Module 1: Special Topics in Household Surveys...

Module 1: Special Topics in

Household Surveys

Georeferencing

Siobhan Murray and Sydney Gourlay

March 6, 2014

Georeferencing: why

• survey management (visit verification, logistics)

• minimal additional cost and time, although need to invest

some extra effort in training and data management

• enable integration with large and expanding pool of spatial

datasets (LSMS-ISA geovariables)

• provide objective measurement of spatial attributes such as

area, path/distance

Georeferencing: why

Objective vs. farmer self-reported measurement of plot area

Clear evidence of heaping/loss of precision in self-reported measurements!

Can have a significant impact on estimates of yield and productivity

Georeferencing: what

• Location variables (enumeration area, city, district, region..),

which can be linked to administrative level boundaries, even

EA in some cases.

• GPS position in specified coordinate system, defined by

1. cs type (spherical vs projected)

2. projection parameters (epsg.org)

3. units

4. datum


Latitude and longitude are spherical coordinates measuring,

respectively, angle above the equator or relative to prime

meridian. The length of a degree of longitude varies based

on latitude, but one degree at the equator = 111 km, so

decimals are significant!

Standard map projections are made onto planar, conic or

cylindrical surfaces. Each type preserves different

properties (area, shape, distance) and requires different sets

of parameters. Units are often meters or feet, so decimals

are less important. (EPSG reference)

Datum refers to a combination of earth model (sphere or

ellipsoid) and the position of the spheroid relative to the

center of the earth.


Numbers ≠ Location if coordinate system is not well defined..

DD MM.mmm DD.ddddd Meters

UTM 37

Meters

UTM 36

Meters

UTM 38

All

Post QC

GPS: how does it work

• With a minimum of 4 satellites in view, a GPS unit can calculate

horizontal (X, Y) and vertical (Z) coordinates, using precise time

measurement included in the signals to determine distance from

each satellite, and overlapping spheres to identify position

(trilateration).

• ideal arrangement of

constellation

• dilution of precision

indicators

• GPS + GLONASS = 50

satellites

GPS: sources of error

Source Effect Typical Error in Meters

Satellite Orbit errors 2.1

Clock bias 2.1

Signal Propagation Ionospheric refraction 4.0

Tropospheric refraction 0.7

Receiver Receiver Noise 0.5

Multipath 1.4

Data collection, entry, managment…

Can be significant offset Can be minimized

GPS Implementation

1. Equipment

2. Questionnaire

3. Training

4. Data Management

• Data entry

• Review & cleaning

5. Final Preparation & Dissemination

Equipment

Consider device features and “fitness for purpose”

• Battery life: plan for back-up batteries, vehicle chargers, power

conservation

• Interface: USB, serial, wireless.

• Track log: storage capacity (expansion option) and

downloading

• Camera

• Area Calculation

• Vertical (Z) coordinates

Equipment

Technical specs on accuracy (5-10 m for recreational, mapping

grade LSMS-ISA surveys) refer to absolute measures.

Accuracy of relative measures (length, area) affected by

• feature (plot, track) size

• walking speed

• device frequency settings

*ongoing research…LSMS-ISA Land Area Measurement

experiments

Simple concept, but still need to put some thought into

designing GPS section of questionnaire:

• What coordinate system will you use? -if UTM does study area cover multiple zones?

-are there negative coordinates in your study area? (N/S, +/-)

• What units will be used? -if degrees, then DD.ddddd, DD MM.mmm, DD MM SS?

-clearly indicate precision/number of decimals expected

• How do coordinates look on the unit chosen for survey? -consistency with device in order of inputs

• if very high accuracy required, record DOP indicators, walking

speed, potential interference (canopy, other structures)

Questionnaire

Questionnaire

Coordinate precision & accuracy (DD):

decimal places

degrees N/S or E/W at equator

E/W at 23N/S

E/W at 45N/S

E/W at 67N/S

0 1.0 111.32 km 102.47 km 78.71 km 43.496 km

1 0.1 11.132 km 10.247 km 7.871 km 4.3496 km

2 0.01 1.1132 km 1.0247 km .7871 km .43496 km

3 0.001 111.32 m 102.47 m 78.71 m 43.496 m

4 0.0001 11.132 m 10.247 m 7.871 m 4.3496 m

5 0.00001 1.1132 m 1.0247 m .7871 m .43496 m

6 0.000001 111.32 mm 102.47 mm 78.71 mm 43.496 mm

7 0.0000001 11.132 mm 10.247 mm 7.871 mm 4.3496 mm

8 0.00000001 1.1132 mm 1.0247 mm .7871 mm .43496 mm

8. WHAT ARE THE GPS COORDINATES OF THE DWELLING?

LATITUDE (N) LONGITUDE (E)

__ __ O

__ __ . __ __ __ __ __ __ O

__ __ . __ __ __

5. ENUMERATOR: RECORD THE COORDINATES FOR THE CENTER POINT OF THE

[PLOT]. SAVE LABEL AS HOUSEHOLD ID AND PLOT ID

LONGITUDE

(E)

LATITUDE

(N) PLOT LABEL

_ _ _ _ ° _ _ . _ _ _ _ _ _ _ ° _ _ . _ _ _

_ _ _ _ ° _ _ . _ _ _ _ _ _ _ ° _ _ . _ _ _

Check for consistency between modules in same survey

Household Qx:

Agriculture Qx:

Questionnaire Example 1

Latitude and longitude values

were reversed in

approximately 16% of

recorded plot locations.

Reversal of lat / lon values is

not easily detected and/or

fixed in areas where these

values can be equal.


Check for consistency between interviewer manual and questionnaire

GPS Coordinates

ParcelID

36N

UTM

The manual states:

“GPS setup should be:

POSITION FRMT:

hddd.dddddo

MAP DATUM: WGS 84

-----------------------------------

Take note of the (N) North

and (E) East coordinates. If

the GPS is correctly setup,

the co-ordinates should

appear on the screen as

decimal degrees with the

following format:

N 00.00000o

E 000.00000o”

2005

2009


2005 GPS locations, interpreted as

decimal degrees

no points below the equator

2009 GPS locations, interpreted as

decimal degrees


Training

• Training and manuals should provide general guidance for

operators on data collection and device maintenance

1. GPS unit setup (CS, units, frequency)

2. wait time for signal acquisition

3. walking speed, perimeter definition for area measurement (start and

closure, check sketch)

4. monitor battery status

• Additional instructions for saving and downloading raw GPS

data (supervisor task)

• Don’t underestimate time required to introduce GPS data

collection. Plan for at least one day of training, including field

practice (even in the parking lot!)

Training - use screen captures from device

How to capture a waypoint. 1. Check that you have enough satellites!!

2. Go to Main Menu

3. Go to Mark

4. Go to Top Field

5. Enter the ID CODE

for the HOUSEHOLD

- Use “Enter / Rocker Key”

6. Record the GPS Coordinate

in the questionnaire.

7. Select “OK”

200901

Training – be specific on process

IHS3:

To get the area, walk the

plot and record the area.

IHPS:

Walk the plot, record the

area and save the track.

Training – be specific on filenaming

PLOT NAME:

Rainy Season Plot: HOUSEHOLD + LOWEST IHS3 PRESENT

HOUSEHOLD MEMBER ID FROM ROSTER

200901R1

CURRENT PLOT

NUMBER

Training – stress important messages

Important!

• GPS units must all be set up to measure location and area in the same format

Meters (not feet)

Acres (not Meters or Hectares)

• Once you set up the GPS unit, make sure never to change the settings

• Periodically check that the GPS unit is set up correctly

Data Management: Data entry

Different ways to record data:

1. Manual data-entry (paper) in field or at HQ

2. GPS raw data: avoids confusion associated with GPS settings,

as well as data-entry errors, but requires extra effort for

• track, waypoint naming convention that ensures link to household or

other survey records

• downloading protocol to account for limits in device storage

• conversion to standard format (open source tools GPS Babel, DNR

Garmin & GPSU, or proprietary TerraSync, Waypoint Manager)

3. Automatic import to CAPI from internal receiver or by USB

or wireless connection device

• Range constraints imposed in data-entry application can eliminate large coordinate errors (wrong/missing digit in degrees, field reversal). If done before leaving EA, opportunity to recollect..

DISTRICT MIN_X MIN_Y MAX_X MAX_Y

Mzuzu City 33.95292 -11.50042 34.07311 -11.36122

Ntchisi 33.69843 -13.53524 34.12824 -13.01336

Nkhata Bay 33.73531 -12.23483 34.32634 -10.99384

Nkhotakota 33.75265 -13.50490 34.34611 -12.16290

Nsanje 32.97952 -17.12500 35.31261 -9.36754


To implement distance calculation in data-entry application, with input in decimal degrees:

1. conversion to planar coordinates

OGP Geomatics Guidance Note number 7: Coordinate Conversions and Transformations including Formulas (www.epsg.org)

2. great-circle distance

a = sin²(Δφ/2) + cos(φ1).cos(φ2).sin²(Δλ/2) c = 2.atan2(√a, √(1−a)) d = R.c where φ is latitude, λ is longitude, R is earth’s radius (mean radius = 6,371km)

3. degrees-to-meters lookup table by latitude Simplest solution, relatively accurate for very small distances


• Reliable coordinates carried forward enable panel verification across waves.. is this same plot visited in previous wave?

DIST METHOD DIST_KM

Coordinate conversion 0.02526

Great Circle 0.02539

Latitude lookup 0.02542


• Data entry and validation could also include internal consistency checks. Is distance from household to plots within a reasonable range? Or households within expected range of village centerpoint?

HHID PLOTNUM DIST_KM

123456 M2 1.9

123456 M3 2.5

123456 M4 1.3

123456 M1 75.2


Rigorous data validation checks implemented in data-entry will hopefully minimize data cleaning required.

Simple filters can be used to generate a summary table of records

for review: field name value description records

units (identified based on woreda extent)

DD.ddddd coordinate system = lat/lon, units = decimal degrees 48

DD MM.mmm coordinate system = lat/lon, units = degrees and minutes 338

Meters utm36 coordinate system = UTM36N, units = meters 418



ll_switch 1 fields reversed, hh_saq25_a approx equal to lon of woreda center & hh_saq25_b approx equal to lat of woreda center 3857

ll_blank 1 missing or zero value in fields hh_saq25_a or hh_saq25_b 55

dups 1 other records with identical values in fields hh_saq25_a and hh_saq25_b 38

Data Management: Data review

Very obvious errors can be fixed in record-level review, but any

changes should be tracked in separate variable (indirect

measure of coordinate reliability)

field name value description

qa_type 1

hh is less than 10km from EA central feature and the coordinates are unique

2 hh is less than 10km from EA central feature but the coordinates are duplicates

3 hh is less than 10km from EA central feature but the coordinates were modified during review (excluding lat-long switches)

4 hh is more than 10km from EA central feature but the hh moved between visits

5 hh is more than 10km from EA central feature

Data Management: Data review

Raw data is a valuable output. Some post-processing steps possible

only with access to raw GPS data..

In Afar Livestock Survey, sample sites were

generated around randomly selected

points, and sometimes fell in very rough

and remote terrain. Enumerators were

unable to access the whole site. As a

result, weights had to be adjusted based on

the extent of site covered.

• Estimates of site coverage were generated

in GIS using visibility analysis based on a

terrain model and stored GPS tracks.

Data Management: Post-processing

Number of

locations greatly

expanded in 100%

tracking scenario…

Use GPS and digital boundaries

to generate new location

variables where missing (ward,

district, urban & rural strata)

Data Management: Panel tracking

• Household GPS coordinates are confidential information, but

location information can be valuable for end-users

• For LSMS-ISA surveys we disseminate modified EA center-

points, offset to prevent identification of communities AND a

set of geovariables generated using the actual household

locations

• In order to preserve anonymity of households and individuals

we adopted method used by MeasureDHS progam, relies

primarily on random offset of cluster coordinates within a

specified range determined by population density

Dissemination

Household locations (red

squares) are averaged by EA (red

dot), and random displacement

applied to produce a public

version of the location (blue dot).

Blue circle represents

the known range of

uncertainty (5 km

radius of offset for 99%

of dataset).

Dissemination

theme variable

distance distance household to plot

distance household to paved road

distance household to major market (if available)

climatology annual mean temperature

mean temperature of wettest quarter

mean temperature of driest quarter

annual precipitation

precipitation of wettest quarter

precipitation of driest quarter

precipitation seasonality (CV)

landscape typology land cover class

agro-ecological zone

soil & terrain elevation

slope class

topographic wetness index

landscape-level soil characteristics

crop season (temporal) short-term average crop season rainfall total

specific crop season rainfall total

short-term average NDVI crop season aggregates

specific crop season NDVI crop season aggregates

Dissemination

Dissemination: spatial variables

Household distance to major road (km)

How does remoteness affect agricultural productivity and welfare at

the household level? Distance to major road or town is one

simple way to measure remoteness. It can be an indicator of

access to markets and services.

Household distance to nearest town (km)

Dissemination: spatial variables 2011 rainfall as percent of normal

Rainfall (mm)

1 25 50 100 150 200 250 450

Time-series data on rainfall, temperature

(GDD) and vegetation (NDVI) allow us

to describe the survey season in terms

of deviation from normal, to capture

both the timing of onset as well as total

amount of rainfall received in the

agricultural season.

Dissemination: sample distribution across

spatial classes

Sample households

0500

10001500200025003000

10 20 30 40 50 60 70 80 90100

> 100

Distance to rd (km)

Aridity zones

Aggregates can be derived

from weighted sample

variables using spatial classes

not included in sample

design, but are not

necessarily representative.

If intent is to study a

particular population

(“remote”, “arid”) may need

to include spatial strata in

survey design.

0500

100015002000250030003500

arid semi-arid dry sub-

humid

other

Ethiopia Case Study

The study focuses on two key agricultural inputs, namely land and soil, and one key output, namely maize production.

In order to improve the quality of data collected on these key inputs and output, we test and validate methodologies with respect to:

1) Land Area

2) Maize production

3) Soil Fertility

Not a nationally representative survey.

Methodological Study: Ethiopia

GIS in Sampling: Ethiopia Site Selection

Zone # Dega # W. Dega # Kolla Total EAs

East Wellega 0 25 15 40

West Arsi 14 25 0 39

Borena 0 17 21 38

14 67 36 117

Below is the TOTAL NUMBER of AgSS EAs in each agroecology:

Zone # Dega # W. Dega # Kolla Total EAs

East Wellega 0 17 11 28

West Arsi 14 14 0 28

Borena 0 13 16 29

14 44 27 85

The determined PRACTICAL ALLOCATION of EAs across administrative and

agroecological zones:

Elevation Rainfall AEZ

GIS in Sampling: Ethiopia Site Selection

Field Area Measurement: Ethiopia

Method Area

(Acres)

Median

(Acres)

Std.

Deviation

GPS 0.375 0.188 0.512

Compass &

Rope 0.370 0.180 0.505

N=1790

• 1790 fields measured with GPS, compass and rope, and

farmer estimate

• Using Garmin eTrex 30

• Precision indicators collected include number of satellites

acquired, weather condition, slope, and compass and rope

closure error.

0

.00

5.0

1.0

15

Ave

rag

e B

ias (

GP

S -

CR

)

0 2 4 6 8 10

Deciles of CR Area

(GPS - CR Area, Acres)

Absolute Bias

0

.02

.04

.06

.08

.1

Ave

rag

e R

ela

tive

Bia

s

0 2 4 6 8 10Deciles of CR Area

(GPS - CR)/CR Area

Relative Bias

0

.02

.04

.06

.08


| GPS - CR Area | GPS - CR Area

(GPS - CR Area, Acres)

Absolute Bias

0.1

.2.3

.4


| GPS - CR | / CR (GPS - CR) / CR

(GPS - CR)/CR Area

Relative Bias

Field Area Measurement: Ethiopia (Cont.)

N=1790

From Ethiopia Methodological Land and Soil Survey, 2013-14

Field Area Measurement: Ethiopia (Cont.)

Method Area

(Acres)

Median

(Acres)

GPS 0.449 0.234

Compass &

Rope 0.441 0.234

Android

Tablet 0.423 0.231

N=236

• Slower acquisition time leads to smaller measurements

• Used external app “Distance and area measurement” by

Kristofer Bjornson. Selected for user friendly interface.

On a subsample of fields, an android tablet was also used for

field area measurement.

Ethiopia, Methodological Land

and Soil Survey 2013-14

• Equipment: Garmin eTrex 30

– High storage capacity (200 saved

tracks)

– Area calculation

– Approx. $300/unit

• Enumerator pool:

– Experienced Enumerators

– Bachelor’s Degree

– Computer/SmartPhone

experience

• Training needs:

– 0.5 – 1 day with little follow up

Zanzibar, Cassava Production

Measurement Experiment

• Equipment: Garmin eTrex 30

– User-friendly interface (so we

thought…)

– Area calculation

– Approx. $300/unit

• Enumerator pool:

– Ag. Extension Officers

– Generally older

– Very low/no computer

experience

• Training needs:

– 2+ days with follow-up

instruction during supervision

visits

GPS in the Field: Case Study Comparison

GPS vs. Farmer Self-Reported: – “Fact or Artefact: The Impact of Measurement Errors on the Farm Size-Productivity

Relationship”

• Gero Carletto, Sara Savastano, and Alberto Zezza

• World Bank Policy Research Working Paper 5908

– “Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements”

• Talip Kilic, Alberto Zezza, Gero Carletto, and Sara Savastano

• Forthcoming…

Upcoming Methodological Validation Experiments: GPS, Compass & Rope, and Farmer Estimates

– Zanzibar: with validation of measurement of continuously harvested crops (cassava)

– Nigeria: on subsample of LSMS-ISA households

– Ethiopia: with validation of soil fertility and production measurements

References – LSMS-ISA

References

• "Using the global positioning system in household surveys for better economics and better policy," Policy Research Working Paper Series 4195, The World Bank. Gibson, John & McKenzie, David, 2007.

• Putting People on the Map: Protecting Confidentiality with Linked Social-Spatial Data. National Research Council. (2007).

• Incorporating Geographic Information Into Demographic and Health Surveys: A Field Guide to GPS Data Collection. MeasureDHS

• LSMS website – manuals and other survey documentation

GPS Resources

• GPS Babel (http://www.gpsbabel.org/)

• GPSU (http://www.gpsu.co.uk/)

• DNR Garmin

(http://www.dnr.state.mn.us/mis/gis/tools/arcview/extensions/

DNRGarmin/DNRGarmin.html)

• proprietary software:

GarminWaypoint Manager,

Trimble TerraSync, etc

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