Blinded by the Light: Monitoring Local Economic Development Over Time With Nightlight Emissions
Open accessAbstract
Nighttime light (NTL) emissions are widely used across disciplines to map the spatial distribution of a variety of socioeconomic variables. For economic studies, NTLs allow to proxy for levels of economic indicators at the country level as well as the local level. Further, multi-temporal differences in NTL intensity are also related to GDP differences on the country level. In this study, we investigate if this relation in temporal differences also holds for the local level. We test this with DMSP as well as VIIRS NTLs data from 2010–2015 in Nigeria, Tanzania, and Uganda. Even though we successfully map local levels of socio-economic status with NTLs, we find multi-temporal changes in NTLs at this local level to be uncorrelated with socio-economic development over time. We conclude that luminosity values based on current DMSP and VIIRS sensors are no silver bullet in measuring local economic changes over time and should, if at all, only be used with caution for this.
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Random forest regression is a powerful tool to map the spatial distribution of humanitarian indicators with NTLs accurately.
“Second, we employ random forest regression which is a powerful tool to map the spatial distribution of humanitarian indicators with NTLsaccurately [18, 26].”
From Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping (2021) · cites this paper as [18]
The Results report a strong spatial association between nighttime lights and local wealth and consumption, supporting the NTL-mapping part of the claim. The Methods specify linear regression, not random forest regression, so this paper does not support the claim about that method’s accuracy. Page numbers are unavailable in our copy. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe R2 values in Table 2 are generally slightly lower than ranges reported in previous studies.
“Compared to previous studies of economic activity and nightlights, 𝑅 2 in Table 2 are in general slightly lower than ranges in previous studies [7, 15, 18, 29].”
From Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping (2021) · cites this paper as [18]
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Cites
Nighttime lights as a proxy for human development at the local level
Bruederle, A., & Hodler, R. (2018) · PLOS ONE · doi:10.1371/journal.pone.0202231 · Cited by 259 · see citing works
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Research works printed but not linked: 7
- Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon, "Com- bining satellite imagery and machine learning to predict poverty," Science, vol. 353, pp. 790-794, 2016.
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- Lukas Kondmann, Matthias Haeberle, and Xiao Xiang Zhu, "Combining twitter and earth observation data for local poverty mapping," in NeuRIPS Machine Learning for the Developing World Workshop, 2020, pp. 1-5.
- Lukas Kondmann and Xiao Xiang Zhu, "Measuring changes in poverty with deep learning and satellite im- agery," ICLR 1st Workshop on Practical ML for Devel- oping Countries, 2020.
- Mia M Bennett and Laurence C Smith, "Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics," Remote Sensing of Environment, pp. 176-197, 2017.
- Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke, "Using publicly available satellite im- agery and deep learning to understand economic well- being in africa," Nature Communications, vol. 11, no. 1, pp. 1-11, 2020.
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Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping
Kondmann, L., & Zhu, X. X. (2021) · arXiv · doi:10.48550/arxiv.2108.02137 · Cited by 3 · see citing works
Self-citation
- Authors
- Kondmann, L., Taubenbock, H., & Zhu, X. X.
- Affiliations
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Venue
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
- Publisher
- IEEE
- Published
- 2021-07-11
- Pages
- 5708-5711
- OpenAlex
- W3206495318
- Cited by
- 3
- References
- 1
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 1
- FWCI
- 1.57
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (publishedVersion)
- elib (German Aerospace Center) (submittedVersion)
- Funders
- European Research Council (ERC) (ERC-2016-StG-714087)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Regional Economics and Spatial Analysis
- Keywords
- nighttime lights, local economic development, socio-economic development, dimethylsulfoniopropionate, VIIRS, Nigeria, Tanzania, Uganda
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