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Artificial night-time light data extraction

If artificial night-time lights (ANTL) are considered means for safety and security, then what is the optimum level of brightness to ensure safety and security without compromising the environment?

About the training

Artificial Night-time Lights (ANTL) have been widely used in social science research as a robust proxy for economic development, the quantification of disaster impacts, electricity consumption, and various other applications. However, employing ANTL in social science research requires a comprehensive understanding of confounding factors, necessitating their individual consideration based on the specific needs of the application. This training aims to provide hands-on experience in both the theory and application of ANTL.

Program objectives

This program will achieve the following outcomes:

  • Raise awareness among emerging global leaders about ANTL.
  • Share knowledge on ANTL theory, practice, and measurement techniques.
  • Train students from the global south to extract, analyse, and visualise ANTL.
  • Understand the limitations of satellite imagery in capturing artificial night-time lights. 

Use the tabs below to find the information required to participate in this course.


Pre-training session: 11 June 2024

Week 1: 25-26 June 2024 (Introductory keynote and module 1)

Week 2: 2-3 July 2024 (Module 2)

Week 3: 9-10 July 2024 (Modules 3 and 4)


22:30 NZDT (New Zealand), 15:15 NPT (Nepal), 10:30 WAT (Nigeria)

The program will run for three weeks with two two-hour sessions per week.

The first week will be a single two-hour general session with a keynote talk from an expert to provide the context and importance of heat stress mapping to understand future energy service requirements. The following two weeks will have technical sessions aimed at practitioners. In the final session, participants will present their map and briefly discuss their interpretations of it.  

Pre-training session

This session will make sure all participants start the course with the same level of data knowledge.

Module one

Part 1: Understanding ANTL
Observe the data and guess the possible sources of nighttime lights.

Part 2: Data sources
Discover open access data source and find the data useful for your study.

Module two

Part 1: Upload data
Extract data and understand the format.

Part 2: Using data
Identify key issues in uploading data and resolve.

Part 3: Map chosen data
Use open-source ANTL data and map it.

Part 4: Experiment with maps
Identify hotspots and potential sources emitting ANTL.

Module three

Part 1: Extract annual ANTL data
Calculate the zonal statistics and export the data to tabular format.

Part 2: Identify and extract socio-economic datasets
Identify the potential World Bank social and economic dataset. Download, clean and export to tabular format.

Part 3: Merge ANTL and GDP datasets
Plot the scatter plot and conduct a correlation analysis.

Module four

Presentations and communication
Student will prepare a 1 to 2 pager report, list down the confounding factors, and discuss their learning.

Journal articles

Bhattarai, D., Lucieer, A., Lovell, H., & Aryal, J. (2023). Remote sensing of night‐time lights and electricity consumption: A systematic literature review and meta‐analysis. Geography Compass, 17(4).

Ch, R., Martin, D. A., & Vargas, J. F. (2021). Measuring the size and growth of cities using nighttime light. Journal of Urban Economics, 125, 103254.

Zhao, C., Cao, X., Chen, X., & Cui, X. (2022). A consistent and corrected nighttime light dataset (CCNL 1992–2013) from DMSP-OLS data. Scientific Data, 9(1).

Dingel, J.I., Miscio, A., Davis, D.R. (2021). Cities, lights, and skills in developing economies. Journal of Urban Economics 125, 103174.

Baragwanath, K., Goldblatt, R., Hanson, G., Khandelwal, A.K. (2021). Detecting urban markets with satellite imagery: An application to India. Journal of Urban Economics 125, 103173.

Monroe, T. (2017). Big Data and Thriving Cities. In World Bank eBooks.


Min, B., Baugh, K., Monroe, T., Goldblatt, R., Stewart, B., Kosmidou-Bradley, W. & Crull, C. (2021). Light every night: New night-time light data set and tools for development. World Bank Blogs.

Useable dataset and tutorial for further information

World Bank. (n.d.). Light every night: Registry of open data on AWST

Training resources are available on the CIET GitHub.