1. 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

2. 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

3. Outline Spatial poverty data Remote sensing for poverty research Creating a human “observing system” Concluding Thoughts

4. 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)

5. Spatial Poverty Data

6. 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”

7. 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

8. 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.

9. 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.

10. Spatializing Demographic and Health Survey Data

11. 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.

12. Global Hunger Map

13. 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

14. 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/)./

15. 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

16. Spatial Error Model Results (pseudo-r square)

17. 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.

18. IMR Growing Season Growing Season (days) High : 365 Low : 0 Analysis for non-wealthy countries only

19. Elevation IMR Elevation High Low Analysis for non-wealthy countries only

20. Malaria IMR Malaria Transmission Index High : 37 Low : 0 Analysis for non-wealthy countries only

21. 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 7. 100 % severe constraints Analysis for non-wealthy countries only

22. Drought High : Low : Drought Index IMR Analysis for non-wealthy countries only

23. Rails High : 99 Low : 0 % of Grid within 2km of Railroad IMR Analysis for non-wealthy countries only

24. Ports IMR Distance to nearest port High Low Analysis for non-wealthy countries only

25. Compared with the non-poor, poor people are more likely to be found in drought-prone areas with shorter growing seasons Non-poor Poor

26. 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.

27. The poor are at much greater risk of experiencing a drought Not Poor Somewhat Poor Moderately Poor Poor Extremely Poor

28. Delhi, India: Multiple Deprivation Index and ASTER Nighttime Thermal Infrared Nighttime Temp Poverty Nighttime Temp MDI / Poverty

29. Houston, Texas: Income Level and MODIS Nighttime Thermal Infrared Income PC Temperature Income PC Nighttime Temp

30. Remote Sensing Applications for Poverty Research

31. Night-time Lights Estimates of GDP / Population

32. Comparison of HH Assets Index and Wealth Based on Mean Brightness of NTL Source: Noor et al., Population Health Metrics, 2008

33. http://www.ciesin.columbia.edu/confluence/display/slummap/Global+Slum+Mapping

34. Dar Es Salaam, Tanzania, 1982 and 2002 Source: Data courtesy of Richard Sliuzas, ITC

35. 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

36. Settlement characterization tool Source: Slide courtesy Eddie Bright, ORNL

37. Source: Lela Prashad, www.nijel.org

38. Creating a Human “Observing System” Source: www.benwilhelmi.com

39. Frequency of Demographic and Health Surveys

40. Subnational Poverty and Extreme Poverty Prevalence Source: Harvest Choice, http://harvestchoice.org/maps/sub-national-poverty- and-extreme-poverty-prevalencehttp://harvestchoice.org/maps/sub-national-poverty- and-extreme-poverty-prevalence

41. Mean Number of Censuses 1970-2010

42. Migration Data Migration is one of the main demographic drivers of environmental change, yet there are very few data on human movements

44. Source: Adamo, CODATA side event, Rio+20, June 2012

46. 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