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.

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.

Abstract. Besides other non-behavioural factors, low-light conditions significantly influence the frequency of traffic accidents in an urban environment. This paper intends to identify the impact of low-light conditions on traffic accidents in the city of Cluj-Napoca, Romania. The dependence degree between light and the number of traffic accidents was analysed using the Pearson correlation, and the relation between the spatial distribution of traffic accidents and the light conditions was determined by the frequency ratio model. The vulnerable areas within the city were identified based on the calculation of the injury rate for the 0.5 km 2 areas uniformly distributed within the study area. The results show a strong linear correlation between the low-light conditions and the number of traffic accidents in terms of three seasonal variations and a high probability of traffic accident occurrence under the above-mentioned conditions at the city entrances/exits, which represent vulnerable areas within the study area. Knowing the linear dependence and the spatial relation between the low light and the number of traffic accidents, as well as the consequences induced by their occurrence, enabled us to identify the areas of high traffic accident risk in Cluj-Napoca.

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.

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 ...

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.

The analysis of pedestrian–vehicle crashes makes a significant contribution to sustainable pedestrian safety. Existing research is based mainly on the statistical analysis of traffic crashes involving pedestrians and their causes, without the identification of areas vulnerable to traffic crashes that involve pedestrians. The main aim of this paper is to identify areas vulnerable to school-aged pedestrian–vehicle crashes at a local level to support the local authorities in implementing new urban traffic safety measures. The vulnerable areas were determined by computing the severity index (SI) based on the number of fatal, serious, and slight casualties throughout the 2011–2016 period in a large urban agglomeration (Bucharest). As well as the vulnerable areas, the triggering factors and the time intervals related to school-aged pedestrian–vehicle crashes were identified. The outcomes of the study showed that the vulnerable areas were concentrated only in districts 2 and 4 of Bucharest, and they were associated with high vehicle speed and pedestrians’ unsafe crossing behavior. The findings revealed that speed and age are triggering factors in generating school-aged pedestrian–vehicle crashes. The identified time peaks with a high number of traffic crashes correspond to the afternoon time intervals, when scholars go home from school. The identification of the areas vulnerable to school-aged pedestrian crashes may help local authorities in identifying and implementing measures to improve traffic safety in large urban agglomerations.

Cette étude a visé l'identification des tendances de modification des cheminements naturels des eaux et du volume d'eau ruisselé en surface urbaine. L'identification des réseaux d'écoulement en surface couverte, respectivement non couverte, sur la base des Modèle Numérique de Terrain-MNT et des Modèle Numérique de Surface-MNS, a mis en évidence la manière dont le cheminement naturel de l'eau a été modifié par les obstacles existants à la surface du terrain. Les Surfaces Totales Imperméables-STI pour les années 1984, 1991, 2002 et 2014 ont été extraites sur la base des images Landsat. La tendance du ruissellement de l'eau pour la période 1984-2014, pour un sous-bassin versant de Bordeaux, a été démontrée à travers la modélisation hydrologique HEC-HSM sur la base des pourcentages des Surfaces Effectives Imperméables-SEI calculés. Les résultats ont mis en évidence la modification des cheminements naturels dans l'aire étudiée et une tendance à l'augmentation du volume d'eau ruisselé. Ces tendances sont le résultat de l'urbanisation et de l'industrialisation et requièrent la mise en place de mesures pour leur atténuation à l'avenir. Mots-clés : hydrologie urbaine, Landsat, modèle numérique de surface, surfaces effectives imperméables, crue urbaine Urban runoff pathways and surface water volumes evolution. Case study: Bordeaux 1984-2014, France ABSTRACT.-This study aimed to identify trends change in natural pathways of urban runoff and of the water volumes into a highly urbanized area of city Bordeaux, France. The identification of the city's flow paths on urbanized terrain and natural land surface based on Digital Terrain Model and Digital Surface Model, highlighted how the natural pathways of the runoff were been modified by successive buildings and other barriers of the urbanized ground surface. On the base of Landsat images, the Total Impervious Area for 1984, 1991, 2002 and 2014 were extracted and the Effective Impervious Areas were calculated. The tendency of the runoff for the period 1984-2014, for a sub-watershed of Bordeaux, was demonstrated through the HEC-HSM hydrologic modeling. The results highlighted the modification of natural paths in the studied area, and a tendency to increase the volume of water runoff. These trends are the result of urbanization and industrialization and require the implementation of mitigation measures in the future.