Global Programme on Evidence for Health Policy Mapping Poverty: Predicting Income using the LandScan Database Workshop on Gridding Population Data CIESIN,

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Presentation transcript:

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 Structure Where is GPE ? Health Systems and Community Health Communicable Diseases Sustainable Development and Healthy Environments Health Technology and Pharmaceuticals Non- communicable Diseases and Mental Health Evidence and Information for Policy External Relations and Governing Bodies General Management Cabinet Director-General Link 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 GPE Objectives 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 PopulationPoverty 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,

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 Income The National Level

Global Programme on Evidence for Health Policy Country level 1 admin level 2 admin level Predicting Income The 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 All rights reserved

Global Programme on Evidence for Health Policy Predicting Income The Sub-national Level 2 R 2 : 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 Income Actual 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 Income The 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.