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Spatial Poverty Assessments
Alex de SherbininSenior Staff Associate
Center for International Earth Science Information Network (CIESIN) The Earth Institute at Columbia University
Deputy ManagerNASA Socioeconomic Data and Applications Center (SEDAC)
GEOSS Science & Technology Stakeholder Workshop30 August 2012
2
NASA Socioeconomic Data & Applications Center (SEDAC)
• Focus on human dimensions of environmental change
• Integration of social and Earth science data, especially with remote sensing
• Direct support to scientists, applied and operational users, decision makers, and policy communities
• Strong links to geospatial data community
Outline
• Spatial poverty data• Remote sensing for poverty research• Creating a human “observing system”• Concluding Thoughts
Measures of Well Being
• Household income/consumption expenditures • Non-monetary indicators of well being
– Malnutrition – Unsatisfied Basic Needs– Infant Morality Rates
• Foster, Greer, Thorbecke measures: – Percent of population below the poverty line (Head Count Index;
FGT_0)– Average shortfall between welfare levels and the poverty line
measured as a percent of the poverty line (Poverty Gap Index; FGT_1)
Why Map Poverty?
• Understand spatial patterns and how poverty varies subnationally and across countries
• Identify hotspots in need of intervention• Understand the spatial correlates of poverty
– Biophysical correlates– Socioeconomic correlates– Spatial isolation or “poverty traps”
In South Africa, the urban-rural poverty differential that applies to the country as whole is not necessarily reflected uniformly across all urban-rural gradients within the country
In Bangladesh, the pattern of poverty rates is primarily shaped by proximity to the capital Dhaka. Poverty rates rise with distance from Dhaka. Coastal areas are less disadvantaged than the inland remote areas.
In Malawi, Llongwe has less poverty within its limits, but is surrounded by regions of very high poverty. Blantyre, by contrast, has very high poverty within its limits, but is surrounded by regions of only moderate poverty.
Analysis of infant and child mortality
For both infant and children, the chances of survival decrease monotonically the further one resides from a city (of 50,000 persons or more), in a 10-country West Africa study
Balk, D., T. Pullum, A. Storeygard, F. Greenwell, and M. Neuman. 2004. A Spatial Analysis of Childhood Mortality in West Africa. Population, Space and Place, Vol. 10, No. 3.
Identification of Hunger Hotspots• Defined by the
Millennium Development Project Hunger TF as those sub-national units with rates of childhood malnutrition >20% and >100,000 children who are underweight
• 75 sub-national units met this criteria
Hunger by Farming Systems
1
2
3
Farming Systems Data Source: Dixon, J., A. Gulliver with D. Gibbon. 2001. Farming Systems and Poverty: Improving Farmers’ Livelihoods in a Changing World. United Nations Food and Agriculture Organization. (Available at http://www.fao.org/farmingsystems/).
What are the biophysical correlates
of malnutrition?
de Sherbinin. 2009. “The Biophysical and Geographical Correlates of Child Malnutrition in Africa” Population, Space and Place Vol.15
IMR Map
High : 208
Low : 2.0
IMR (2000)
• Sources – Demographic and Health Surveys (41 countries)– Multiple Indicator Cluster Surveys (5 countries)– National Human Development Reports (14 countries)– National Statistical Offices (16 countries)– UNICEF Childinfo – (115 countries)
• Subnational representation– 8,029 units (6,886 in Brazil and Mexico alone)– 77 countries have subnational data; 115 national only– 80% of world population has subnational data– Average 14 units per country (outside Brazil and Mexico)
• Converting rates to counts– For each subnational unit,
estimates of live births, infant deaths calculated based on gridded population, national fertility data, and subnational IMR.
• Calibration– Subnational IMR values
adjusted to be consistent with national UNICEF 2000 IMR values
Source: de Sherbinin et al. AGU 2004.
IMR
Growing Season
Growing Season (days)
High : 365
Low : 0
Cumulative Population by Growing Season (days)
0.00.51.01.52.02.53.03.54.04.55.0
IMR by Growing Season (days)
0102030405060708090
100
Analysis for non-wealthy countries only
Elevation
IMR
Elevation
IMR by Elevation (meters)
0102030405060708090
100
unknown 0-50 50-100 100-500 500-1000 1000-
Cumulative Population by Elevation (meters)
0.00.51.01.52.02.53.03.54.04.55.0
unknown 0-50 50-100 100-500 500-1000 1000-
High
Low
Analysis for non-wealthy countries only
Malaria
IMRCumulative Population (billions) by Malaria Risk Score
0.00.51.01.52.02.53.03.54.04.55.0
0 0-.08 .08-3.4 3.4-8.4 8.4-37
IMR by Malaria Risk Score
0102030405060708090
100
0 0-.08 .08-3.4 3.4-8.4 8.4-37
Malaria Transmission Index
High : 37
Low : 0
Analysis for non-wealthy countries only
Soils
Cumulative Population by Soil Constraint Class(1=few constraints; 7=severe constraints)
0.00.51.01.52.02.53.03.54.04.55.0
Unknown 1-3 4 5 6 7
IMR by Soil Constraint Class
01020
3040506070
8090
100
Unknown 1-3 4 5 6 7
Soil Constraints On Agricultural Production
Undefined
1. No constraints
2. 1-20 Slight
3. 20-40 Moderate
4. 40-60 Constraints
5. 60-80 Severe
6. 80-99 Very Severe
7. 100 % severe constraints
Analysis for non-wealthy countries only
Drought
IMR by Drought Index
0102030405060708090
100
1 2 3 4 5 6 7 8 9 10 11
High :
Low :
Drought Index
IMRCumulative Population by Drought Index
0.00.51.01.52.02.53.03.54.04.55.0
1 2 3 4 5 6 7 8 9 10 11
Analysis for non-wealthy countries only
Rails
High : 99
Low : 0
% of Grid within 2km of Railroad
Cumulative Population (billions) by Rail Density
0.00.51.01.52.02.53.03.54.04.55.0
0 0-5 5-15 15-23 23-100
IMR by Rail Density
0102030405060708090
100
0 0-5 5-15 15-23 23-100
IMRAnalysis for non-wealthy countries only
Ports
IMR
Distance to nearest port
High
Low
Cumulative Population by Nearest Port (decimal degrees)
0.00.51.01.52.02.53.03.54.04.55.0
0-.5 .5-1 1-2 2-3 3-4 4-5 5-7 7-10 10-15 15-24
IMR by Nearest Port (decimal degrees)
0102030405060708090
100
0-.5 .5-1 1-2 2-3 3-4 4-5 5-7 7-10 10-15 15-24
Analysis for non-wealthy countries only
Compared with the non-poor, poor people are more likely to be found in drought-prone areas with shorter growing seasons
Non-poor Poor
Millennium Ecosystem Assessment, 2005
For the Millennium Ecosystem Assessment CIESIN calculated average IMR within each MA ecosystem. We also calculated another measure of well-being, the ratio of the share of world population to share of world GDP. The drylands are the most disadvantaged. We further calculated rates of population growth within each ecosystem unit, and noted that the drylands had the highest rate of growth. To have fragile ecosystems with low levels of well-being experience the highest population growth is bound to make challenges more difficult in these regions.
The poor are at much greater risk of experiencing a drought
Not Poor Somewhat Poor Moderately Poor Poor Extremely Poor
Delhi, India: Multiple Deprivation Index and ASTER Nighttime Thermal Infrared
Nighttime Temp
Pov
ert
y
Nighttime TempMDI / Poverty
Houston, Texas: Income Level and MODIS Nighttime Thermal Infrared
Income PC
Te
mp
era
ture
Income PC
Nighttime Temp
Comparison of HH Assets Index and Wealth Based on Mean Brightness of NTL
Source: Noor et al., Population Health Metrics, 2008
http://www.ciesin.columbia.edu/confluence/display/slummap/Global+Slum+Mapping
Damascus, Syria
•Very loosely structured•Historical ethnic quarters/neighborhoods•Poor residents currently being displaced in some areas with urban development/tourism
•Formal Urban Planning•Typical Urban Services•Middle to Upper Income
•Unstructured Settlements•Lowest to lower middle income•Rural migrants
Neighborhood Mapping
Source: Slide courtesy Eddie Bright, ORNL
Subnational Poverty and Extreme Poverty Prevalence
Source: Harvest Choice, http://harvestchoice.org/maps/sub-national-poverty-and-extreme-poverty-prevalence
Migration Data
Migration is one of the main demographic drivers of environmental change, yet there are very few data on human movements
Concluding Thoughts
• There is a growing availability of spatial poverty data, but– gaps remain
• Integration of “bottom up” with “top down” data is possible, but – Development of globally integrated and harmonized subnational SE data is costly – It needs to be driven by specific research or decision making needs– One size fits all approaches for web services are unlikely to work
• Growth in novel data sources – anonymized mobile phone records for human movement, crowd sourced data, etc. – are exciting developments, but
– as yet have not provided a globally consistent view– Data quality issues may exist