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.
Dates
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)
Times
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.
Blog
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.
Dipendra Bhattarai
University of Tasmania
Technical lead
Dipendra Bhattarai brings a decade of experience collaborating with grassroots organisations and leveraging remote sensing skills to address the challenges of sustainable development goals in data-scarce regions. His research focuses on artificial night-time lights, energy, and climate policy, utilising data to translate complex information into actionable insights through a blend of economic and spatial rigour.
Monjit Borthakur
Cotton University, India
Monjit Borthakur is an Assistant Professor in the Department of Geography at Cotton University. His research interests include ecology and the environment, remote sensing and geographic information systems (GIS).
Dr Bishal Bharadwaj
Curtin University, Australia
Bishal Bharadwaj is a quantitative economic geographer interested in evaluating environmental policies (energy, plastic, pollution and climate change) in heterogeneous contexts to identify strategies to ensure policies are effective and equitable. He is currently a Research Fellow at the CIET.
Darcy Glenn
University of Canterbury, New Zealand
Darcy Glenn has worked to bring climate science to local governments so they can make informed decisions. She was a research assistant on Woodwell Climate Research Centre’s Risk team, working on free climate risk assessments for local governments.
WiZelle Kritzinger
University of Pretoria, South Africa
WiZelle Kritzinger is a Master of Philosophy in Economics student at the University of Pretoria. She is interested in energy and environmental economics, with a keen focus on energy poverty, its causes and consequences. She is currently a research assistant for the University of Pretoria’s Economic and Management Sciences Faculty Funded Transdisciplinary Project on Energy Poverty.
Contact details
If you are registered for this training and need assistance, contact the training coordinator as below.