Using Multi-Source Nighttime Lights Data to Proxy for County-Level Economic Activity in China from 2012 to 2019

Xiaoxuan Zhang,John Gibson

doi:10.3390/rs14051282

Abstract

The use of nighttime lights (NTL) data to proxy for local economic activity is well established in remote sensing and other disciplines. Validation studies comparing NTL data with traditional economic indicators, such as Gross Domestic Product (GDP), underpin this usage in applied studies. Yet the most widely cited validation studies do not use the latest NTL data products, may not distinguish between time-series and cross-sectional uses of NTL data, and usually are for aggregated units, such as nation-states or the first sub-national level, yet applied studies increasingly focus on smaller and lower-level spatial units. To provide more updated and disaggregated validation results, this study examines relationships between GDP and NTL data for 2657 county-level units in China, observed each year from 2012 to 2019. The NTL data used were from three sources: the Defense Meteorological Satellite Program (DMSP), whose time series was recently extended to 2019; and two sets of Visible Infrared Imaging Radiometer Suite (VIIRS) data products. The first set of VIIRS products is the recently released version 2 (V.2 VNL) annual composites, and the second is the NASA Black Marble annual composites. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of economic activity changes over time, and also considered different levels of spatial aggregation.

Highlights

The use of nighttime lights (NTL) data to proxy for local economic activity is well established in remote sensing and other disciplines
Contrasts were made between cross-sectional predictions for Gross Domestic Product (GDP) differences between areas and time-series predictions of economic activity changes over time, and considered different levels of spatial aggregation
The weighted average of the snowfree and snow-covered all-angles annual composites from Black Marble has the highest correlation with GDP, at 0.74, with the V.2

Summary

Introduction

The use of nighttime lights (NTL) data to proxy for local economic activity is well established in remote sensing and other disciplines. Studies focused on cross-sectional comparisons of nations and sub-national regions, but more recent studies use NTL data to track changes in economic activity These fluctuations may be due to natural disasters, such as earthquakes, floods, hurricanes, and tsunami [14–18]; public health crises, such as COVID-19 [19–21]; or to various economic policies [22,23]. Extant evidence is that changes in nighttime lights data poorly predict temporal changes in economic variables despite NTL data being good cross-sectional predictors of differences in economic activity across space within these same studies [25,26] These studies with negative findings on the performance of NTL data as a proxy for temporal changes in economic activity use Defense Meteorological Satellite Program (DMSP) data

Methods

Results

Discussion

Conclusion