Night-Time Light Data: A Good Proxy Measure for Economic Activity?

Abstract

Much research has suggested that night-time light (NTL) can be used as a proxy for a number of variables, including urbanization, density, and economic growth. As governments around the world either collect census data infrequently or are scaling back the amount of detail collected, alternate sources of population and economic information like NTL are being considered. But, just how close is the statistical relationship between NTL and economic activity at a fine-grained geographical level? This paper uses a combination of correlation analysis and geographically weighted regressions in order to examine if light can function as a proxy for economic activities at a finer level. We use a fine-grained geo-coded residential and industrial full sample micro-data set for Sweden, and match it with both radiance and saturated light emissions. We find that the correlation between NTL and economic activity is strong enough to make it a relatively good proxy for population and establishment density, but the correlation is weaker in relation to wages. In general, we find a stronger relation between light and density values, than with light and total values. We also find a closer connection between radiance light and economic activity, than with saturated light. Further, we find the link between light and economic activity, especially estimated by wages, to be slightly overestimated in large urban areas and underestimated in rural areas.

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

“…The differences in radiance at each pixel location were then calculated. Nightlight intensity was considered a driver variable because it is strongly correlated with economic activity and the gross domestic product (GDP) [58,59]. Changes in nightlight intensity over time can also be considered an indicator of economic growth.…”

Night-time lights: A global, long term look at links to socio-economic trends

“…Over this period, a great deal of topics has been explored, at various spatial scales. At finer geographical scales, for example, Mellander et al [3] have had success in using DMSP as a proxy for certain indicators in Sweden (e.g. population, establishment density); many similar analyses have been done for other regions [4,5,6,7,8,9].…”

Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China

“…Different methods, such as threshold techniques, have been employed by previous researchers to overcome the overglow effects and to increase the accuracy of the GDP estimation based on DMSP/OLS NTL images [22,25,26]. Moreover, the saturation problem is also severe when GDP-like measures are derived by using NTL, which can result in the poorer correlation using nighttime light for centrally-planned economies [14,24,27].…”