Potential of Night-Time Lights to Measure Regional Inequality

Abstract

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 ...

VIIRS Nighttime Light Data for Income Estimation at Local Level

“…The regression analyses illustrated a very powerful relationship between SOL and income for the cities with more than 50,000 inhabitants. This finding is in line with our previous research [30], and it underlines the direct relationship between city size, the economic complexity of cities on one side, and the intensity of nightlights, on the other side. The combination of the machine learning model with the EO-Incity tool, combined with a strategy for error reduction enabled an efficient and real-time estimation of the income, which explained 87% of the total variability.…”

“…Several studies highlighted a positive statistical relationship between the NTL data and GDP, both at national level and at sub-national level [33][34][35]51,52]. For Romania, Ivan et al [30] noticed a strong relationship between NTL and income, as well as between NTL and the GDP at county level [53]. The NTL data was correlated best with the income, which indicated its high potential to estimate the income at sub-national level.…”

“…Patino and Duque [23] reviewed the potential applications of satellite remote sensing to regional science research in urban settings by focusing on social problems with a spatial dimension. Application of EO can also be found in assessing social inequality and quality of life by detecting and monitoring the formation of slums [24,25], urban growth and urban sustainability [26,27], but also the estimation of population or GDP [28][29][30]. The se studies highlighted the big challenges of EO data in supporting sustainable urban research and policy development.…”

“…2020, 12, x FOR PEER REVIEW 3 of 19 of cities; that means bigger cities have more complex economic functions, related to industries and a wide variety of services, while smaller cities have a large agricultural sector or one dominant industry (for example, mining) or service function (generally tourism) [40,41]. For these reasons, there is no significant correlation between the economic output of smaller cities and night lights, the latter being largely generated by industries and service-related functions [30]. The analysis is expected to contribute to the development of novel methods for improved EObased urban services to support smart and sustainable urban development.…”

Maps of light pollution in odd places

“…The review of the uses of light pollution maps presented above is by no means comprehensive. However, together with insights and examples from elsewhere (such as literature focusing on the use of light pollution maps as proxy indicators of other environmental effects, economic and societal development and human well-being [45][46][47]) it is sufficient to suggest that light pollution maps are used across various communication contexts in addition to discussions focusing solely on light pollution assessment and management.…”