There is great variation in urban-rural. poverty. differentials globally, but, at a national scale, urban dwellers are generally better off.

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1 Integration of Poverty and Remote Sensing Data Alex de Sherbinin Socioeconomic Data and Applications Center (SEDAC) CIESIN, The Earth Institute at Columbia University Expert Group Meeting on Slum Identification Using Geo-Information Technology May 2008, Enschede, Netherlands

2 Presentation Outline Measurement of poverty and well-being Advantages of high resolution poverty data Sources of urban poverty data Statistical issues Research examples

3 Measurement of Poverty and Well-Being

4 Current State of Global Poverty? Close to 1 billion people (1/5 world s population) lives on less than $1 day (estimate is million as of 2004) Good News: Some parts of the world are on track (East Asia) or have met (China) MDG 1 target for halving percentage of population living on less than $1 day. Bad news: Others are at severely risk of failing (Sub- Saharan Africa).

5 ty Poverty as % of Rural Povert Urban Urban poverty higher than rural poverty Urban poverty lower than rural poverty Bolivia Nepal Lesotho Ecuador Guatemala Ghana Algeria Tanzania Yemen Mauritania Vietnam Brazil Tunisia Philippines China Mali Morocco Kyrgyz Laos Nicaragua PDR Rep Bangladesh Dominican Rep. Zimbabwe Burkina Rwanda Faso Colombia Panama Ethiopia Malawi India Niger Costa Pakistan Honduras Cambodia Kazakhstan El Salvador Rica Romainia Paraguay Benin Sierra Leone Thailand Zambia Madagascar Peru Kenya Gambia Georgia Mongolia Burundi Egypt Chad Mozambique Nigeria Rural poverty There is great variation in urban-rural poverty differentials globally, but, at a national scale, urban dwellers are generally better off. Still, there are spatial pockets of poverty within urban areas

6 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, persons or more), in a 10-country West Africa study Balk, D., T. Pullum, A. Storeygard, F. Greenwell, and M. Neuman A Spatial Analysis of Childhood Mortality in West Africa. Population, Space and Place, Vol. 10, No. 3.

7 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)

8 The Advantages of Higher Resolution Socioeconomic Data

9 Why are high spatial resolution socioeconomic data desirable? More efficient policies, strategies, and resource allocation Disaggregated g data can help to identify highest priority areas If high priority units are clustered, the strategy will be different than if they are spread out Improved data for research purposes (understanding the correlates of poverty) Can utilize high resolution poverty data in combination with high resolution biophysical, infrastructure (roads, health/education facilities), or other data

10 6 billion persons (2000, UN)

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12 Data Set Construction Obtained percent of children underweight* from DHS and MICS surveys Match survey data to boundary data 377 sub-national units (SNUs) * Children are defined as underweight if their weight-for-age z-scores are below minus two standard deviations (-2 SD) from the median of the NCHS/CDC/WHO International Reference Population.

13 Small Area Estimates of Poverty

14 Census Tract Level Data

15 The urban-rural poverty differential that applies to a country as whole is not necessarily reflected uniformly across all urbanrural gradients within the country

16 In Bangladesh, the pattern of poverty rates is primarily shaped by proximity to the capital Dhaka. In this map we can see that in general, poverty rates rise as one moves increasingly far from Dhaka. We can also see how the coastal remote areas are less disadvantaged than the inland remote areas. For the country as whole, the urban areas tend to be less poor than their rural counterparts.

17 This map compares poverty gaps in the capital, Llongwe, and Blantyre. Both cities are comparable in population (approximately a half million). Llongwe has far 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.

18 Ha Noi Non-Uniformity Size of Units, Across Countries Kampala Kampala km km 0 12 km Intra-urban definition in data No intra urban information available

19 One national poverty line with information on urban and rural from survey respondents Viet Nam: Urban and Rural Poverty Rates Based on a National Poverty Line 0 Poverty He eadcount Index Rural Poverty Headcount Index Urban Poverty Headcount Index

20 Separate urban and rural poverty lines Kenya: Urban and Rural Poverty Rates Based on Distinct Urban and Rural Poverty Lines 0 Poverty He eadcount Index Rural Poverty Headcount Index Urban Poverty Headcount Index

21 Urban Poverty Data Sources

22 Other sources of information on Censuses urban populations Household surveys (DHS, MICS, LSMS) Blended d model linking surveys and censuses (small area estimation techniques)

23 Census vs. Survey PROS PROS Results can be representative for small geographic areas CONS Costly to implement Decennial Limited number of questions Relatively lower costs Annual / every few years Can cover larger number or more focused set of issues CONS Representative only for larger geographic g areas

24 Census Data - Varies widely in quality and detail US includes over 1 million geographic units; avg size of unit is less than 1km 2 South Africa includes over 83,000 geographic units; avg size of unit is less than 1 km 2 Indonesia, close to 70,000 geographic units; avg size of unit is about 5 km 2 Chad includes 14 geographic units; avg size of unit is 298 km 2 Saudi Arabia includes 13 geographic units; avg size of unit is 386 km 2

25 Some countries e.g., e.g., Brazil, Mexico, China have data at moderate-high resolution IMR and other data (e.g., e 0 ) based on censuses These countries tend not to have current DHS/MICS surveys. New methods should be flexible enough to think about data poor and data-wealthier regions.

26 Survey Data Strengths, Weaknesses Flexibility can focus the survey questions specific a particular research interest HIV/AIDS, beliefs etc. Generalizable with good survey design, can get data from sample and apply information to the larger population. Can be implemented more often and cheaper than full census. Dependent on response rate may have inherent bias due to self selection Comparability different sets of questions may or may not be comparable between study areas. Translation to different languages may lead to different meanings

27 Demographic and Health Surveys Number of city-regions DHS Countries (n=54, MICS Countries (n=22, surveyed since1990) All Africa MICS 2) Zimbabwe 1 The Gambia Egypt These DHS surveys have city modules with higher sampling. Good boundary files are not necessarily available for all urban areas

28 DHS clusters + GRUMP Colored footprints t are GRUMP urban extents Black dots are DHS clusters Clusters average 30 households Potential for grouping into urban areas, by size of area, has some promise

29 SAE Methodology (1) The methodology of small area estimation involves imputing into population census which does not have consumption data a a measure of per capita consumption from household survey data Relies on statistical relationships between een common variables in census and survey data Detailed description in Elbers et al. (2003), Econometrica.

30 SAE Methodology (2) Three Stages: zero stage: : establish comparability of data sources; identify common variables; understand sampling strategy. First stage: : estimate model of consumption in the household survey based on common variables. second stage: : take parameter estimates to census, predict consumption. Use this to estimate aggregate poverty and inequality indicators for small areas.

31 Statistical Issues

32 Spatial Statistics Spatial characteristics of the dataset are important to take into account when building a model Standard OLS regression assumptions include: Normality Independence Homoskedasticity Independence d among observations is generally violated when building statistical models for spatial data sets

33 Spatial Autocorrelation (SA) The extent to which an occurrence of an event constrains or makes more likely an event in a a neighboring unit Like serial autocorrelation (in time series data), the events are not independent, and thus violates Gauss- Markov assumptions* Estimated coefficients are biased and inconsistent Residuals/Standard Errors are artificially deflated leading to type I errors (inproper rejection of null hypothesis) * According to Lembo (undated): If the observations are spatially clustered in some way, the estimates t obtained from the correlation coefficient i or OLS estimator t will be biased and overly precise. They are biased because the areas with higher concentration of events will have a greater impact on the model estimate and they will overestimate precision because, since events tend to be concentrated, there is actually a fewer number of independent observations than are being assumed.

34 Example of test for spatial randomness Negative autocorrelation Positive autocorrelation Moran's I is a weighted correlation coefficient used to detect departures from spatial randomness. Ranges from -1 to +1.

35 Research Examples

36 Data integration: inputs Survey data: Other attributes Census data: Population (Age Structure Other attributes) GPW Spatial data: Administrative boundary files Remotely sensed: -Vegetation -Lights -Built-up area past present future

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40 Poverty, Health & Land Cover in Accra, Ghana Study used: Census data at tract level Women s health survey data Remotely sensed data Source: Weeks et al Intra-urban differentials in poverty and health in Accra, Ghana. Paper presented at the IUSSP International Population Conference, July 2005.

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42 Poverty & Health: Low Correlation

43 Poverty & Vegetation Cover: Higher Correlation

44 Stow et al. (2007) used QuickBird imagery to delineate residential land use polygons and identify the socioeconomic status of neighborhoods in Accra. Using two different approaches yielded residential land-use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery. D. Stow, A. Lopez, C. Lippitt, S. Hinton, and J. Weeks. (2007). Object-based classification of residential land use within Accra, Ghana based on QuickBird satellite data. International Journal of Remote Sensing, Volume 28, Issue 22 January 2007, pages

45 Exploratory Analysis in Hanoi, Vietnam

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47 Head Count Index vs. Avg. NDVI

48 Poverty NDVI

49 In greater Hanoi, poverty levels are positively correlated with greenness

50 Next Steps (1) Look at astronaut night-time time photos lights in relation to intra-urban socioeconomic levels (hypothesis: slum areas will have inadequate lighting) Identify green space locations in relation to socio-economic status (hypothesis: slum areas will be farther from green spaces) Thermal band information in relation to poverty (hypothesis: slum areas will have higher temperatures)

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53 Next Steps (2) Use SRTM slope data to identify low income areas that have high slopes and therefore risk of landslides, or that are vulnerable to SLR Use high resolution data to identify health threats such as garbage tips, water courses, etc., in relation to density of poor populations

54 Thank you! For data visit: