1. Estimation of GDP at Sub-National Scales using Nighttime Satellite Imagery Paul Sutton Department of Geography University of Denver April, 2005 Presentation for Association of American Geographers Conference Denver, Colorado

2. Outline Nationally Integrated Nighttime Imagery has some correlation with National GDP figuresNationally Integrated Nighttime Imagery has some correlation with National GDP figures Linear allocation of GDP to Light Intensity on a Pixel by Pixel basis produces an un-validatable map of GDP at 1 km resolution….Linear allocation of GDP to Light Intensity on a Pixel by Pixel basis produces an un-validatable map of GDP at 1 km resolution…. Sub-national GDP figures Compared with Sub-National integration of Nighttime ImagerySub-national GDP figures Compared with Sub-National integration of Nighttime Imagery “Regional Parameters”“Regional Parameters” A Cheeze-Ball Statistical Technique? A Cheeze-Ball Statistical Technique? Future Research… (aka Back to the Drawing Board )Future Research… (aka Back to the Drawing Board )

3. Scatterplot of Night Light Energy & PPP of GDP for 208 nations

4. Global map of Marketed Economic Activity as measured by Nighttime Satellite Image Proxy

5. Motivation Question: Why estimate Known GDP values with Nighttime Satellite Imagery? Answer #1: Because it would be interesting if it is possible to do. Answer #2: If these methods are developed they may provide estimates of GDP in nations with large informal economies (e.g. Mexico?) Answer #3: Also, they may provide independent estimates of economic activity in places where ‘official’ numbers are suspect

6. Data and The “Cheeze-Ball” Statistics Trick: “Regional Parameters” Applied to China, Turkey, U.S., and India (China example below) Dummy Code the “Region” parameter derived from Residuals (definitely cheating); However, then apply the derived regression parameters do a different TIME.

7. Apply Non-Linear Areal Extent vs. Population rule for cities to GDP estimates… Light ‘Blobs’ are Population ‘Blobs’ are Money (GDP) ‘Blobs’ Why not model them that way?

8. Regression Model Development for China (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

9. Regression Model Application for China (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

10. Urban Pop Model Application for China (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

11. Regression Model Development for Turkey (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

12. Regression Model Application for Turkey (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

13. Urban Pop Model Application for Turkey (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

14. Regression Model Development for India (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

15. Regression Model Application for India (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

16. Urban Pop Model Application for India (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

17. Regression Model Development for United States (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

18. Regression Model Application for United States (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

19. Urban Pop Model Application for United States (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

20. Aggregate Estimates of National GDP by Summation of State/Canton estimates Note: GDP numbers all in different currencies

21. Discussion These methods do not work well enough for much of anything yet…. “Learn by Losing”…….. In Turkey Istanbul was underestimated and Ankara was over-estimated (Primate & Capitol City effects?) Ankara 41% Too High (capitol city effect?) Istanbul 53% Too Low (primate city effect?)

22. Conclusions… Future Avenues of Investigation…. Interpret the residuals and use that information. Convert all GDP data to gold standard currency like perhaps the EURO. Increase the “Dummy Coding” (Is this legit?) Want to work on this? Send me an e-mail, I’ll send you the data: psutton@du.edu

23. Acknowledgements Without Chris Elvidge at NOAA’s NGDC there might not be any DMSP OLS derived data products. I am working with Chris Elvidge and others to develop better estimators of GDP using this imagery and other relevant information. Sharolyn Anderson and others with Grid command of doom: Zoldor = int(exp((float(floatnat30int) / 1000) + (float(nat30slope) / 1000) * (float(lnclustarea30) / 1000) / float(clustarea30))