Remote Sensing Based Assessment of Variation of Spatial Disparities

Abstract

The aim of this paper is to identify the relationship between the spatial and temporal variation of spatial disparities (measured with Gross Domestic Product, GDP) and nighttime lights at regional (county) level in Romania. The analysis presumed using night-time lights data captured by the DMSP-OLS satellites, in addition to official statistical data expressing economic income (GDP). The DMSP-OLS night-time lights data collected by the National Oceanic and Atmospheric Administration (NOAA) has a spatial resolution of 30 arc second and is available for the period of 1992-2013. The delimitation of unlit and lit areas was performed using ArcGIS software. The lower value of light intensity reflects less developed areas and those with higher value reflects more developed areas. The assessment relationship between GDP and night lights value was made using statistical correlation. The results show a strong linear correlation between the GDP and night lights value. It means that night-time lights are an excellent proxy for measuring spatial (regional) disparities. Moreover, based on the linear dependence and the spatial relation between these two datasets, we will be able in the future to go one step further and measure the level of spatial disparities at local level (cities and communes), where official statistical data is not recorded.

Potential of Night-Time Lights to Measure Regional Inequality

“…At the United Nations (UN) Sustainable Development Summit held in September 2015, the leaders of 193 UN member states expressed their adherence to the 17 Sustainable Development Goals (SDGs) of the 2030 Agenda which, as a global action plan, aims at reducing poverty, fighting inequalities and injustice, and protecting the environment by 2030 [1]. In monitoring progress towards these goals, Earth observation (EO) solutions play an important role in providing information where national data are not available or are costly [2][3][4][5][6][7].…”

“…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 has started an economic development process from a low level, making it very suitable for the application of night-time light data from satellite imagery [44]. However, the results are not limited to the area of study, as will be shown in the following parts of the article.The literature on regional inequalities focused until recently on local tax income from household surveys and GDP per capita from national accounts, as a complex measure of economic development highlighting inter-regional differentials worldwide [34,[45][46][47][48][49][50][51][52][53] or applying case studies in a Romanian context [34][35][36]42].However, the above-mentioned economic indicators used to measure regional inequalities have several limitations, such that GDP is measured directly only at the national level, while at the regional level it is calculated indirectly with a two-year delay compared to the national one [3,54]. Compared to traditional statistical data, night-time lights satellite images have a number of advantages: regional inequality can be calculated locally in real time (instead of waiting two years for the indirect calculations from national accounts); they have higher temporal resolution; and they can be obtained free of charge.…”

“…However, the above-mentioned economic indicators used to measure regional inequalities have several limitations, such that GDP is measured directly only at the national level, while at the regional level it is calculated indirectly with a two-year delay compared to the national one [3,54]. Compared to traditional statistical data, night-time lights satellite images have a number of advantages: regional inequality can be calculated locally in real time (instead of waiting two years for the indirect calculations from national accounts); they have higher temporal resolution; and they can be obtained free of charge.…”

Spatial Patterns of Local Income Inequalities

“…The severe economic decline in the 1990s, followed by a period of high economic growth (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) have put their mark on the development of settlements and regions. While some regions have successfully adapted to the new challenges (the capitalregion of Bucharest, the metropolitan regions of Cluj, Constanţa, and Timișoara) others have faced a deep crisis (the rural peripheries, mining and heavy industrial regions) [7], [8]. Growing interregional income inequalities have also represented one of the main concerns of the EU Member States.…”

Two decades of changes in spatial distribution of retail and commercial services: Czech experience

“…2019). The part of the countryside that is in decline is mainly located on periphery and faces to an aging population, worsened quality of housing or lack of new places for living, and (younger) population departure to cities or suburban areas (Benedek, J. and Ivan, K. 2018;Mládek, J. et al 2018;Patay, T. 2018;Kubeš, J. and Nováček, A. 2019).…”