Urban transformation and urban preservation are often seen as contradicting goals. Currently, there is no agreed-upon strategy on how to transform historic parts of the city while maintaining their heritage values. Many cities today are characterised by modern 20th-century heritage, yet it poses a challenge to their liveability and to the adoption of less car-dependent lifestyles. The concept of 15-minute cities can be used to improve urban liveability, which may positively affect the experience and functionality of modern historic urban landscapes (HUL). A review of the existing literature on the 15-minute city concept was carried out with the aim of identifying its key indicators and proposing possible new ones. This research primarily explores how the 15-minute city concept can be applied holistically and efficiently in modern heritage. Following the PRISMA guidelines, 20 articles published after the introduction of the concept and meeting the inclusion criteria in the Scopus and Web of Science (WoS) databases were reviewed. Selected examples and case studies are analysed to contextualise these variables and explore how they can be reflected in modern historic urban landscapes. As a result, the following variables are identified as central to the 15-minute city concept: mobility, time, distance, speed, functions of the amenities each with their associated indicators. Moreover, spatial characteristics, human-centred factors and heritage values are proposed as variables. The results are expected to give insights into how to operationalise the concept of 15-minute cities in modern sites of historic urban landscapes, in order to improve liveability while simultaneously preserving its values. This research contributes to the Sustainable Development Goals (SDGs) by promoting walkable, climate-resilient neighbourhoods. In particular, it aligns with SDG Target 11.4, which focuses on cultural and natural heritage.

Introduction: In recent decades, there has been a rise in mental illnesses. Community infrastructures are increasingly acknowledged as important for sustaining good mental health. Moreover, green spaces are anticipated to offer advantages for both mental health and social cohesion. However, the mediating pathway between green space, social cohesion and mental health and especially the proximity and characteristics of green spaces that trigger these potential effects remain of interest. Methods: We gathered data from 1365 individuals on self-reported social cohesion and mental health across four satellite districts in European cities: Nantes (France), Porto (Portugal), Sofia (Bulgaria), and Høje-Taastrup (Denmark). Green space data from OpenStreetMap was manually adjusted using the PRIGSHARE guidelines. We used the AID-PRIGSHARE tool to generate 7 indicators about green space characteristics measured in distances from 100–1500 m, every 100 m. This resulted in 105 different green space variables that we tested in a single mediation model with structural equation modelling. Results: Accessible greenness (900–1400 m), accessible green spaces (900–1500 m), accessible green space corridors (300–800 m), accessible total green space (300−800), and mix of green space uses (700–1100 m) were significantly associated with social cohesion and indirectly with mental health. Green corridors also showed negative indirect and direct associations with mental health in larger distances. Surrounding greenness and the quantity of green space uses were not associated with social cohesion nor indirectly with mental health. We also observed no positive direct associations between any green space variable in any distance to mental health. Conclusions: Our results suggest that accessibility, connectivity, mix of use and proximity are key characteristics that drive the relationship between green spaces, social cohesion and mental health. This gives further guidance to urban planners and decision-makers on how to design urban green spaces to foster social cohesion and improve mental health.

Introduction: Non-communicable diseases are the global disease burden of our time, with physical inactivity identified as one major risk factor. Green spaces are associated with increased physical activity of nearby residents. But there are still gaps in understanding which proximity and what characteristics of green spaces can trigger physical activity. This study aims to unveil these differences with a rigorous sensitivity analysis. Methods: We gathered data on self-reported health and physical activity from 1365 participants in selected neighbourhoods in Porto, Nantes, Sofia, and Høje-Taastrup. Spatial data were retrieved from OpenStreetMap. We followed the PRIGSHARE guidelines to control for bias. Around the residential addresses, we generated seven different green space indicators for 15 distances (100–1500 m) using the AID-PRIGSHARE tool. We then analysed each of these 105 green space indicators together with physical activity and health in 105 adjusted structural equation models. Results: Green space accessibility and green space uses indicators showed a pattern of significant positive associations to physical activity and indirect to health at distances of 1100 m or less, with a peak at 600 m for most indicators. Greenness in close proximity (100 m) had significant positive effects on physical activity and indirect effects on health. Surrounding greenness showed positive direct effects on health at 500–1100 m and so do green corridors in 800 m network distance. In contrast, a high quantity of green space uses, and surrounding greenness measured in a larger radius (1100–1500 m) showed a negative relationship with physical activity and indirect health effects. Conclusions: Our results provide insight into how green space characteristics can influence health at different scales, with important implications for urban planners on how to integrate accessible green spaces into urban structures and public health decision-makers on the ability of green spaces to combat physical inactivity.

In the interdisciplinary field of green space health research, there is a demand to reduce the effort to assess green space, especially for non-spatial disciplines. To address this issue, we developed AID-PRIGSHARE, an open-source script that automates over 400 QGIS processes to substantially reduces the time-intensive task of generating green space indicators. AID-PRIGSHARE calculates greenness, public green space, access to green infrastructure, and green space uses within distances of 100–1500 m around geolocations. This substantially reduces the effort for sensitivity analysis and may provide support for research that aims to understand the impact of different green space features and distances on health outcomes.

The relationship between green spaces and health is attracting more and more societal and research interest. The research field is however still suffering from its differing monodisciplinary origins. Now in a multidisciplinary environment on its way to a truly interdisciplinary field, there is a need for a common understanding, precision in green space indicators, and coherent assessment of the complexity of daily living environments. In several reviews, common protocols and open-source scripts are considered a high priority to advance the field. Realizing these issues, we developed PRIGSHARE (Preferred Reporting Items in Greenspace Health Research). It is accompanied by an open-source script that supports non-spatial disciplines in assessing greenness and green space on different scales and types. The PRIGSHARE checklist contains 21 items that have been identified as a risk of bias and are necessary for understanding and comparison of studies. The checklist is divided into the following topics: objectives (3 items), scope (3 items), spatial assessment (7 items), vegetation assessment (4 items), and context assessment (4 items). For each item, we include a pathway-specific (if relevant) rationale and explanation. The PRIGSHARE guiding principles should be helpful to support a high-quality assessment and synchronize the studies in the field while acknowledging the diversity of study designs.