The configuration of public open spaces plays a crucial role in shaping how different people use them. Nevertheless, our understanding of how the physical features of public open spaces influence the activities conducted within them, and the extent to which this impact differs across various individuals and population groups, is currently limited. In this study, we explore how the physical characteristics of public open spaces influence the likelihood of use among individuals, spanning different age and gender groups. By employing crowdsourcing, street-level imagery, statistical comparisons, and reflexive thematic analysis we uncover significant variations in the suitability of public open spaces for distinct activities, such as socializing or exercising. Greenspaces emerge as the preferred choice for almost all activities, whereas streets are consistently rated as the least suitable. Additionally, we identified various characteristics that influence the activities people are likely to engage in. These include the size of the space, the presence of seating, natural elements such as vegetation or water bodies, and the proximity to transport infrastructure. Surprisingly, we do not observe statistically significant differences in preferences among most age and gender groups. Overall, our study underscores the need for providing a diverse range of public open spaces tailored to accommodate different individuals, population groups, and activities.@en

Accessibility is a widely used concept across various disciplines to evaluate the degree to which individuals can reach desired destinations. Conventionally, accessibility is determined by the attractiveness of a destination and the associated travel cost to reach it. However, existing place-based accessibility measures do not differentiate between destinations accessible to individuals from a single demographic group and those accessible to individuals from diverse demographic groups. We propose a measure to assess the potential of distinct destinations to bring different individuals and demographic groups together, defining this property as co-accessibility. We demonstrate how measuring co-accessibility can enhance existing accessibility measures, describe its components, and provide a mathematical formulation for quantifying it. To illustrate the practical application of our measure, we conduct a case study in Amsterdam, the Netherlands, comparing the accessibility and co- accessibility of various destinations. This sample case study highlights the complexities and challenges inherent in measuring co-accessibility. Building on existing literature and our analysis results, we discuss the potential implications of co-accessibility, identify key challenges in its assessment, and recommend directions for future research.@en

The study of urban greenspaces typically relies on three types of data: people’s subjective perceptions collected via questionnaires, vegetation indices derived from satellite imagery, such as the Normalized Difference Vegetation Index (NDVI), and Land Use or Land Cover maps, such as OpenStreetMap (OSM). Data on people’s perceptions are essential when researching human activities, yet they scale poorly. NDVI and OSM data, on the other hand, are freely available worldwide, thus valuable for assessing cities at scale or prioritizing locations for interventions. However, it is unclear how effectively NDVI and OSM data capture people’s visual perceptions of urban greenspaces. In this work, we collect people’s visual perceptions of public spaces in three major European cities through crowdsourcing, quantitatively compare them to NDVI and OSM data, and qualitatively investigate disparities. We found that NDVI moderately correlates with perceived greenness and that not only OSM greenspaces but also pocket parks and play spaces are often considered green. Furthermore, we found that people’s perceptions correspond best to OSM data in small radius distances and NDVI data in larger radius distances and that combining NDVI and OSM data can improve identification of places in OSM that are commonly considered green. Our qualitative analysis revealed that configuration and variety of vegetation, and presence of other natural or built-up features, influence people’s perceptions of greenspace. With our findings we aim to help researchers and practitioners make more informed decisions when collecting greenspace data for their specific context, ultimately contributing to green urban environments that reflect people’s perspectives.@en

Access to greenspace impacts children’s physical, social, and mental health. Numerous factors affect children’s access to urban greenspaces, often distinct from those affecting the general population, including parental restrictions, limited routine activity-space, and particular preferences. Most accessibility measures, however, employ the same principles for children and the general population, and a comprehensive exploration of factors and corresponding measures remains lacking. We conduct a scoping review and workshops with researchers and practitioners to identify factors affecting children’s access to greenspace, synthesize them into a conceptual model, and assess how existing accessibility measures address these factors. We focus on children aged 6–11 years old. Our analysis indicates children’s access involves a trade-off between reachability, determined by the route connecting the child’s starting setting to greenspace, and attractiveness, determined by how the greenspaces adhere to the child’s, and their companions’, preferences and motivations for visiting. Safety perceptions are important throughout. Existing accessibility measures predominantly emphasize reachability, neglecting personal characteristics and motivations. Based on our findings, we propose future directions for developing child-centered accessibility metrics. Our overview of metrics can facilitate decision-making in the selection of suitable measures, while our conceptual model can foster shared understanding of factors affecting children’s access to urban greenspace.@en

Recent evidence underscores the importance of greenspace exposure in promoting physical activity, and in having a positive impact on mental health and cognitive development. Accessibility has been identified to be the primary motivating factor when it comes to encouraging greenspace use and, correspondingly, exposure. Existing quantitative approaches to measuring greenspace accessibility predominantly focus on the areas surrounding home locations, often disregarding access from other settings such as schools or workplaces, exposures while on the move, and mobility differences among different population age groups. This article introduces a novel method to measure greenspace accessibility that considers access from different activity settings (i.e., homes, schools, and the commutes between them) for children and adolescents, while accounting for the dependency of human access on the road network. We use Amsterdam, Rotterdam, and The Hague in the Netherlands as case studies to illustrate the utility of our method. Compared to conventional measures of greenspace accessibility, we show that accounting for school and commuting settings, in addition to residences, captures previously untapped accessibility aspects for both children and adolescents. Our approach can be replicated in other cities worldwide, with the aspiration to provide planners and public health policy-makers with a methodological tool that can help in evaluating access and use of greenspaces when designing health-promoting interventions.

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Accessible outdoor spaces for unsupervised play are important for children’s health. However, parents impose constraints based on their perception of safety, which can have a significant impact on which play spaces are actually accessible to children. Such constraints are not taken into account by widely adopted accessibility indicators that use generic radial buffers or travel distances. We introduce a child’s play accessibility metric, which measures the ease with which children can reach outdoor play spaces without supervision. We developed this metric through an iterative co-design process with experts on the built environment and children’s health, leveraging open data. Our metric considers traffic, natural barriers to children, and a range of playable spaces. It can be used by planners and policymakers to enable large-scale assessments of play space accessibility, identify associated equity issues, and benchmark progress toward healthier environments for all ages.@en

City streets that feel safe and attractive motivate active travel behaviour and promote people’s well-being. However, determining what makes a street safe and attractive is a challenging task because subjective qualities of the streetscape are difficult to quantify. Existing evidence typically focuses on how different street features influence perceived safety or attractiveness, but little is known about what influences both. To fill this knowledge gap, we developed a crowdsourcing tool and conducted a study with 403 participants, who were asked to virtually navigate city streets in Frankfurt, Germany, through a sequence of street-level images, rate locations based on perceived safety and attractiveness, and explain their ratings. Our results contribute new insights regarding the key similarities and differences between the factors influencing perceived safety and attractiveness. We show that the presence of human activity is strongly related to perceived safety, whereas attractiveness is influenced primarily by aesthetic qualities, as well as the number and type of amenities along a street. Moreover, we demonstrate that the presence of construction sites and underpasses has a disproportionately negative impact on perceived safety and attractiveness, outweighing the influence of any other features. We use the results to make evidence-informed recommendations for designing safer and more attractive streets that encourage active travel modes and promote well-being.@en

Mental disorders among children and adolescents pose a significant global challenge. The exposome framework covering the totality of internal, social and physical exposures over a lifetime provides opportunities to better understand the causes of and processes related to mental health, and cognitive functioning. The paper presents a conceptual framework on exposome, mental health, and cognitive development in children and adolescents, with potential mediating pathways, providing a possibility for interventions along the life course. The paper underscores the significance of adopting a child perspective to the exposome, acknowledging children's specific vulnerability, including differential exposures, susceptibility of effects and capacity to respond; their susceptibility during development and growth, highlighting neurodevelopmental processes from conception to young adulthood that are highly sensitive to external exposures. Further, critical periods when exposures may have significant effects on a child's development and future health are addressed. The paper stresses that children's behaviour, physiology, activity pattern and place for activities make them differently vulnerable to environmental pollutants, and calls for child-specific assessment methods, currently lacking within today's health frameworks. The importance of understanding the interplay between structure and agency is emphasized, where agency is guided by social structures and practices and vice-versa. An intersectional approach that acknowledges the interplay of social and physical exposures as well as a global and rural perspective on exposome is further pointed out. To advance the exposome field, interdisciplinary efforts that involve multiple scientific disciplines are crucial. By adopting a child perspective and incorporating an exposome approach, we can gain a comprehensive understanding of how exposures impact children's mental health and cognitive development leading to better outcomes.@en

Neighborhood safety and its perception are important determinants of citizens’ health and well-being. Contemporary urban design guidelines often advocate urban forms that encourage natural surveillance or “eyes on the street” to promote community safety. However, assessing a neighborhood’s level of natural surveillance is challenging due to its subjective nature and a lack of relevant data. We propose a method for measuring natural surveillance at scale by employing a combination of street-level imagery and computer vision techniques. We detect windows on building facades and calculate sightlines from the street level and surrounding buildings across forty neighborhoods in Amsterdam, the Netherlands. By correlating our measurements with the city’s Safety Index, we also validate how our method can be used as an estimator of neighborhood safety. We show how perceived safety varies with window level and building distance from the street, and we find a non-linear relationship between natural surveillance and (perceived) safety.@en

Can planning and design influence health and well-being in urban settings? Even though it was the public health issues faced by industrial cities that originally gave rise to the field of city planning, their paths have diverged over the years.However, how human settlements are planned, designed, and built can drastically improve or harm human health and well-being through factors that either promote or obstruct healthier lifestyles. Global organizations currently advocate city designs that enhance access to a wide range of resources and experiences for all. But how do we evaluate the successful translation of these goals into healthy, resilient, and socially cohesive human settlements and communities? This chapter revisits fundamental concepts of proximity, walkability, and accessibility that are omnipresent in planning and design directives for healthier communities. It critically examines prevailing conceptualizations and measures and offers alternative directions for operationalizations that accommodate the variety of human behaviours and the complex linkages between factors in the urban environment.@en

Background:
There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children’s suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time.

Methods:
Equal-Life develops and tests combined exposures and their effects on children’s mental health and cognitive development. Data from eight birth-cohorts and three school studies (N = 240.000) linked to exposure data, will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped, at different scale levels and timeframes, while accounting for social inequities. Reasoning from the outcome point of view, relevant stakeholders participate in the formulation and validation of research questions, and in the formulation of environmental hazards. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling.

Conclusions:
Equal-Life contributes to the development and utilization of the exposome concept by (1) integrating the internal, physical and social exposomes, (2) studying a distinct set of life-course effects on a child’s development and mental health (3) characterizing the child’s environment at different developmental stages and in different activity spaces, (4) looking at supportive environments for child development, rather than merely pollutants, and (5) combining physical, social indicators with novel effect markers and using new data sources describing child activity patterns and environments.@en

A growing body of literature underscores the societal and mental health benefits of facilitating interactions between different age groups. While it is acknowledged that age segregation might be experienced in daily activities beyond an individual’s home location, the majority of spatial age segregation studies and corresponding measures are almost exclusively based on the concentration and distribution of age groups at the neighborhood level as the major determinants. Disregarding potential encounters with individuals from different age groups in places beyond the residential space could result in fragmented estimates of the level of spatial age segregation. To take such encounters at various activity locations into consideration, it is important to determine both how accessible these places are to individuals of different ages and the likelihood of being exposed to other age groups. This article introduces a methodological approach to assessing spatial age segregation that accounts for the degree of age-adjusted co-accessibility to different activity locations, in addition to the age structure of neighborhoods. We use spatially disaggregated data about activity locations across the cities of Amsterdam, Rotterdam, The Hague, Utrecht, and Eindhoven in the Netherlands to calculate several spatial accessibility metrics, and to estimate age diversity and co-accessibility scores for each activity. Our analysis results demonstrate how the proposed methodology can provide new insight into the potential moderating effect that exposure to other age groups in places outside of the home can bring to the level of spatial age segregation.@en

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.@en

Social web data increasingly complement studies of various social phenomena, especially when the availability of traditional data is limited. One such case is that of vulnerable young populations that are disengaged from employment, education, or training; usually referred to as NEETs. This paper explores the extent to which social media data and discussion websites could complement conventional sources in the study of NEETs. We focus on user-generated content posted to the dedicated r/NEET subreddit, which gathers subscribers who self-identify as NEETs. We develop and implement a data processing pipeline for the analysis of the behavioral patterns and main concerns of this social group. Our analysis of Reddit data reaches similar conclusions to official reports from governmental institutions in Europe. The paper also provides insights into health-related issues and latent interests of NEETs, not recorded in official reports and related literature.

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Points of interest (POIs) digitally represent real-world amenities as point locations. POI categories (e.g. restaurant, hotel, museum etc.) play a prominent role in several location-based applications such as social media, navigation, recommender systems, geographic information retrieval tools, and travel-related services. The majority of user queries in these applications center around POI categories. For instance, people often search for the closest pub or the best value-for-money hotel in an area. To provide valid answers to such queries, accurate and consistent information on POI categories is an essential requirement. Nevertheless, category-based annotations of POIs are often missing. The task of annotating unlabeled POIs in terms of their categories — known as POI classification — is commonly achieved by means of machine learning (ML) models, often referred to as classifiers. Central to this task is the extraction of known features from pre-labeled POIs in order to train the classifiers and, then, use the trained models to categorize unlabeled POIs. However, the set of features used in this process can heavily influence the classification results. Research on defining the influence of different features on the categorization of POIs is currently lacking. This paper contributes a study of feature importance for the classification of unlabeled POIs into categories. We define five feature sets that address operation based, review-based, topic-based, neighborhood-based, and visual attributes of POIs. Contrary to existing studies that predominantly use multi-class classification approaches, and in order to assess and rank the influence of POI features on the categorization task, we propose both a multi-class and a binary classification approach. These, respectively, predict the place category among a specified set of POI categories, or indicate whether a POI belongs to a certain category. Using POI data from Amsterdam and Athens to implement and evaluate our study approach, we show that operation based features, such as opening or visiting hours throughout the day, are the most important place category predictors. Moreover, we demonstrate that the use of feature combinations, as opposed to the use of individual features, improves the classification performance by an average of 15%, in terms of F1-score.@en

As cities resume life in public space, they face the difficult task of retaining outdoor activity while decreasing exposure to airborne viruses, such as the novel coronavirus. Even though the transmission risk is higher in indoor spaces, recent evidence suggests that physical contact outdoors also contributes to an increased virus exposure. Given that streets constitute the largest percentage of public space in cities, there is an increasing need to prioritise their use to minimise transmission risk. However, city officials currently lack the assessment tools to achieve this. This article evaluates the extent to which street segments are associated with spatiotemporal variations of potential exposures of pedestrians to virus transmission. We develop a multi-component risk score that considers both urban form and human activity along streets over time, including (a) an assessment of pedestrian infrastructure according to the average width of pavements, (b) a measure of accessibility for each street based on its position in the street network, (c) an activity exposure score that identifies places along streets where exposure could be higher and (d) an estimate of the number of pedestrians that will pass through each street during weekdays and weekends. We use Amsterdam in the Netherlands as a case study to illustrate how our score could be used to assess the exposure of pedestrians to virus transmission along streets. Our approach can be replicated in other cities facing a similar challenge of bringing life back to the streets while minimising transmission risks.

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A wealth of indicators and indicator sets, structured in the form of standards (e.g., ISO, OECD, SDG Index, etc.), have been developed and proposed over the past decades to define and measure urban sustainability. Unfortunately, literature studies confirm that there is a lack of knowledge systematisation for such indicator sets in the sustainability assessment domain. In this chapter, we present and describe a formal representation of knowledge about sustainability indicators and their interrelationships, through the development of a domain ontology. To increase the potential for its adaptability and reuse, as well as to facilitate its continuous redevelopment, we make the developed ontology available online, through a dedicated web portal.@en

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep learning approach that takes storefronts from street-level imagery as input, and directly provides the geo-location and type of commercial function as output. Our method showed a recall of 89.05% and a precision of 88.22% on a real-world dataset of street-level images, which experimentally demonstrated that our approach achieves human-level accuracy while having a remarkable run-time efficiency compared to methods such as Faster Region-Convolutional Neural Networks (Faster R-CNN) and Single Shot Detector (SSD).

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Street-level imagery contains a variety of visual information about the facades of Points of Interest (POIs). In addition to general mor- phological features, signs on the facades of, primarily, business-related POIs could be a valuable source of information about the type and iden- tity of a POI. Recent advancements in computer vision could leverage visual information from street-level imagery, and contribute to the classification of POIs. However, there is currently a gap in existing literature regarding the use of visual labels contained in street-level imagery, where their value as indicators of POI categories is assessed. This paper presents Scene-Text Semantics (ST-Sem), a novel method that leverages visual la- bels (e.g., texts, logos) from street-level imagery as complementary in- formation for the categorization of business-related POIs. Contrary to existing methods that fuse visual and textual information at a feature- level, we propose a late fusion approach that combines visual and textual cues after resolving issues of incorrect digitization and semantic ambiguity of the retrieved textual components. Experiments on two existing and a newly-created datasets show that ST-Sem can outperform visual-only approaches by 80% and related multimodal approaches by 4%.@en