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The design of urban streetscapes influences our perception of the city but also affects behaviour and well-being. In this paper, we analyse if incivilities and more particularly illegal dumping are related to the urban context, at a micro scale (presence of trees, sidewalk width,...) and a meso scale (urban fabric typology: historical, residential, industrial,…). Illegal dumping is costly for the community, is a major inconvenience for residents, and can lead to a feeling of insecurity. To simplify the reporting of nuisances, apps have been developed for citizens such as Fix My Street, launched in Brussels, Belgium in 2013. It enables to report incidents such as broken public lamps or blocked sewer. An 'Illegal dump' category was added in 2017 and has since become the most reported type of incident: between July 2017 and February 2020, 46 744 illegal dumps were reported. We investigate what urban streetscape features are associated with such reports in Brussels. Unfortunately, the use of Fix My Street is not spatially uniform nor exhaustive. To avoid this selection bias, we compare illegal dumps with a control group composed of other types of incidents (n=56,122). Logistic regressions (presence/absence) and geographically weighted analyses are performed to (spatially) explore the association between illegal dumping and some urban morphometrics. Results show that the urban environment is significantly associated with the probability of illegal dumping. On a meso scale, we observe fewer illegal dumps in office neighbourhoods and the green periphery, and many more in the historical fabric. On a micro scale, the typical street where dumping takes place is a narrow, quiet residential street with urban trees. A “broken window” effect is also observed: illegal dumping are more likely to reappear where there has already been a dump.

Background Mental health disorders appear as a growing problem in urban areas. While common mental health disorders are generally linked to demographic and socioeconomic factors, little is known about the interaction with the urban environment. With growing urbanization, more and more people are exposed to environmental stressors potentially contributing to increased stress and impairing mental health. It is therefore important to identify features of the urban environment that affect the mental health of city dwellers. The aim of this study was to define associations of combined long-term exposure to air pollution, noise, surrounding green at different scales, and building morphology with several dimensions of mental health in Brussels. Methods Research focuses on the inhabitants of the Brussels Capital Region older than 15 years. The epidemiological study was carried out based on the linkage of data from the national health interview surveys (2008 and 2013) and specifically developed indicators describing each participant’s surroundings in terms of air quality, noise, surrounding green, and building morphology. These data are based on the geographical coordinates of the participant’s residence and processed using Geographical Information Systems (GIS). Mental health status was approached through several validated indicators: the Symptom Checklist-90-R subscales for depressive, anxiety and sleeping disorders and the 12-Item General Health Questionnaire for general well-being. For each mental health outcome, single and multi-exposure models were performed through multivariate logistic regressions. Results Our results suggest that traffic-related air pollution (black carbon, NO2, PM10) exposure was positively associated with higher odds of depressive disorders. No association between green surrounding, noise, building morphology and mental health could be demonstrated. Conclusions These findings have important implications because most of the Brussel’s population res

Mental well-being in cities is being challenged worldwide and a more detailed understanding of how urban environments influence mental well-being is needed. This qualitative study explores neighborhood factors and their interactions in relation to mental well-being. Individual semi-structured walking interviews were conducted with 28 adults living in the Brussels-Capital Region. This paper provides a detailed description of physical neighborhood factors (green-blue spaces, services, design and maintenance, traffic, cellphone towers) and social neighborhood factors (neighbor ties, neighbor diversity, social security) that link to mental well-being. A socio-ecological framework is presented to explain interactions among those neighborhood factors, and personal and institutional factors, in relation to mental well-being. The findings are linked to existing concepts and theories to better understand the mechanisms underlying the associations between the urban neighborhood environment and mental well-being. Finally, implications of the walking interview method are discussed.

1. Introduction Understanding the relationship between urban environment and mental health is a challenging exercise (Pelgrims et al., 2021). The issue of nonresponse and incomplete data, that often confronts population studies such as health surveys, further complicates the matter (Berete et al., 2019; Boshuizen et al., 2006; Burzykowski et al., 1999; Volken, 2013). In the case of the Belgian Health Interview Survey (BHIS), three types of non-response can be highlighted: (i) initial nonresponse: refusal to participate in the survey (Van der Heyden et al., 2014), not addressed in this paper, (ii) self-administered questionnaire (SAQ) nonresponse: for those over 15 years of age, the survey consists in two parts: a face-to-face (F2F) interview and a SAQ, the latter of which is not filled out, and (iii) item nonresponse: at different moments during the survey some questions were not answered by the participants. In this paper, the determinants of non-response are analysed, taking into account socio-economic as well as urban environmental factors; the impact of these non-responses on the analysis of the association between environment and mental health are discussed. 2. Background For the BHIS, determinants of SAQ nonresponse have already been highlighted by Berete et al (2019): nonresponse is more frequent among youngsters, non-Belgians, lower educational levels and lower income, residents of Brussels and Wallonia, and people with poor perceived health. Non-response is also strongly associated with the interviewer. Other studies (Boshuizen et al., 2006; Volken, 2013) on partial non-response have additionally shown that non-response is greater for men and for unskilled workers. These studies also include urbanity indicators but the results do not converge. 3. Data and Method Data used here are from the 2008 and 2013 BHIS, and are spatially limited to the Brussels-Capital Region, because the Region was oversampled compared to the rest of Belgium and because of data consi

The design of the urban streetscape influences our way of perceiving the city but also affects behaviour and well-being. In this paper, we analyse if incivilities and more particularly illegal dumping are related to the urban context, at a micro scale (presence of trees, sidewalk width,...) and a meso scale (urban fabric typology: historical, residential, industrial,…). Illegal dumping is costly for the community, is a major inconvenience for residents, and can lead to a feeling of insecurity. To simplify the reporting of nuisances, apps have been developed for citizens such as Fix My Street, launched in Brussels, Belgium in 2013. It enables to report incidents such as broken public lamps or blocked sewer. An 'Illegal dump' category was added in 2017 and has since become the most reported type of incident: between July 2017 and February 2020, 46 744 illegal dumps were reported. We investigate what urban streetscape features are associated with such reports in Brussels. Unfortunately, the use of Fix My Street is not spatially uniform nor exhaustive. To avoid this selection bias, we compare illegal dumps with a control group composed of other types of incidents (n=56,122). Logistic regressions (presence/absence) are performed to explore the association between illegal dumping and some urban morphometrics. Results show that the urban environment is associated with the probability of illegal dumping. On a meso scale, we observe fewer illegal dumps in office neighbourhoods and the green periphery, and many more in the historical fabric. On a micro scale, the typical street where dumping takes place is a narrow, quiet residential street with urban trees. A “broken window” effect is also observed: illegal dumping are more likely to reappear where there has already been a dump.

Background In environmental epidemiology, air pollution exposure is often estimated at the population level. To avoid the risk of exposure misclassification, one possibility is to interpolate air pollution measures at the residence through Geographical Information Systems. However, this might imply cumbersome administrative procedures. Data on air pollution annoyance from surveys can be an alternative to assess individual exposure to air pollution. This study investigates the association between air pollution annoyance and individual air pollution exposure. Methods Analyses were carried out based on a linkage of data from the Belgian Health Interview Survey (HIS 2008 and 2013; >15 years; n = 9347) and annual means of air pollution concentration at the residence. Self-reported air pollution annoyance was assessed through a five-point Likert scale. Statistical analyses included Spearman correlation coefficient, analysis of variance and multivariable ordinal logistic regressions (OLR). Results A significant exposure-response relationship was observed between long-term air pollution exposure and self-reported air pollution annoyance. However, Spearman coefficients were low (0.18-0.24), meaning a high heterogeneity of annoyance levels for a given exposure. In multivariable OLR, the odds of being annoyed by air pollution was 2.10 (95% CI: 1.86-2.67) times higher for each Interquartile range (IQR) increase in NO2 concentration (PM2.5: 1.77 [95% CI: 1.53-2.07], PM10: 1.61 [95% CI: 1.33-1.67], black carbon: 1.45 [95% CI: 1.27-1.67]). Air pollution annoyance depended largely on individual factors especially health status. Conclusions Air pollution exposure has a significant influence on self-reported air pollution annoyance. However, many other factors were found to influence annoyance, independently of the exposure. We found a limited validity of self-reported air pollution annoyance to assess individual long-term exposure to air pollution. Key messages Significant exposure

Urban landscapes are the backdrop of life in more than half of the world population. The streets and public spaces provide the setting for urban activities, with buildings, gardens and parks as background. The layout of this urban street landscape influences our way of perceiving the city but also, more broadly, affects well-being. To support this assumption, it is necessary to well characterize the urban fabrics in order to link it with well-being. Finding the spatial unit at the right scale has always been a challenge for spatial analysts. Administrative partitions (wards, municipalities) often do not match with urban landscapes: each spatial unit is heterogeneous in terms of built-up and not built-up surfaces hence not fitting with further spatial statistical analyses. Araldi and Fusco (2017) have developed a new method - Multiple Fabric Assessment method (MFA) - with the idea of characterizing urban fabrics as they are perceived by the pedestrians freely moving on the streets, so from the road network. The original MFA method does not include any vegetation indicator, while the beneficial impacts of urban green spaces on psychological, emotional and mental health has already led to numerous analyses. The MFA method is applied to Brussels (Belgium). The basic spatial unit (Proximity Bands) is first defined around each street segment. Each Proximity Bands unit is further described by 21 geometrical and spatial indicators of urban form as perceived by the city user; three of them are related to vegetation. Geostatistic analyses (ILINCS) are then achieved to identify local patterns of urban form features. Finally, Bayesian clustering is carried out to identify and describe family of urban fabrics. The application of this variant of the MFA method makes it possible to highlight twelve family of urban fabrics in Brussels. This result highlights elements specific to the city's history, such as bruxellisation - destruction of part of the historic center to build offices

Les paysages urbains sont le cadre de vie de plus de la moitié de la population mondiale. Les rues servent de support aux activités urbaines et à la mobilité, avec les bâtiments, les jardins et les parcs en toile de fond (Gehl, 1987). La disposition de ce paysage urbain influence la façon de percevoir la ville, mais aussi, plus largement, le bien-être (Guite et al., 2006). Pour soutenir cette approche, Araldi et Fusco (2017) ont développé une méthode, Multiple Fabric Assesment (MFA), dont le but est de caractériser les tissus urbains tels qu'ils sont perçus par les piétons se déplaçant librement dans les rues, c’est-à-dire à partir du réseau viaire. Cette méthode est adaptée et appliquée ici à la Région de Bruxelles-Capitale (19 communes du cœur de l’agglomération) afin d’en caractériser le tissu urbain. L'unité spatiale de base, dite Bande de Proximité, est définie autour de chaque segment de rue. Chacune de ces unités spatiales est définie par 21 indicateurs géométriques et spatiaux de la forme urbaine telle que perçue par le piéton. Ces indicateurs ont été sélectionnés et adaptés suivant les spécificités du contexte géographique, ainsi qu’en lien avec les objectifs de recherche ultérieurs (perspective de recherche en santé). Des analyses géostatistiques (ILINCS) sont réalisées afin d’identifier localement l’association spatiale des indicateurs morphométriques (clusters de grandes valeurs, de faibles valeurs et éléments non significatifs). Une classification bayésienne de ces pat-terns locaux est ensuite effectuée pour identifier et décrire les tissus urbains. Au final, l’application de cette variante de la méthode MFA permet de mettre en évidence douze tissus urbains dans le cas de Bruxelles. Ce résultat met notamment en évidence des éléments propres à l’histoire de la ville, tels que la bruxellisation – destruction d’une partie du centre historique pour construire des bureaux – et les « cités-jardins ». Dans le but de vali-der cette typologie, une série d’enquêt

Finding the spatial unit at the right scale for analyzing urban fabrics has always been a challenge for spatial analysts. Administrative partitions (wards, municipalities) often do not match with urban landscapes: each spatial unit is heterogeneous in terms of built-up and not built-up surfaces hence not fitting with further spatial statistical analyses. In the context of rising interest on the impact of the urban environment on (mental) health, we start the analysis from the road network for taking into account the perceived public spaces. Multiple Fabric Assessment methodology (MFA) (Araldi and Fusco, 2017) is applied to Brussels (Belgium) in order to investigate urban fabrics from the street point of view. Once the basic spatial unit is redefined around each street segment, the so-called Proximity Bands are described by a set of geometrical and spatial indicators of urban form as perceived by the city-user. Then, geostatistic analyses (ILINCS) are achieved to identify local patterns of urban form features. Finally, clustering is carried out to identify and describe neighborhoods. Despite the small extension of Brussels, a large amount of detailed geographic information is available: new indicators are proposed and tested within the MFA method. Urban vegetation, sidewalks, new visibility indicators are added to the traditional urban morphometric evaluation. With this new combination of indicators, MFA is now fitted to the context of studying the impact of urban spaces on mental health. Finally, results of this application are compared to administrative neighborhoods to evaluate their suitability in terms of morphological context.

info:eu-repo/semantics/published