Global Mapping Resources: Insights from Spatial Analysis & Exploration of Data

1. Global Mapping Resources: Insights from Spatial Analysis & Exploration of Data Deborah Balk Baruch College, School of Public Affairs & CUNY Institute for Demographic Research 25 March 2008 2nd Annual Census Workshop Series, Baruch College

2. Population Distribution • 15 years of progress • Counts  Models • More than just population distribution • Urbanization • Mortality • Other

3. Evolution in global collection of population and poverty data More attention to global scope More attention to comparability More attention to problem-oriented science More attention to spatial frameworks

4. GlobalPopulation Distribution http://sedac.ciesin.columbia.edu/gpw

5. Two views of Population Distribution (2000): Density estimates at the National level vs. 2.5’ grid Spatial data: Drilling down to finer resolution

6. Population Counts (gridded) http://sedac.ciesin.columbia.edu/gpw

7. http://sedac.ciesin.columbia.edu/gpw

8. http://sedac.ciesin.columbia.edu/gpw

9. http://sedac.ciesin.columbia.edu/gpw

10. Score card on global data

11. The US Census in International Perspective

13. MEASURE DHS GPS Data Availability Slide courtesy of Livia Montana, Harvard University data available from http://www.measuredhs.com/ October 20, 2005

14. What makes a GIS special? • Data Visualization • Data Exploration • Data Integration • Data Analysis • Service provision, public & constituency participation

15. Visualization • The organization of spatial information is different than that of tabular data. • That organization is often intrinsically visual • Identification of neighbors • Construction of neighborhoods • Identification of factors that share characteristics • Cites that are situated on a coast, along a river, etc

16. IMR Afghanistan168 Australia5 Brazil 33 Cambodia 97 Cameroon 95 China 30 … Zimbabwe 78 In some scholarly traditions, the world is not only flat but also alphabetized.

17. Subnational underweight database also available (sparser coverage) www.ciesin.columbia.edu/povmap

18. Data exploration Example courtesy of Professor Juliana Maantay, Lehman College, CUNY

19. “Layers” of GIS Information Municipalities

20. “Layers” of GIS Information Census Tracts

21. “Layers” of GIS Information Lakes and Rivers

22. “Layers” of GIS Information Polluting Companies

23. “Layers” of GIS Information Schools

24. Exploration  Identification

25. Human Settlements: Rendered as Points

26. Human Settlements: Render with spatial form or “Polygons” • Note the variety of shape • Spatial location of large and small cities • Form conveys much more than points

27. Integration • Overlay or combine units in a spatial framework to produce estimates or analysis • School buffers (in above example) • Cities and coastal flooding

28. Calculations based on spatial overlays All data are gridded Vietnam Cambodia Ho Chi Minh City + low elevation coastal buffer + urban extent boundaries Administrative Boundaries Urban population and coastal flooding

29. Which country has the greatest number of persons living at risk of coastal flooding? But, countries with the highest % of their populations in the zone include the populous deltaic countries and islands.

30. Integration  Analysis • Poverty (derived from econometric model for subnational units) • + • Elevation (derived from satellites, measured on a contiguous grid)

31. Urban areas are centers of population & more affluent Ecuador: Poverty Rate • High-poverty parroquias: • are numerous • more spatially distributed • of much lower population densities

32. Ecuador: + Elevation • Not all of Ecuador's poorest parroquias are found at high elevations, but there is a strong association: • Of the low-poverty parroquias, no non-urban ones are found at elevations above 2000 meters • In contrast, of the high-poverty parroquias almost half are found at elevations above 2000 meters, and nearly two-thirds are above 1000 meters. • In reaching the poor, account for access associated with elevation. www.ciesin.columbia.edu/povmap

33. Analysis • Spatial • Characteristics & patterns associated with • Distance • Spatial relationships (e.g., neighbors) • Spatial correspondence (i.e., to other factors) • Non-spatial based on spatial integration • Analysis of omitted variables • May result in maps or tables, or both • May be “descriptive” or “analytic”

34. Same pattern in Asia • Largest cities tend to be near coasts • Elevation overlay show that they also tend to be in low lying areas

35. Final remark: Confidentiality • Already a concern with information is collected from survey or census respondents • Investigators and practitioners are ethically obligated to maintain respondent confidentiality • Geocoding may increase the difficulty in so doing

36. Frequency of cluster size(ordered by cluster ID number below) • Ranges from 2 to 36 persons per cluster

38. US Census data is an excellent model • There are increasingly diverse and high quality data being produced & distributed throughout the world • In rich and poor countries alike • Though coverage and consistency remain barriers to global coverage for many variables of interest • Using international data does not alter responsibility to standards, such as maintaining confidentiality