Night light directly reflects the local level of industrialization and urbanization on the one hand; on the other hand, it can also partially reflect the concentration of population. At the same time, according to the brightness of night light on the surface, it can characterize the intensity of human activities to a certain extent. Long-term series of night light data are of great reference value for urban development research.

  Currently, night light data are mainly used for urban expansion research, estimation of socioeconomic factors, and other fields such as environment, disasters, fisheries, and energy.

• Urban expansion monitoring: The world is in a period of rapid development today, and the urban area range provided by traditional statistical methods often changes before it can take effect. Night light images that reflect the characteristics of human activities take towns as the target area, providing an accurate, direct, economical, and time-saving method for urban expansion monitoring.

• Estimation of socioeconomic factors: The contrast between bright and dark areas on light images makes it a powerful tool for studying intensive human activities and their impacts. Obviously, there is a positive correlation between light intensity and socioeconomic factors. This quantitative relationship can be used to carry out socioeconomic factor estimation research, such as urbanization level estimation, population distribution estimation, GDP, energy consumption, carbon emissions and other factor estimation

• Other aspects: In addition to socioeconomic data research, night light images have also been tried to be applied to the environment, disasters, fisheries, energy and other fields. At present, some exploratory studies have been carried out using this image in the spatial analysis of light pollution propagation, fire control and resource protection, fishery monitoring, and natural gas burning monitoring.     

  CnOpenData has launched night light raster data for provinces, cities and counties in China. According to the administrative division levels of provinces, prefecture-level cities, districts and counties, it is divided into three tables, mainly providing the mean data of night lights.

Time interval

1992-2022

Field display

Sample data

Medium National night light grid data - provincial level

China Night Light Grid Data - City Level

China Night Light Grid Data - District and County Level

Related Literature

• Xu Kangning, Chen Fenglong, Liu Xiuyan, 2015: "The authenticity of China's economic growth: a test based on global night light data", Economic Research, No. 9.
• Xu Yang, Wu Kaiwen, 2023: "Overtime and labor income share: empirical evidence based on satellite night light", World Economy, No. 10.

Data update frequency

Annual update

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