geo-sampling: from design to implementation...rti international primary sampling units within each...
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RTI International
RTI International is a trade name of Research Triangle Institute. www.rti.org
Geo-Sampling:
From Design to Implementation
Safaa Amer, PhD
AAPOR
May 14-17, 2015
Hollywood, Florida
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RTI International
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– Outdated census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
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