept in the household the night before the survey are 

eligible for a woman’s individual survey. In all households or in a subsample of households, all men of 

reproductive age (typically age 15–49, 15–54, or 15–59) are eligible for a man’s individual survey. 

1

 An EA is a geographical area that serves as a counting unit for the population census, and is usually a city block in 

urban areas or a village in rural areas with 200-300 households. 

2

 The master sample is a representative random sample of EAs selected from the most recent completed population 

census. 

3

 In the Egypt DHS 2014, a complete list of all Shiakhas (urban administrative unit) and villages served as the 

sampling frame. In Congo (DRC), an electoral frame was used for the 2013-14 Congo (DRC) DHS. 
4 

2.3 Fieldwork 

Fieldwork begins with the household listing process that takes place between the first and second stages 

of sampling. This process is essential to the construction of a list of households in each selected EA, from 

which a sample of households can be selected at random in the second stage. Since census maps typically 

exist for each EA with clearly defined boundaries (natural boundaries such as rivers, streets, and roads), 

field enumerators use these maps to locate the selected EAs in the field. After they tour the EA and match 

the actual boundaries on the ground with the map boundaries, the enumerators begin the extensive process 

of household listing. In situations where matching the boundaries on the ground is not possible due to low 

quality maps or real geographic changes on the ground, the field workers seek help from local authorities. 

2.4 Survey Weights 

All probability sampling surveys use survey weights to guarantee the actual representativeness of the 

sample. The DHS sampling stages and procedures allow for a straightforward calculation of survey 

weights that account for all selection probabilities of the sampling units. The calculations benefit from the 

available measure of size for each EA in the census frame, and the number of listed households in each 

EA based on the household listing. 
5 

3 THE GRIDDED POPULATION SAMPLING 

3.1 Sampling Frame 

The gridded population datasets contain model-produced grid cells that are uniform in areal size and 

shape, but variable in population totals.4

 Models can either disaggregate census data and administrative 

population counts or predict population density based on small area surveys and other spatial covariates, 

such as road infrastructure and nightlight intensity. Gridded population datasets also vary in the 

geographic scale of the grid cells. For example, WorldPop offers 100 × 100 m grid cells, whereas 

LandScan offers 1 × 1 km grid cells (Quader et al. 2020; Thomson et al. 2017) Therefore, the choice of 

the gridded population datasets definitely influences the subsequent sampling stages, where small grid 

cells can be grouped into bigger sampling units or large grid cells can be segmented into smaller sampling 

units. To be used as a sampling frame, the gridded population datasets might be supplemented with other 

cartographic materials that support the sample design. For example, to clearly define the sample strata, a 

shapefile5

 of boundaries of the sample strata should be overlaid with the gridded population dataset 

(Thomson et al. 2017). 

3.2 Sampling Stages 

Similar to the traditional sampling method, gridded population sampling is a multistage sampling design, 

but with grid cells or groups of grid cells that are selected in the first stage. There are several approaches 

to first-stage sample selection and the subsequent stages. With the segmentation approach, grid cells 

(such as 1 km × 1 km) are selected with the probability proportionate to estimated population size (PPES) 

as PSUs, and then segmented into secondary selection units (SSUs) that are selected in the second stage 

of selection (Thomson et al. 2012). The segmentation process can be done by smaller grid cells or by 

manual delineation that uses satellite imagery. With the point approach, randomly generated points are 

located within grid cells where the number of points is proportional to the estimated population. A random 

sample of points are selected as PSUs centroids, and the PSU boundaries are then manually delineated 

based on satellite imagery (Muñoz and Langeraar 2013). With the growth approach, sample grid cells 

(such as 100 m × 100 m) are randomly selected with PPES. In a second step, PSUs are developed by 

including neighboring areas around selected seed cells until a minimum population threshold is achieved 

(Elsey et al. 2016). 

3.3 Fieldwork 

Several approaches have been used to identify the selected PSUs or SSUs in the field and to select 

households. When boundaries of grid cells are used as boundaries for PSUs or SSUs, identifying the 

selected sampling units in the field can be challenging (Elsey et al. 2016). Whereas, when PSUs or SSUs 

are manually delineated using satellite imagery to follow sensible boundaries such as rivers, streets, and 

4

 Different datasets are available based on the models used to construct the datasets’ grid cells and to estimate the 

population size per each cell. These include WorldPop, GPWv4, GHS-POP, GRUMP, UNEP, and Landscan. 5

 A type of file used to store the geometric location and attribute information of geographic features represented by 

points, lines, or polygons (areas). 
6 

roads, it is easy for the field teams to find the selected PSUs/SSUs and perform the household 

enumeration by using sketch or digital maps, or satellite imagery. However, such manual delineation is 

subject to human bias (Muñoz and Langernaar 2013; Thomson et al. 2012). 

Although a traditional household listing can be done, many non-traditional approaches are used with 

gridded population sampling. These include non-probability sampling approaches that are prone to 

selection bias, such as the random-walk, spin-the-pen, or other semi-probability sampling approaches 

such as selecting every tenth household (Chen et al. 2019; Elsey et al. 2016; Galway et al. 2016; Sollum 

et al. 2011). The traditional household listing has also been replaced by digitizing dwelling point locations 

in satellite imagery followed by selecting sampling points at random (Escamilla et al. 2014; Lin and 

Kuwayama 2016; Pearson et al. 2015; Siri et al. 2008; Wampler et al. 2013).

3.4 Survey Weights 

Since gridded population sampling is a multistage sampling design, survey weights can be calculated by 

considering selection probabilities from different stages (Heeringa 2018). However, the inclusion 

probabilities of the grid cells or the aggregated cells are sensitive to the size of these cells, where the 

quality of these sizes might be questionable in some gridded population datasets. The approach to 

calculation is also not clear when techniques like the growth approach are used (Thomson et al. 2017; 

Qader et al. 2020). 
7 

4 COMPARISON BETWEEN THE GRIDDED POPULATION 

SAMPLING AND THE TRADITIONAL SAMPLING DESIGN 

OF DHS SURVEYS 

Table 1 compares a sampling design for a DHS survey as an example of a traditional area sample design, 

with a gridded population sampling design for the same survey. The following criteria guide the 

comparison: (1) sampling frame; (2) sampling stages; (3) fieldwork; and (4) survey weights. With each 

criteria, we address the pros, cons, and potential challenges. 

Table 1 Comparison of a traditional sampling design with a gridded population sampling design 

for a DHS survey 

Traditional approach Gridded population sampling 

1. Sampling frame Frame of EAs from the most recent census. A gridded population dataset that covers the 

whole area land of the country. 

Coverage The sampling frame provides complete coverage 

for the whole population. 

The sampling frame provides complete coverage 

for the whole population. 

Accessibility and summary 

tables of the sampling frame 

In most of the cases, the sampler must have 

access to the census frame for checking, study, 

and production of the summary tables, which 

include necessary information for the sample 

design. 

Checking the quality of the gridded population 

datasets and producing summary tables require 

special GIS technical expertise. 

Accessibility and flexibility of 

the frame for sample 

selection 

The sampler can sort the frame properly to 

achieve the designed and implicit stratifications. 

The sampler can decide which software will be 

used for sample selection. 

Selecting the sample PSUs might require using 

several GIS tools and packages. 

Size of PSUs MOS from the last census (number of 

households) is provided for each EA, and used 

for the PPS selection of EAs as PSUs. 

Based on the actual size of the EA, large EAs 

might be segmented during the household 

listing, and small EAs might be combined with 

adjacent EAs to form bigger PSUs. 

Disaggregated administrative population counts 

are used as a measure of size for each grid cell. 

The last census is a key source of these counts. 

Grid cells might be selected as PSUs or 

aggregated in larger PSUs with appropriate size. 

Boundaries of PSUsT Most use natural landmarks and roads as 

boundaries. A census map is available for each 

EA with clearly identifiable boundaries and text 

descriptions. 

Boundaries of grid cells are mostly imaginary 

boundaries. When grid cells are aggregated into 

a larger PSU, manual delineation based on 

satellite imagery can be used to form PSUs with 

natural boundaries. Such manual delineation is 

subject to human bias. 

Urban-rural definitionT The urban-rural definition in a census frame 

reflects a country’s official definition of urban and 

rural areas. The DHS surveys use the country’s 

officially defined urban and rural areas. 

The gridded sampling frames use different 

model based urban-rural simulations. These 

may not correspond to the country’s official 

definition of urban and rural areas. 

Variables for stratificationT Stratification variables (regions by urban/rural) 

are available for each EA in the census frame, 

and/or updated regions that reflect the recent 

administrative changes. 

To define stratification variables, gridded 

population datasets might need to be 

supplemented by the boundary shapefiles of the 

strata. If accessible, such shapefiles might not 

reflect recent administrative changes. 

Special population coverageG The census frame may not be up to date for 

identification of special target populations such 

as a displaced population, a nomadic population, 

and a population that lives in slum areas. 

Based on current high resolution imagery and 

pattern recognition, the gridded population frame 

may be used to identify special target 

populations such as displaced populations, 

nomadic populations, and populations living in 

slum areas. 

Continued... 
8 

Table 1—Continued

2. Sampling stagesT When using a census EA frame, a typical twostage design sampling procedure is 

straightforward. 

Since grid cells are uniform in shape and areal 

size, the multistage design might involve more 

than two stages. The sampling procedure can be 

sophisticated, and might be labor intensive. It 

might also be difficult to control the work quality 

since some designs depend heavily on the 

surveyor’s subjective judgments. 

3. Fieldwork Fieldwork begins with the household listing 

process. 

Fieldwork begins with the household listing 

process. 

Identifying the boundariesT Boundaries are identifiable especially when 

sensible boundaries are used such as natural 

boundaries—rivers, streets, and roads. 

When imaginary boundaries of grid cells are 

used, identifying the boundaries can be 

challenging. Boundaries are identifiable if 

sensible boundaries are used. 

Household listing Household listing is a necessary if a random 

sample of households is to be selected. DHS 

uses a standard process of listing where all 

structures are listed, and a random sample of 

the listed households is selected. 

Gridded population sampling also needs a 

household listing because the satellite photos 

are not sufficiently detailed to allow a direct 

selection of households. Many non-traditional 

approaches are used with gridded population 

sampling. However, a complete household 

listing can be used and a truly random sample of 

households can be selected. 

4. Survey weightsT The sampling stages and procedures used in the 

DHS allow for a straightforward calculation for 

survey weights that account for all selection 

probabilities for all sampling stages and all 

sampling units. 

The sampling stages and selection procedures 

can be executed in a way that allows for a 

proper calculation of selection probabilities in 

each sampling stage and for each sampling unit. 

With the point approach and growth approach, 

the selection probability can be challenging 

because it depends on how the sample point 

grows and the surveyor’s subjective judgements. 

T Traditional sampling is potentially superior to gridded population sampling. 

G Gridded population sampling is potentially superior to traditional sampling. 
9 

5 DISCUSSION

Gridded population sampling methods are multistage sampling designs that are GIS/GPS assisted in the 

construction of the sampling frame. With procedures constructed for multistage sampling that are similar 

to the traditional approach, gridded population sampling is a promising approach for population surveys, 

especially when access to a census frame is limited. This paper compared the traditional method and the 

gridded sampling approach by several criteria, including the frame coverage, sampling stages, fieldwork, 

and survey weights. 

Several issues should be considered when using gridded population sampling. The following points need 

to be addressed: 

1. The accuracy of the gridded population datasets. Several gridded population datasets that were 

used as sampling frames in different circumstances are available. The accuracy of these datasets 

varies by the scale of the output grid, the complexity of models used to construct the grid cells, 

and the geographic scale and accuracy of the input census data and covariates used in the model. 

To prepare a sampling frame for a household survey, measuring and improving accuracy by 

considering all these factors might be challenging for some survey practitioners. 

2. The stratification variables. To construct a complete sampling frame with all variables needed 

for a sampling design such as the stratification variables, the gridded population datasets may 

need to be supplemented with other datasets from different sources, such as a shapefile of 

boundaries of regions or urban/rural areas. This can be a challenge for survey practitioners 

because these shapefiles might not publicly accessible, especially for the urban/rural areas. 

3. The sampling stages and the size of PSUs. The use of gridded PSUs or larger units influences 

the sampling inclusion probabilities, and also the feasibility and the protocols used in the field. In 

addition, other alternatives, such as the point approach and the growth approach, are prone to 

bias because they involve considerable subjective judgment. 

4. The boundaries of the final sampling units. When gridded PSU boundaries are used, field 

protocols should be developed and used in conjunction with high-resolution satellite imagery to 

guide fieldworkers in accurately locating the selected grids in the field. In contrast, when 

boundaries are manually delineated to follow sensible boundaries related to features in the real 

world, fieldwork becomes more feasible but is more labor intensive. 

5. Resources and technical expertise and skills. Designing and implementing a gridded population 

sampling design require access to technical expertise, implementation skills, and technology. 

These might affect the cost and timeline of the survey, and should be considered and properly 

evaluated before proceeding with the design. 

6. Survey weights. A multistage sampling design must be constructed in a way that would allow for 

a straightforward calculation of the overall selection probabilities and survey weights. An area 

gridded population sampling, together with a standard household listing, will outperform the 

point and growth approaches. 
10 

Gridded population sampling is a potential alternative for a DHS survey when a census frame is not 

available in a country. In such situations, the gridded population sampling should be assessed in 

conjunction with other alternative sampling frames available in the country. In this assessment, in addition 

to the six points mentioned above, legality and justifiability6

 should be considered. 

6 The DHS Program does not decide which sampling frame to use for a survey. It is the host country that decides 

which sampling frame is to be used. If a census frame is available, the host country may not accept another sampling 

frame other than the last population census frame, even if that census frame is old.
11 

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