This study proposes an integrated methodology for evaluating and mapping quality of life (QoL) and the quality of a place as residence area, at local level. The QoL assessment was based on the development of composite criteria, using geographical variables that evaluate QoL, and geographic information systems. The composite criteria are related to the natural and the socioeconomic environment, the housing conditions, the infrastructure and services, and the cultural and recreational facilities. Each criterion was evaluated by a set of variables and each variable was weighted based on the residents’ preferences and the analytical hierarchy process. The criteria were also weighted and combined to assess overall QoL. The methodology was implemented in the Municipality of Katerini, Greece, and QoL mapping led to the zoning of the study area and the identification of areas with low and high QoL. The results revealed the highest level of overall QoL in three out of twenty-nine communities, which provide better housing conditions and access to public services and infrastructures, combining also qualitative natural environment, whereas five mountainous and remote communities scored the lowest level. Mapping QoL may support decision making strategies that target to improve human well-being, increase QoL levels and upgrade living conditions.

The purpose of this study is to assess and visualize the Quality of Life provided by urban space as a place of residence. The proposed methodology, after its theoretical documentation, is implemented in Athens Metropolitan Area, Greece. For the evaluation of Urban Quality of Life, a complex index is constructed by using multicriteria analysis. For this purpose, Quality of Life controlling factors such as built space, natural, socioeconomic, and cultural environment, infrastructure and services, and the quality of housing were analyzed within a GIS environment. The mapping of this index led to the identification of areas with different levels of Quality of Life. The results of the research can lead to more effective decision making regarding the planning of targeted actions and the distribution of financial resources to improve the Quality of Life of the residents in urban areas.

The importance of small urban green areas has increased in the context of rapid urbanization and the densification of the urban tissue. The analysis of these areas through remote sensing has been limited due to the low spatial resolution of freely available satellite images. We propose a timeseries analysis on 3 m resolution Planet images, using GEOBIA and vegetation indices, with the aim of extracting and assessing the quality of small urban green areas in two different climatic and biogeographical regions: temperate (Bucharest, Romania) and mediterranean (Athens, Greece). Our results have shown high accuracy (over 91%) regarding the extraction of small urban green areas in both cities across all the analyzed images. The timeseries analysis showed consistency with respect to location for around 55% of the identified surfaces throughout the entire period. The vegetation indices registered higher values in the temperate region due to the vegetation characteristics and city plan of the two cities. For the same reasons, the increase in the vegetation density and quality, as a result of the distance from the city center, and the decrease in the density of built-up areas, is more obvious in Athens. The proposed method provides valuable insights into the distribution and quality of small urban green areas at the city level and can represent the basis for many analyses, which is currently limited by poor spatial resolution.

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies.

This paper presents a geographical analysis to evaluate urban quality of life in Athens, Greece, and investigate spatial heterogeneity and potential clustering. The urban environment was examined using composite criteria related to natural, built and socioeconomic environment, housing conditions, public services and infrastructures, and cultural and recreational facilities. Each criterion constructed from a set of mappable sub-criteria/variables. Weighted cartographic overlay was implemented to assess the overall urban quality of life of each spatial unit, based on the importance the residents of the area attributed to each criterion. High levels of quality of life were revealed in the eastern neighborhoods of the municipality, whereas low levels were noticed mainly in the western neighborhoods. The results of the study were validated using the perceived quality of life of the study area’s residents, resulting in substantial agreement. Finally, after spatial autocorrelation analysis, significant clustering of urban quality of life in Athens was revealed. The quality-of-life assessment and mapping at a local scale are efficient tools, contributing to better decision making and policy making.