Global Programme on Evidence for Health Policy

Mapping Poverty:Predicting Income using the

LandScan Database

Workshop on Gridding Population Data

CIESIN, New York

2-3 May 2000

Global Programme on Evidence for Health Policy

WHO StructureWhere is GPE ?

Health Systemsand Community

Health

CommunicableDiseases

SustainableDevelopmentand Healthy

Environments

HealthTechnology andPharmaceuticals

Non-communicable Diseases and

Mental Health

Evidenceand Information

for Policy

External Relationsand Governing

Bodies

GeneralManagement

Cabinet

Director-GeneralLink to

Regional Directors

Global Programme on Evidence for Health Policy

Mission of GPE

Strengthen the scientific and ethical foundations for evidence-based

policy formulation

Global Programme on Evidence for Health Policy

• Thematic mapping

GIS in GPEObjectives

• Risk mapping

Global Programme on Evidence for Health Policy

Definition of Risk

UNDRO (1991): Mitigating Natural Disasters. Phenomena, Effects and Options. A manual for Policy Makers and Planners, 164p. United Nations

Risk = Hazard * Element at Risk * Vulnerability

Specific Hazard Population Poverty

Risk Mapping

Global Programme on Evidence for Health Policy

Predicting Income

Elvidge C.D., Baugh K.E., Kihn E.A., Kroehl H.W., Davis E.R. and Davis C.W. (1997): Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. Int. J. Remote Sensing, Vol. 18, N° 6, 1373-1379

Global Programme on Evidence for Health Policy

Night time light

Population

Predicting Income

The 1998 LandScan Database

Global Programme on Evidence for Health Policy

Predicting IncomeThe National Level

All the data (138 countries) y = 0.8752x + 7.0575

R2 = 0.8159

456789

1011121314

0 1 2 3 4 5 6 7

log (nbr of pix, 1998)

log

(G

DP

pp

p, 1

997)

Global Programme on Evidence for Health Policy

Country level

1 admin level

2 admin level

Predicting IncomeThe Sub-national Level

The boundaries and names shown and the designations used on this map do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement.

© WHO 2000. All rights reserved

Global Programme on Evidence for Health Policy

Predicting IncomeThe Sub-national Level

Country Nbr of Units RMexico 31 0.11USA 51 0.24Thailand 75 0.37Indonesia 26 0.47South Africa 8 0.57Russia 72 0.69

2

R2 : linear relationship between the total number of cells and the GDP ppp values on a log-log scale

Global Programme on Evidence for Health Policy

Predicting IncomeActual State

At the country level the correlation between light and income has now been confirmed for 138 countries;

At the 1 administrative level the first results indicate that the distribution of light is not significant to obtain good quantitative estimates for the distribution of income;

At the 1 administrative level distribution of light is a good qualitative parameter for the estimation of income.

Global Programme on Evidence for Health Policy

Predicting IncomeThe Next Steps

1) Collect GDP ppp data for the first and second administrative

level;

2) Test different combination of parameters and correction factors, taking light into account, to improve the quantitative estimation of income at the sub national level;

3) Apply the resulting model(s) for countries where we do not

have detailed income information;

4) Use the resulting map within risk mapping models.