The extent to which the built environment encourages people to walk in public spaces, hence the quality of being walkable or ‘walkability’ has long been associated with positive outcomes on people’s health. While various studies have developed indices to assess walkability, limited attention has been given to indices that reflect the influence of specific city characteristics on walkability. This study showcases the development of a city-specific walkability index through a participatory approach using Amsterdam as a case study. It explores the viewpoints of urban designers and policy-makers who work or reside in Amsterdam on what constitutes a walkable street and identifies the most significant walkability factors for Amsterdam. These factors are then quantified based on open-access datasets and integrated into a street-level weighted walkability index. The resulting walkability index underscores the importance of factors such as traffic and crime safety, quality of the pedestrian infrastructure, and proximity to public amenities in shaping residents’ decisions to walk in specific public spaces. Finally, this research underscores the importance of involving individuals through participatory methods, considering subjective perspectives, and acknowledging shared experiences within particular groups and spaces when assessing walkability.@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

Accessibility is a widely employed concept across a variety of disciplines to evaluate the degree to which individuals can reach a desired destination. 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. This hinders our ability to discern the encounter potential of different destinations. We address this gap by introducing the concept of co-accessibility to measure how accessible a given destination is to different individuals and demographic groups.@en

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

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

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

As we navigate the physical and digital world, we unknowingly leave behind an immense trail of data. We are informed about this via lengthy documents (e.g., privacy policies) or short statements (e.g., cookie popups). However, even when we know that data is collected, we remain largely unaware of its nature; what information it contains and how it relates to us. Data is highly personal. It contains and reveals information about our behavior and experiences scattered over time, which can be abstract and opaque even to us. Dataslip is an interactive installation where the construct of personal data is translated into a material and tangible representation in the form of a receipt or ‘personal data slip’. The receipt contains detailed information and illustrative examples of the data generated from our interactions with five different categories of products and services: (1) personalized public transport cards, (2) supermarket loyalty cards, (3) credit and debit cards, (4) wearables, and (5) mobile apps. Its length is proportional to the amount of data collected about us. With dataslip, we aim to reduce the distance between individuals and their personal data, elicit confrontation and invite people to question their role within the personal data ecosystems in which they are embedded.@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

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