In order to measure progress in achieving the Sustainable Development Goals (SDGs) by 2030, 169 targets have been approved globally. Even though interest in implementing these goals is high, many states have not yet established a set of subnational indicators to measure the implementation of the SDGs and have not completed their own assessment of progress in achieving these global goals. This study aims to measure the progress toward achieving the SDG at local and regional level in Romania by calculating the SDG Index. For the calculation of the SDG Index at subnational level, we propose an integrated approach based on 90 indicators, stored and processed in a PostgreSQL object‐relational database. The results show the concentration of the highest performances of sustainable development in some specific geographical areas. The rural areas and the extended peripheral regions in the eastern and southern part of the country are the poorest performers.

Metropolitan areas became the breeding ground for economic, political, and cultural concentration as well as for creativity and innovation. Therefore, sustainability within the urban dimension plays a crucial role in the overall success to attain the SDGs (Sustainable Development Goals) and their targets under the 2030 Agenda for Sustainable Development in 2015. Nevertheless, for cities, regions, or countries to monitor and measure their progress, there is a need for harmonized and reliable indicators. Consequently, the current study addresses sustainability on a local level by measuring the extent to which a Romanian metropolitan area achieves the SDGs agreed to in 2015 by the 193-member states of the United Nations. The paper analyses 16 out of the 17 SD (Sustainable Development) goals as the goal titled “Life below water” was not applicable. Relying on mainly quantitative data, we used the method of normalization and aggregation based on the arithmetic mean, which helped us to calculate the scores attained by each of the component localities of the metropolitan area within the SDGs and their overall SDG index. Next to this, the study combines the quantitative data analysis with a GIS (Geographic Information System) computer mapping technique. The results show that the municipality achieved the best results in the metropolitan area and a vertical development process from west to the east prevails. Measuring progress through a well-defined set of indicators and an optimization technique proved to be crucial in defining attainments’ levels within the metropolitan area.

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 km² 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 km²) 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 R² 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 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.