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Open AccessArticle

Global Mapping of GDP at 1 km2 Using VIIRS Nighttime Satellite Imagery

1
Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA
2
Morgridge College of Education, University of Denver, Denver, CO 80210, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 580; https://doi.org/10.3390/ijgi8120580
Received: 5 November 2019 / Revised: 6 December 2019 / Accepted: 7 December 2019 / Published: 11 December 2019
Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. Over the past decades, scientists have proposed many methods for estimating human activity on the Earth’s surface at various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime light (NTL) data. However, the DMSP-OLS NTL data and the associated processing methods have limited their reliability and applicability for systematic measuring and mapping of socioeconomic development. This study utilized Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest machine learning algorithm for more intelligent data processing to capture human activities. We used machine learning and NTL data to map gross domestic product (GDP) at 1 km2. We then used these data products to derive inequality indexes (e.g., Gini coefficients) at nationally aggregate levels. This flexible approach processes the data in an unsupervised manner at various spatial scales. Our assessments show that this method produces accurate subnational GDP data products for mapping and monitoring human development uniformly across the globe. View Full-Text
Keywords: nighttime light; VIIRS; GDP; sustainability; machine learning nighttime light; VIIRS; GDP; sustainability; machine learning
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Wang, X.; Sutton, P.C.; Qi, B. Global Mapping of GDP at 1 km2 Using VIIRS Nighttime Satellite Imagery. ISPRS Int. J. Geo-Inf. 2019, 8, 580.

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