VIIRS Nighttime Light Data for Income Estimation at Local Level

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...

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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:
Online Access:https://www.mdpi.com/2072-4292/12/18/2950
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spelling doaj-87cc2730a6ae472388972c5a11af1a6f2020-11-25T01:55:10ZengMDPI AGRemote Sensing2072-42922020-09-01122950295010.3390/rs12182950VIIRS Nighttime Light Data for Income Estimation at Local LevelKinga Ivan0Iulian-Horia Holobâcă1József Benedek2Ibolya Török3Faculty of Geography, Babeş-Bolyai University, 5-7 Clinicilor Street, 400006 Cluj-Napoca, RomaniaFaculty of Geography, Babeş-Bolyai University, 5-7 Clinicilor Street, 400006 Cluj-Napoca, RomaniaFaculty of Geography, Babeş-Bolyai University, 5-7 Clinicilor Street, 400006 Cluj-Napoca, RomaniaFaculty of Geography, Babeş-Bolyai University, 5-7 Clinicilor Street, 400006 Cluj-Napoca, RomaniaThe 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.https://www.mdpi.com/2072-4292/12/18/2950nighttime lights (NTL)local incomemachine learning modelssmart citiesNPP/VIIRS
collection DOAJ
language English
format Article
sources DOAJ
author Kinga Ivan
Iulian-Horia Holobâcă
József Benedek
Ibolya Török
spellingShingle Kinga Ivan
Iulian-Horia Holobâcă
József Benedek
Ibolya Török
VIIRS Nighttime Light Data for Income Estimation at Local Level
Remote Sensing
nighttime lights (NTL)
local income
machine learning models
smart cities
NPP/VIIRS
author_facet Kinga Ivan
Iulian-Horia Holobâcă
József Benedek
Ibolya Török
author_sort Kinga Ivan
title VIIRS Nighttime Light Data for Income Estimation at Local Level
title_short VIIRS Nighttime Light Data for Income Estimation at Local Level
title_full VIIRS Nighttime Light Data for Income Estimation at Local Level
title_fullStr VIIRS Nighttime Light Data for Income Estimation at Local Level
title_full_unstemmed VIIRS Nighttime Light Data for Income Estimation at Local Level
title_sort viirs nighttime light data for income estimation at local level
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-09-01
description 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.
topic nighttime lights (NTL)
local income
machine learning models
smart cities
NPP/VIIRS
url https://www.mdpi.com/2072-4292/12/18/2950
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