I got an email tip on this article from voxeu.org which has some relevance to the work done by the surfacestations.org project in that it shows clearly the impact of urbanization. While Hansen et al (GISS) uses “nightlights” in the USA to gauge “urbanness” of a station’s surroundings, they only use one source image from the Defense Meteorological Satellite Program from 1995. You can do the same yourself in Google Earth. Clearly from this example, GISS should be updating that source image if they are to get anything remotely representative of a true measure of urbanization around a climate station
This image is also illustrative to what Dr. Pielke Sr. and others have been saying for sometime. Impacts of Land use and land cover change can affect the surface temperature measurement environment over time and should be considered in any assesment of local/regional climate trends.
Of course the most striking evidence of economic growth measured by nightlights comes from this DMSP image of North and South Korea:
h/t to WUWT reader and climate blogger Warren Meyer for the voxeu.org link. – Anthony
GDP data is often poorly measured, especially for sub-Saharan Africa. This column shows that satellite data on lights at night can be used to enhance the quality of GDP growth measures. Using rainfall and satellite data, it also shows that growth of immediate agricultural hinterland of a sub-Saharan city spurs growth of the city.
GDP growth is poorly measured for many countries (Johnson, Larson, Papageorgiou, and Subramanian, 2009) and rarely measured for cities at all. The Penn World Tables rank countries by the quality of their GDP and price data, with grades A-D. Almost all sub-Saharan African countries get a grade of C or D, to be interpreted roughly as a 30% or 40% margin of error (Deaton and Heston, 2008). Given the low quality of GDP measures for countries and the almost total absence of GDP measures for sub-national units such as cities, we propose a readily available proxy: satellite data on lights at night. The best use of lights data is to examine growth in GDP rather than GDP levels, so that cross-country differences in how lights spatially and culturally reflect consumption are differenced out.
We start by examining cross-country GDP growth rates, focusing on the period 1992-2003, and develop a statistical framework for optimally combining the growth in lights measure for each country with estimates of GDP growth from the World Development Indicators. We first establish that changes in lights are well related to particular positive or negative economic growth episodes for particular regions and times and, more generally, that growth in lights is a good predictor of growth in GDP measures. As an illustration (Elvidge et al, 2005), Figure 1 contrasts the big increase in lights from 1992 to 2002 in the Eastern European countries of Poland, Hungary, and Romania with the distinct dimming of lights to the east in the former Soviet Republics of Moldova and the Ukraine, which endured a harsh transition process.
Figure 1. Eastern Europe in lights
Next, we develop a framework to optimally combine measured GDP growth with growth in lights to obtain a best estimate of true GDP growth. The objective is to minimise the variance of true GDP growth from its best estimate. The weights placed on the World Bank GDP growth measure and the lights growth measure depend in part on the ratio of signal to total variance in the World Bank measure.
Applying our method to the countries given a data quality grade D in the Penn World Tables, we get estimates of true GDP growth that are starkly different from conventional measures. We assume the World Bank measures have a signal to total variance ratio of 0.75. This is likely to be conservative since grade D countries are expected to GDP levels measured with a 40% margin of error. As examples of the application, for the Democratic Republic of Congo, lights suggest a 2.4% annual growth rate in GDP, while official estimates suggest a -2.6% growth over the same time period. The Congo seems to be growing a lot faster than official estimates suggest. At the other extreme, Myanmar has an official growth rate of 8.6% a year, but the lights data imply only a 3.4% annual growth rate. Combining the two measures using the hypothesised signal to total variance ratio, the true growth rate estimates for Congo and Myanmar are 0.08% and 4.6% for 1992-2003.
Finally, we turn to a long standing debate in developing countries about whether growth of the immediate agricultural hinterland of a city spurs growth of the city. We use annual rainfall data for the hinterlands of 541 cities in sub-Saharan Africa as our exogenous source of agricultural growth. We find that increases in rainfall have big positive effects on city growth as measured by changes in night lights, confirming the casual impression that African cities and towns are heavily dependent on the economic health of their immediate hinterlands. Lights in a given year are affected not just by rain in the same year but also by rain in the previous two or even three years. Not surprisingly the effects are smaller for the primate cities of a country which are less dependent on their agricultural hinterlands. But overall city growth is closely connected to local hinterland growth.
Deaton, Angus and Alan Heston. 2008. “Understanding PPPs and PPP-based National Accounts.” NBER Working Paper 14499.
Elvidge, Christopher D., Kimberley E. Baugh, Jeffrey M. Safran, Benjamin T. Tuttle, Ara T. Howard, Patrick J. Hayes, and Edward H. Erwin. 2005. “Preliminary Results From Nighttime Lights Change Detection.” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(8).
Henderson, J. Vernon, Adam Storeygard, and David N. Weil (2009). “Measuring Economic Growth from Outer Space.” NBER Working Paper 15199.
Johnson, Simon, William Larson, Chris Papageorgiou, and Arvind Subramanian. 2009. “Is Newer Better? The Penn World Table Revisions and the Cross-Country Growth Literature.“ Unpublished.