VIIRS Nighttime Light Data for Income Estimation at Local Level

• Main Authors: Kinga Ivan, Iulian-Horia Holobâcă, József Benedek, Ibolya Török
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Remote Sensing
• Subjects: nighttime lights (NTL); local income; machine learning models; smart cities; NPP/VIIRS
• Online Access: https://www.mdpi.com/2072-4292/12/18/2950

The aim of the paper is to develop a model for the real-time estimation of local level income data by combining machine learning, Earth Observation, and Geographic Information System. More exactly, we estimated the income per capita by help of a machine learning model for 46 cities with more than 50,000 inhabitants, based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime satellite images from 2012–2018. For the automation of calculation, a new ModelBuilder type tool was developed within the ArcGIS software called EO-Incity (Earth Observation–Income city). The sum of light (SOL) data extracted by means of the EO-Incity tool and the observed income data were integrated in an algorithm within the MATLAB software in order to calculate a transfer equation and the average error. The results achieved were subsequently reintegrated in EO-Incity and used for the estimation of the income value at local level. The regression analyses highlighted a stable and strong relationship between SOL and income for the analyzed cities. The EO-Incity tool and the machine learning model proved to be efficient in the real-time estimation of the income at local level. When integrated in the information systems specific for smart cities, they can serve as a support for decision-making in order to fight poverty and reduce social inequalities.