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Spatial Poverty Assessments

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)

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Spatial Poverty Assessments

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  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. 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 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/).

  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. High : 208 Low : 2.0 IMR Map • 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) IMR (2000) 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 • Subnational IMR values adjusted to be consistent with national UNICEF 2000 IMR values

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

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

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

  21. 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 Soils Soil Constraints On Agricultural Production Analysis for non-wealthy countries only

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

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

  24. IMR High Low Ports Distance to nearest port 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. 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

  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 Poverty Nighttime Temp Nighttime Temp MDI / Poverty

  29. Houston, Texas: Income Level and MODIS Nighttime Thermal Infrared Temperature Income PC 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+Mappinghttp://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. 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

  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-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

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

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

  45. 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

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