Ronald András Kolcsár

Regional and national 3D soil hydraulic maps enhance understanding of soil hydraulic properties, essential for environmental assessments. However, data aggregation is often necessary in large-scale models to facilitate the modelling of complex soil characteristics. This study presents a soil hydrologic groups map for Hungary, derived through k-means clustering and expert-based rules. Clustering was applied to the 100 m resolution 3D HU-SoilHydroGrids database, considering eight hydraulic parameters across six depths. The accuracy of these maps is limited for rare soil types with extreme characteristics due to their small spatial extent and sparse representation in national datasets. To account for these underrepresented soil types, we refined each statistics-based cluster using expert-based rules incorporating soil profile depth, genetic type, electrical conductivity, and exchangeable sodium content. The final classification includes 68 soil hydrologic groups, defined by distinct hydraulic properties, such as van Genuchten parameters to describe water retention, and saturated hydraulic conductivity. This national map supports country-wide hydrological modelling, environmental management, and agricultural planning in Hungary by enabling consistent treatment of similar soils.

Analysis of urban green space (UGS) provision is becoming increasingly important from an urban-planning perspective, as processes related to climate change tend to worsen the urban heat-island effect. In the present study, we aimed to map the UGS provision of Szeged, Hungary, using a GIS-based complex approach. Different age groups, especially the elderly, have different demands on the ecosystem services and infrastructure of UGSs. To provide an in-depth assessment of UGS provision for planners, we analysed the UGS availability and accessibility, using subblock-level population data, which includes not only the total number of residents but also provides information about the age-group distribution for each building of the city. We delineated areas having different UGS provision levels (called provision zones) and assessed the age distribution of the residents living in each zone. We found that the residents within 2-min walking distance to public green spaces are older than expected by comparison to the age distribution of Szeged. In provision zones with abundant locally available UGSs (measured as UGS per capita within 50-m buffers), we found that the youngest (0–18 years) and oldest (≥ 61 years) inhabitants are overrepresented age groups, while the age group 19–40 has the lowest overall UGS provision within the city of Szeged. Our research, which has the potential to be adapted to other settlements, contributes to the identification of UGS-deficit areas in a city, thereby providing essential information for urban planners about where increases in UGS are most needed and helping to assess infrastructural enhancements that would be adequate for the locally most-dominant age groups.

Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.