Spatial Patterns of Local Income Inequalities

Abstract

K e y w o r d s: local income, inequality, spatial clusters of income distribution, exploratory spatial data analysis, RomaniaA B S T R A C T with the EU average, although this was achieved at the cost of an increasing internal, sub-national divergence [17], [6]. Centre for Research on Settlements and Urbanism Journal of Settlements and Spatial PlanningJ o u r n a l h o m e p a g e: http://jssp.reviste.ubbcluj.roThe main aim of this study is to describe the spatial patterns of local income inequalities by employing techniques of spatial exploratory data analysis. Global and local measures of spatial autocorrelation were computed in order to obtain estimates for the existing spatial autocorrelation at the local income level. Using this information, we determined the spatial clusters of significantly auto-correlated local income distribution. The first major result of the paper consists in the measurement of the local income levels, an innovative contribution to the advancement in this field since there are no official statistics on local level economic data in Romania. The second major result of the paper consists in the identification and delineation of specific spatial structures at high spatial resolution. It enables the precise designation of peripheral regions for spatial planning interventions. The third major outcome of the paper brings empirical evidence for the existence of highly significant spatial interactions and for the strong spatial interconnections between areas of similar local income levels.

Potential of Night-Time Lights to Measure Regional Inequality

“…Earth observation allowed us to calculate the Night Light Development Index (NLDI) which we used to measure regional inequality. This index is calculated based on the methodology for the calculation of the Gini coefficient, commonly used to measure regional inequality [43,48,53,67].…”

“…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 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].…”

“…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.…”

VIIRS Nighttime Light Data for Income Estimation at Local Level

“…These cities are the major economic hubs of the country [42][43][44], especially the capitalthe Municipality of Bucharest-the sixth city in the EU in terms of population (2,131,034 inhabitants in 2019), followed by six regional centers with a population exceeding 300,000 inhabitants: Iași (378,954 inhabitants), Timișoara (328,186 inhabitants), Cluj-Napoca (324,960 inhabitants), Constanța (313,021 inhabitants), Galați (304,050 inhabitants), and Craiova (301,269 inhabitants) [45]. With an important rural population (around 48% of the total population), the major municipalities of Romania are the leading places for innovation and smart city solutions, connecting large rural hinterlands with the global flow of capital, goods, and information [46,47]. With an important rural population (around 48% of the total population), the major municipalities of Romania are the leading places for innovation and smart city solutions, connecting large rural hinterlands with the global flow of capital, goods, and information [46,47].…”

“…With an important rural population (around 48% of the total population), the major municipalities of Romania are the leading places for innovation and smart city solutions, connecting large rural hinterlands with the global flow of capital, goods, and information [46,47]. With an important rural population (around 48% of the total population), the major municipalities of Romania are the leading places for innovation and smart city solutions, connecting large rural hinterlands with the global flow of capital, goods, and information [46,47].…”