Spatial Poverty Assessments
Alex de Sherbinin
Senior Staff Associate
Center for International Earth Science Information Network (CIESIN)
The Earth Institute at Columbia University
Deputy Manager
NASA Socioeconomic Data and Applications Center (SEDAC)
GEOSS Science & Technology Stakeholder Workshop
30 August 2012
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
2
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)
Spatial Poverty Data
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.
Spatializing Demographic and Health
Survey Data
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.
Global Hunger Map
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
2
1
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
Spatial Error Model Results
(pseudo-r square)
IMR Map
IMR (2000)
High : 208
Low : 2.0
•
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
–
–
–
–
Source: de Sherbinin et al. AGU 2004.
•
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
8,029 units (6,886 in Brazil and Mexico alone)
– Subnational IMR values
77 countries have subnational data; 115 national only
adjusted to be consistent with
80% of world population has subnational data
national UNICEF 2000 IMR
Average 14 units per country (outside Brazil and Mexico)
values
Growing Season
Growing
Season
(days)
High : 365
Low : 0
Analysis for non-wealthy countries only
IMR
IMR by Growing Season (days)
Cumulative Population by Growing Season (days)
36
5
60
-8
9
90
-1
19
12
014
9
15
017
9
18
020
9
33
036
4
24
029
9
30
036
5
0
159
kn
o
un
36
5
60
-8
9
90
-1
19
12
014
9
15
017
9
18
020
9
33
036
4
24
029
9
30
036
5
159
0
w
n
kn
o
un
w
n
100
90
80
70
60
50
40
30
20
10
0
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Elevation
Elevation
High
Low
Analysis for non-wealthy countries only
IMR
Cumulative Population by Elevation (meters)
IMR by Elevation (meters)
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
100
90
80
70
60
50
40
30
20
10
0
unknown
0-50
50-100
100-500
500-1000
1000-
unknown
0-50
50-100
100-500
500-1000
1000-
Malaria
Malaria
Transmission
Index
High : 37
Low : 0
Analysis for non-wealthy countries only
IMR
IMR by Malaria Risk Score
Cumulative Population (billions) by Malaria Risk Score
100
90
80
70
60
50
40
30
20
10
0
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
0-.08
.08-3.4
3.4-8.4
8.4-37
0
0-.08
.08-3.4
3.4-8.4
8.4-37
Soils
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
Analysis for non-wealthy countries only
7. 100 % severe constraints
IMR by Soil Constraint Class
Cum ulative Population by Soil Constraint Class
(1=few constraints; 7=severe constraints)
100
90
80
70
60
50
40
30
20
10
0
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Unknow n
1-3
4
5
6
7
Unknown
1-3
4
5
6
7
Drought
Drought Index
High :
Low :
Analysis for non-wealthy countries only
IMR
IMR by Drought Index
Cumulative Population by Drought Index
100
90
80
70
60
50
40
30
20
10
0
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1
2
3
4
5
6
7
8
9
10
11
1
2
3
4
5
6
7
8
9
10
11
Rails
% of Grid
within 2km
of Railroad
High : 99
Low : 0
Analysis for non-wealthy countries only
IMR
Cumulative Population (billions) by Rail Density
IMR by Rail Density
100
90
80
70
60
50
40
30
20
10
0
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
0-5
5-15
15-23
23-100
0
0-5
5-15
15-23
23-100
Ports
Distance
to nearest
port
High
Low
Analysis for non-wealthy countries only
IMR
Cumulative Population by Nearest Port (decimal degrees)
IMR by Nearest Port (decimal degrees)
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
100
90
80
70
60
50
40
30
20
10
0
0-.5
.5-1
1-2
2-3
3-4
4-5
5-7
7-10
10-15
15-24
0-.5
.5-1
1-2
2-3
3-4
4-5
5-7
7-10
10-15
15-24
Compared with the non-poor, poor people are more likely to be found in
drought-prone areas with shorter growing seasons
Non-poor
Poor
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.
Millennium Ecosystem Assessment,
2005
Not Poor
Somewhat Poor Moderately Poor
Poor
Extremely Poor
The poor are at
much greater risk
of experiencing a
drought
Poverty
Delhi, India: Multiple Deprivation Index and
ASTER Nighttime Thermal Infrared
Nighttime Temp
MDI / Poverty
Nighttime Temp
Temperature
Houston, Texas: Income Level and
MODIS Nighttime Thermal Infrared
Income PC
Income PC
Nighttime Temp
Remote Sensing Applications for Poverty
Research
Night-time Lights Estimates of GDP /
Population
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
Dar Es Salaam, Tanzania, 1982 and 2002
Source: Data courtesy of Richard Sliuzas, ITC
Neighborhood Mapping
Damascus, Syria
•Unstructured
Settlements
•Lowest to lower
middle income
•Rural migrants
•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
Source: Slide courtesy Eddie Bright, ORNL
Settlement characterization tool
Source: Slide courtesy Eddie Bright, ORNL
Source: Lela Prashad, www.nijel.org
Creating a Human “Observing System”
Source: www.benwilhelmi.com
Frequency of Demographic and Health
Surveys
Subnational Poverty and Extreme Poverty
Prevalence
Source: Harvest Choice, http://harvestchoice.org/maps/sub-national-povertyand-extreme-poverty-prevalence
Mean Number of Censuses 1970-2010
Migration Data
Migration is one of the main demographic
drivers of environmental change, yet there
are very few data on human movements
Source: Adamo, CODATA side event, Rio+20, June 2012
Source: Adamo, CODATA side event, Rio+20, June 2012
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