Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey

Basihos, Seda

doi:10.2139/ssrn.2885518

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…Night-time lights (NTL) satellite images have also been used in a variety of other studies, such as gross domestic product (GDP) estimation [8][9][10][11][12][13], economic decline detection [14,15], ship detection [16], light pollution detection [17][18][19], urban expansion monitoring [20][21][22][23], human well-being measurement [24], built-up area mapping [25,26], modeling of electricity consumption dynamics [27][28][29][30][31], and fire detection [32]. administrative units (local administrative units (LAU1)): 103 municipalities, 217 cities/towns, and 2861 communes (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Potential of Night-Time Lights to Measure Regional Inequality

et al. 2019

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Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light data were calculated using a grid with a 0.15 km 2 area, based on Defense Meteorological Satellite Program (DMSP) /Operational Linescan System (OLS satellite imagery for the 1992-2013 period and based on the National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) satellite imagery for the 2014-2018 period. Two population density grids were created at the level of equal cells (0.15 km 2 ) using ArcGIS and PostgreSQL software, and census data from 1992 and 2011. Subsequently, based on this data and using the Gini index approach, the Night Light Development Index (NLDI) was calculated within the MATLAB software. The NLDI was obtained for 42 administrative counties (nomenclature of territorial units for statistics level 3 (NUTS-3 units)) for the 1992-2018 period. The statistical relationship between the NLDI and the socio-economic, demographic, and geographic variables highlighted a strong indirect relationship with local tax income and gross domestic product (GDP) per capita. The polynomial model proved to be better in estimating income based on the NLDI and R 2 coefficients showed a significant improvement in total variation explained compared to the linear regression model. The NLDI calculated on the basis of night-time lights satellite images proved to be a good proxy for measuring regional inequalities. Therefore, it can play a crucial role in monitoring the progress made in the implementation of Sustainable Development Goal 10 (reduced inequalities).The adoption of the 2030 Agenda with its 17 Sustainable Development Goals (SDGs) created a framework for a radical change in the use of geospatial and EO solutions: 60% of the 169 SDG-related targets and 232 indicators can be directly monitored with EO solutions [4]. SDG 10 aims at reducing inequalities, which include, among other actions, empirical evidence production and monitoring the evolution of inequalities within and among countries. The monitoring of the latter is not difficult for most countries where national accounts and national statistical offices have been established [33]. The difficulties are related to the measurement of sub-national regional inequalities, with two shortcuts: scarce statistical data and a considerable time delay in calculating regional GDPs. Our study links statistical and geospatial frameworks for improved monitoring and reporting on SDG 10. At the same time, to our knowledge, this is the first attempt to introduce EO solutions in measuring SDG 10 at the sub-national level.We chose Romania as the study area for three reasons: it is one of the most unequal countries of the European Union (EU) [34-40]; these regional inequalities have been generated in the last 20 years [41][42][43]; and the country ...

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Section: Introductionmentioning

confidence: 99%

Potential of Night-Time Lights to Measure Regional Inequality

et al. 2019

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“…Night lights have been proven to be a good development indicator for GDP, population density, energy consumption, and urban sprawl, and it can be considered a composite index for economic and social development in China [ 21 , 22 , 23 , 24 , 25 ]. Thus, the NL dataset was selected in this study to represent the spatiotemporal characteristics of the socioeconomic characteristics for the GWR model.…”

Section: Results and Analysismentioning

confidence: 99%

“…NL data is well-suited to large-scale monitoring research, since it can express the spatial information and intensity change which is always used to reflect the socioeconomic characteristics. A large number of previous studies have demonstrated that nightlights can be considered a good developmental indicator for the estimation of the Gross Domestic Product (GDP) [ 21 ], population density [ 22 , 23 ], energy consumption [ 24 ], and urban sprawl [ 25 ], and is often exploited to derive the global mapping of socioeconomic parameters [ 26 ]. Thus, in this study, the NL dataset was selected as a comprehensive index, which could reflect the spatiotemporal characteristics of the socioeconomic characteristics from 2001 to 2010, which were obtained from NOAA’s National Centers for Environmental Information (NCEI) [ 27 ].…”

Section: Data Acquisitionmentioning

confidence: 99%

Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China

Zeng

Jiang

et al. 2017

IJERPH

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Epidemiological studies conducted around the world have reported that the under-five mortality rate (U5MR) is closely associated with income and educational attainment. However, geographic elements should also remain a major concern in further improving child health issues, since they often play an important role in the survival environment. This study was undertaken to investigate the relationship between the U5MR, geographic, and socioeconomic factors, and to explore the associated spatial variance of the relationship in China using the geographically weighted regression (GWR) model. The results indicate that the space pattern of a high U5MR had been narrowed notably during the period from 2001 to 2010. Nighttime lights (NL) and the digital elevation model (DEM) both have obvious influences on the U5MR, with the NL having a negative impact and DEM having a positive impact. Additionally, the relationship between the NL and DEM varied over space in China. Moreover, the relevance between U5MR and DEM was narrowed in 2010 compared to 2001, which indicates that the development of economic and medical standards can overcome geographical limits.

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“…As a result of comparing the accuracy of the results, we noticed that the DMSP-OLS data can be used for the estimation of the GDP at province level exclusively, while the NPP/VIIRS data can be used both at province and at local (cities) level. Basihos [36] estimated the GDP at sub-national level in Turkey for the 2001-2013 period, based on nightlights, using the neural network algorithm and rebuilt the GDP series for the 1992-2001 period, achieving statistically significant results.…”

Section: Research Overviewmentioning

confidence: 99%

VIIRS Nighttime Light Data for Income Estimation at Local Level

et al. 2020

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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.

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Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey

Cited by 8 publications

References 15 publications

Potential of Night-Time Lights to Measure Regional Inequality

Potential of Night-Time Lights to Measure Regional Inequality

Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China

VIIRS Nighttime Light Data for Income Estimation at Local Level

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