Muntasir Wahed
- Collaborator, AI/ML Wing
Ph.D Candidate
University of Illinois Urbana-Champaign
Research Interests
Machine Learning
Conference Paper2020Cited by 4
What Can Nighttime Lights Tell Us about Bangladesh?
Muntasir Wahed, Redwan Ahmed Rizvee, Riddho Ridwanul Haque, Amin Masud Ali, Moinul Zaber, Amin Ahsan Ali
2020 IEEE Region 10 Symposium (TENSYMP)
IEEE, pp. 1612–1615
Abstract
Analyzing the state and growth of various socioeconomic indicators is essential for effective developmental planning at a sub-national level. However, in many cases, data regarding such indicators are not publicly available and/or hard to collect. In many other cases, the available data may not be recent enough. In contrast, satellite data is both readily available and up-to-date. In recent times, various studies have been conducted to use different types of satellite data as a proxy to determine the condition of socio-economic indicators in different places around the world. In this paper, we study the efficacy of one such data source, nighttime lights (NTL), for monitoring factors related to sustainable development in the context of Bangladesh.
Keywords
Proxy (statistics)Socioeconomic statusContext (archaeology)Computer scienceSustainable developmentEconomic indicatorData scienceEnvironmental resource management
CCDS Authors
Amin Masud Ali, PhD
- Supervisor, DS Wing
Professor
Department of Economics
Jahangirnagar University
Research Interests
Development Economics, Public Sector Economics
Amin Ahsan Ali, PhD
- Director, AI/ML Wing
Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Research Interests
Artificial Intelligence, Machine Learning
References
- 1.Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794[10.1126/science.aaf7894]
- 2.Xi Chen, William D. Nordhaus. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 108(21), 8589–8594[10.1073/pnas.1017031108]
- 3.Charlotta Mellander, José Lobo, Kevin Stolarick, Zara Matheson. (2015). Night-Time Light Data: A Good Proxy Measure for Economic Activity?. PLoS ONE, 10(10), e0139779[10.1371/journal.pone.0139779]
- 4.M. Lazar. (2010). Shedding Light on the Global Distribution of Economic Activity. The Open Geography Journal, 3(1), 147–160[10.2174/1874923201003010147]
- 5.Christopher N.H. Doll, Shonali Pachauri. (2010). Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery. Energy Policy, 38(10), 5661–5670[10.1016/j.enpol.2010.05.014]
- 6.Tilottama Ghosh, Rebecca L. Powell, Christopher D. Elvidge, Kimberly Baugh, Paul C. Sutton, Sharolyn Anderson. (2010). Shedding Light on the Global Distribution of Economic Activity. The Open Geography Journal, 3(1)
- 7.Xuecao Li, Yuyu Zhou. (2017). Urban mapping using DMSP/OLS stable night-time light: a review. International Journal of Remote Sensing, 38(21), 6030–6046[10.1080/01431161.2016.1274451]
- 8.L. Zhuo, T. Ichinose, J. Zheng, J. Chen, P. J. Shi, X. Li. (2009). Modelling the population density of China at the pixel level based on DMSP/OLS non‐radiance‐calibrated night‐time light images. International Journal of Remote Sensing, 30(4), 1003–1018[10.1080/01431160802430693]
- 9.Tilottama Ghosh, Sharolyn Anderson, Christopher Elvidge, Paul Sutton. (2013). Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being. Sustainability, 5(12), 4988–5019[10.3390/su5124988]
- 10.Md. Shahinoor Rahman, Hossain Mohiuddin, Abdulla-Al Kafy, Pintu Kumar Sheel, Liping Di. (2018). Classification of cities in Bangladesh based on remote sensing derived spatial characteristics. Journal of Urban Management, 8(2), 206–224[10.1016/j.jum.2018.12.001]
- 11.CHRISTOPHER ELVIDGE, KIMBERLY BAUGH, VINITA HOBSON, ERIC KIHN, HERBERT KROEHL, ETHAN DAVIS, DAVID COCERO. (1997). Satellite inventory of human settlements using nocturnal radiation emissions: a contribution for the global toolchest. Global Change Biology, 3(5), 387–395[10.1046/j.1365-2486.1997.00115.x]
- 12.Naizhuo Zhao, Eric L. Samson. (2012). Estimation of virtual water contained in international trade products using nighttime imagery. International Journal of Applied Earth Observation and Geoinformation, 18, 243–250[10.1016/j.jag.2012.02.002]
- 13.Qiqi Zeng, Wenjun Zhang. (2019). Research on the Development of “Ghost City” Based on Night Light Data: Taking Sichuan Province as an Example. Open Journal of Social Sciences, 07(12), 176–188[10.4236/jss.2019.712013]
- 14.Eric Strobl, Emmanuel Skoufias, Thomas Tveit. (2019). Can we Rely on VIIRS Nightlights to Estimate the Short-Term Impacts of Natural Disasters?. World Bank, Washington, DC eBooks[10.1596/1813-9450-9052]
- 15.(2014). Policy Research Working Papers. World Bank policy research working paper[10.1596/prwp]
Cited By
- 1.Md. Manjur Morshed, Tanmoy Chakraborty, Tanmoy Mazumder. (2022). Measuring Dhaka’s Urban Transformation Using Nighttime Light Data. Journal of Geovisualization and Spatial Analysis, 6(2)[10.1007/s41651-022-00120-2]
- 2.Mohammad Tondro, Mohammad Jahanbakht, Shourav Bin Rabbani, Moinul Zaber. (2022). Does Immergence of ICT Focused Institutions Increase the Pace of Urban Development? A Provincial Case Study in Iran Using Data from the Ground and Above. , 219–223[10.1109/sustech53338.2022.9794195]
- 3.Amin Masud Ali, Amin Masud Ali, Muntasir Wahed, Amin Ahsan Ali, Amin Ahsan Ali, Moinul Zaber. (2024). Capturing the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime lights. Journal of the Asia Pacific Economy, 31(2), 397–426[10.1080/13547860.2024.2426872]
- 4.Hyun Hak Kim. (2025). Measuring Labor Market Status Using Remote Sensing Data. Sustainability, 17(7), 2807[10.3390/su17072807]