During the last 30 years, the Romanian economy has faced different challenges due to structural readjustments, overcoming crisis and globalization. The share of primary and secondary sectors in the gross domestic product have strongly decreased, while the services have taken off. The main objective for this study is to observe how these economic readjustments can be assessed and measured using the Corine Land Cover datasets from 1990, 2000, 2006, 2012 and 2018 (with special observation on the range 2006 and 2018, after Romania was included in European Union). Despite some of the methodological limitations (like the minimum surface change), the Corine Land Cover turned out to be a powerful tool and it allowed us to detect an intense correlation between the socioeconomic and the structural trends in land use, in specific spatial contexts. The artificial surfaces are constantly increasing and this trend is rather visible as a distance function to the major Romanian cities. The most interesting changes occurred in the case of the agricultural polygons. The main trend emphasized by our analysis regards the redeployment of large farms in areas of agronomic and environmental territorial optimum. Such is the case for vineyards (after a decline during 2000–2006) and for annual cultures. All these changes in land-use patterns are too complex to be encompassed by a single methodology, which is why we used different tools, ranging from spatial analysis to geo-economic modeling, in order to detect how the Corine Land Cover datasets might be used for a better understanding of the Romanian economic readjustments.

The study of visitor experiences incorporating user-generated content from accommodation platforms is a distinct trend in the geography of tourism. Our research aims to better understand how this content can be instrumentalized in order to assess the viability of Airbnb accommodation ranks based on specific case studies from Greece, Italy, and Spain. The methodological frame we propose is based on two separate tools. First, the users’ reviews are summarized using sentiment analysis techniques, and the positive component is extracted as a separate indicator (predictor). The second step consists of mobilizing geographically weighted regression (GWR) to identify of the potential statistical association between the Airbnb apartments’ ranks and the quantitative outputs of sentiment analysis. The results obtained for 13 case studies are based on more than 4.6 million reviews. They clearly emphasize a gap between the rank proposed by the platform and the positive scores of sentiment analysis for the accommodation units analyzed (88,053). Despite some limitations linked to the quantity of data needed to be integrated into the investigation, the methodological frame can be transferred to other destinations, providing useful information about the potential distortions of tourism markets by the meta-description of accommodation systems.

The study examines land use and land cover changes (LUCC) in Romania’s seven largest metropolitan areas from 2000 to 2018, concentrating on trends and their driving forces at the national level. The multitemporal analysis was conducted utilising GIS tools to track changes in urban, agricultural, and natural land categories. To increase accuracy, high-resolution satellite imagery and reclassification methods were also used, providing more reliable results. The detected results were linked to demographic data to highlight the dynamics of population pressure, economic growth, and infrastructure development. By identifying the common causes of land-use changes, the main trends were presented, along with local territorial and socioeconomic specificities. The results reflect the challenges the country is facing due to increasing suburbanisation and land-use fragmentation and illustrate the need for context-specific urban planning methods.

The territorial contexts in which the universities are supposed to function become more and more complex and involve different forms of military, social and political risks. This article examines potential methodological approaches for evaluating these risks, using the network of AUF universities as a case study. The data used for the evaluation are provided by the ACLED datasets of incidents at a world scale, and they were spatialized using classical GIS tools. The first methodological approach measures the university's exposure by placing them in different distance bands around the median centers of significant state and non-state actors responsible for the military conflicts between 2021 and 2025. The output of the second approach is a multivariate typology of the AUF universities, each class describing different forms of exposure to risks. The key findings underline the fact that there are substantial disparities between the AUF regions, in terms of exposure, with severe risks in Western Africa, the Middle East and Eastern Europe.0 Based on the results of the research, a set of recommendations for policy design inside AUF was developed.