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Geo-Sampling:

From Design to Implementation

Safaa Amer, PhD

AAPOR

May 14-17, 2015

Hollywood, Florida

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Acknowledgment

RTI Team

– Lisa Thalji - Charles Lau

– Karol Krotki - William Wheaton

– Jamie Ridenhour - James Cajka

– Cynthia Augustine - Mark Bruhn

– Jennifer Unangst - Justine Allpress

– Larry Campbell

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Overview

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Study

– The main objective of the study is to identify barriers to internet

access in developing countries

– Electronic Data collection using Tablets and Handhelds

Countries Covered

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Sampling in Developing Countries

Sample design in developing countries is difficult due to:

– Lack of complete frame

– Outdated census

– Inaccurate population estimates

Need to find ways to implement random probability

based sample selection and avoid selection bias

– Move away from random walks

– Move away from sampling without a listing/re-listing exercise

– Represent full population across different geographic location,

urban/rural, poverty levels, gender, age

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What is Geo-Sampling?

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• A complex sampling design supported by Geographic

information Systems

• Multi-stage Sampling Design with Stratification and Clustering• Step 1: Two way stratification by Region and Poverty level to select

states/counties

• Step 2: Stratification by Urban/Rural using proportional allocation to

select Districts with Probability Proportional to Size

• Step 3: Randomly select units (PSUs) with Probability Proportional to

Size

• Step 4: Randomly select units (SSUs) and cover all households within

the SSU

• Step 5: Select respondent within households – adult age 15 to 64

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Constraints

• Exclusions are accounted for due to security or

remoteness/low density population – Less than 5% of the

total population

• Targeted sample size 3000 households

• 50 Districts are selected using probability proportional to

size (PPS) where population is the measure of size

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Primary Sampling Units

Within each District:

– A 1 Km2 grid is laid over the entire district where each Km2 within

the grid representing a PSU

– Landscan data provides an estimate of population per Km2

LandScan is a worldwide raster dataset of modeled population estimates

which is updated on a regular basis (annual)

– PSUs are sampled then inspected for residential dwellings. PSUs

without residential dwellings are excluded

Note; an oversample of double the number of the needed PSUs is

selected to account for non-residential exclusions.

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Primary Sampling units

– 3 main PSUs and two spare PSUs are randomly selected from

each district within the residential PSUs using PPS

– 150 main PSUs Average of 20 households per PSU targeted

to achieve 3000 households

Note: For ease of reference to field staff, although the District are considered

the primary sampling units, we are referring to the 1 Km2 as PSU.

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Secondary Sampling Units

Within each PSU:

– Another grid is overlaid on top of each sampled 1 km2 PSU.

– The size of the new grid cells is:

100 m2 for rural areas

50 m2 for urban areas

– Each cell inspected to identify if it contains any residential

dwellings. Cells without residential dwellings are excluded.

– 2 main SSU and 2 spare SSUs are randomly selected

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Secondary Sampling Units

– All households within an SSU are interviewed emphasize to

interviewers that this is not a quota sample

– All residential dwellings with a main entrance within the SSU are

included in the sample and a census of all households within

these dwellings is included in the sample.

– For buildings with more than 5 floors, interviewers are instructed

to flip a coin to either go to odd or even floors.

– A clear definition of what is considered a household was provided

to interviewers to avoid confusion

– Selection of adult within the household was automated using

CAPI once the household roster was completed.10

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Grid Overlay

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Geo-Sampling Logistical Steps

Compile Frame

Tailor Design

Test Maps

Adapt

Sample PSUs & Maps

Sample SSUs & Maps

Monitor & Adjust Sample

Sampling Weights

Weight Adjustments

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Maps in Support of Sampling

Levels of maps– Country

– District

– PSU

– SSU

Maps are used for:– Putting a field plan / planning trajectory

– Getting to the targeted locations using roadways and landmarks

from maps

– Establishing the boundaries of the SSUs

– Aerial maps used to help identify major landmarks or terrain.

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District map

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PSU Maps

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SSU Maps

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Benefits of Electronic Data Collection

CAPI use facilitated use of Geo-Sampling

– Maps uploaded on tablets

– Use of GPS for orientation

– Use of Google maps

– Capture of geo-location for verification and reduce

potential for curb stoning

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Challenges In Implementation

Changes in administrative divisions since last census

Lack of poverty mapping

Urban/rural assignment not accessible

Sample Monitoring during field and achieving the

targeted sample size

GIS information inconsistent with census information

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Challenges in Implementation

Satellite images incomplete, outdated, or unavailable

Satellite image resolution low and captures only rooftops

– Difficult to determine if structure is a business, group quarters, vacant,

controlled access, etc.

– Environmental changes (landslides, etc.) and new buildings not

captured

– GPS accuracy varies across countries

– Detailed rural road network not available in majority of cases with

accessibility issues due to elevation and natural blocks (e.g. ravines)19

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Next Steps

Overlay different GIS layers on maps (elevation, rivers

network, cover, etc.)

Continue to refine the sample techniques using data

from satellite and Google Street View

Improve the screening filtering of PSUs and SSUs for

residential

Refine weights and improve design to reduce clustering

and design effect

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Related Papers & Presentations

Drone Assisted Sampling

ESRA –

– Geo-Sampling: Geospatial Grid Based Sampling in Developing

Nations

JSM –

– Geo-Sampling: Improvements using GIS Layers and Spatial Data

– Geo-Sampling Weights and Design Effect

– Geo-Sampling & Meta-Analysis : A Data Management Model for

Multinational Surveys

Geo-Sampling: Design Impact on Analysis and Variance

Estimation

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More Information

Safaa Amer

samer@rti.org

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