Built Environment Characteristics Research Articles

The Role of the Built Environment in Supporting Older Adults' Engagement: A Narrative Literature Review.

This literature review aimed to summarize the existing knowledge surrounding the role of the built environment in supporting older adults' physical and social engagement and identify opportunities for future research and design that facilitates older adults' engagement at multiple environmental scales: from interior spaces to neighborhoods. Physical environments can support or impede older adults' physical and social engagement: willful, motivated involvement in meaningful activity or occupation. Research shows that engagement is a core component of health and well-being and relates to positive health outcomes, including reduced disease risk, better mental health, and improved physical and cognitive function. Thus, designing supportive built environments for engagement can yield significant, positive health and well-being impacts. A systematized search of five databases, a hand search, and an iterative screening process identified 55 studies for inclusion in this review. Through inductive thematic analysis, this review summarizes findings regarding the built environment's role in physical and social engagement in older age and design strategies to facilitate engagement and support health and well-being. Evidence indicates that built environment characteristics can influence older people's physical and social engagement in homes, neighborhoods, and care settings. The thematic analysis of the included studies identified three key themes concerning the relationships of built environment characteristics to older adults' engagement across multiple environmental scales: connection, access, and security. Built environments influence older people's physical and social engagement, significantly affecting their health, well-being, abilities, and longevity. Numerous design strategies can support older adults' engagement, but more research is needed.

Exploring non-linear effects of environmental factors on the volume of pedestrians of different ages using street view images and computer vision technology

Creating pedestrian-friendly neighborhoods and encouraging walking activities not only improves urban liveliness but also delivers health and environmental benefits. Previous research has largely focused on individual walking behavior, which often exhibits stronger associations with personal traits than with built environment characteristics. Pedestrian volume, a significant indicator of urban vitality and collective walking behavior, may have a stronger relationship with environmental characteristics. Moreover, prior studies often hypothesize a linear relationship between built environmental determinants and walking behavior. The exploration of potential non-linear influences may help policy makers and urban planners by identifying minimum, maximum, and optimal values of built environmental variables conducive for walking. Furthermore, studies focusing on the collective walking of older pedestrians remain scarce in the context of aging societies.In this study, we utilize street view imagery and advanced computer vision algorithms to estimate citywide pedestrian volumes and their corresponding age classifications (older adults vs. all). Additionally, we assessed both micro and macro built environmental factors. The possible non-linear impact is examined using Gradient Boosting Decision Tree (GBDT). Our findings reveal disparities in the influence of environmental determinants on the volume of older pedestrians versus that of all pedestrians. Also, the significance of environmental elements exhibits variations across different spatial resolutions. Further, both eye-level vegetation and building level show an inverted U-shaped influence on the pedestrian count.

Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.

Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality. To investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities. This cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (CHD), stroke, and chronic kidney disease (CKD). The study obtained aerial images from GSI covering census tracts in 7 cities (Cleveland, Ohio; Fremont, California; Kansas City, Missouri; Detroit, Michigan; Bellevue, Washington; Brownsville, Texas; and Denver, Colorado). The study used census tract-level data from the US Centers for Disease Control and Prevention's 500 Cities project. The data were originally collected from the Behavioral Risk Factor Surveillance System that surveyed people 18 years and older across the country. Analyses were conducted from February to December 2022. GSI images of built environment and cardiometabolic disease prevalence. Census tract-level estimated prevalence of CHD, stroke, and CKD based on image-based built environment features. The study obtained 31 786 aerial images from GSI covering 789 census tracts. Built environment features extracted from GSI using machine learning were associated with prevalence of CHD (R2 = 0.60), stroke (R2 = 0.65), and CKD (R2 = 0.64). The model performed better at distinguishing differences between cardiometabolic prevalence between cities than within cities (eg, highest within-city R2 = 0.39 vs between-city R2 = 0.64 for CKD). Addition of GSI features both outperformed and improved the model that only included age, sex, race, income, education, and composite indices for social determinants of health (R2 = 0.83 vs R2 = 0.76 for CHD; P <.001). Activation maps from the features revealed certain health-related built environment such as roads, highways, and railroads and recreational facilities such as amusement parks, arenas, and baseball parks. In this cross-sectional study, a significant portion of cardiometabolic disease prevalence was associated with GSI-based built environment using convolutional neural networks.

Industrial location and urban macroform prediction model based on cellular automata and multi-criteria decision-making methods (ILAUFM)

AbstractThe relationships between industrialization and urban regions, as well as technological advancements, innovations, and new work patterns and organizations, significantly impact the structuring of cities. Industrial sectors can become key drivers in shaping the form and macrostructure of a city over time. By examining and evaluating the existing system of structures and relations in the settlements in order to “estimate the land use differentiations” that may arise in the future and “to determine the spatial location of industrial facilities or sites,” a model has been developed. This model aims to predict potential “land use differentiations” and determine the “spatial location selection of industrial facilities or sites” in the future based on the analysis and evaluation of existing structures and relational systems in settlements. The model consists of the integration of the LUCAM, which is based on a cellular automata operating system, along with the “GIS-Based Integrated Industrial Area Location Selection Model,” which evaluates all data under the main headings of natural structure characteristics, socio-economic structure characteristics, and built environment characteristics using weighted overlay analysis. In this article, the general structure of the model developed within the scope of the research and İnegöl sampling are explained. The outputs of this model can be used for alternative separation for logistics centers, residential areas, secondary centers, reinforcement areas, and industrial sites in different settlements. In addition, in the research, a detailed model was designed by integrating decision-makers (central administrations, local administrations, registered users, NGOs) into the urban planning process. The proposed model can be used as a tool in designing transportation and other urban systems (distribution order of equipment areas, green area system, grading of centers, etc.).

Neighborhood built environment, obesity, and diabetes: A Utah siblings study

BackgroundThis study utilizes innovative computer vision methods alongside Google Street View images to characterize neighborhood built environments across Utah. MethodsConvolutional Neural Networks were used to create indicators of street greenness, crosswalks, and building type on 1.4 million Google Street View images. The demographic and medical profiles of Utah residents came from the Utah Population Database (UPDB). We implemented hierarchical linear models with individuals nested within zip codes to estimate associations between neighborhood built environment features and individual-level obesity and diabetes, controlling for individual- and zip code-level characteristics (n = 1,899,175 adults living in Utah in 2015). Sibling random effects models were implemented to account for shared family attributes among siblings (n = 972,150) and twins (n = 14,122). ResultsConsistent with prior neighborhood research, the variance partition coefficients (VPC) of our unadjusted models nesting individuals within zip codes were relatively small (0.5%–5.3%), except for HbA1c (VPC = 23%), suggesting a small percentage of the outcome variance is at the zip code-level. However, proportional change in variance (PCV) attributable to zip codes after the inclusion of neighborhood built environment variables and covariates ranged between 11% and 67%, suggesting that these characteristics account for a substantial portion of the zip code-level effects. Non-single-family homes (indicator of mixed land use), sidewalks (indicator of walkability), and green streets (indicator of neighborhood aesthetics) were associated with reduced diabetes and obesity. Zip codes in the third tertile for non-single-family homes were associated with a 15% reduction (PR: 0.85; 95% CI: 0.79, 0.91) in obesity and a 20% reduction (PR: 0.80; 95% CI: 0.70, 0.91) in diabetes. This tertile was also associated with a BMI reduction of −0.68 kg/m2 (95% CI: −0.95, −0.40) ConclusionWe observe associations between neighborhood characteristics and chronic diseases, accounting for biological, social, and cultural factors shared among siblings in this large population-based study.

The Urban Environment and Cardiometabolic Health.

Urban environments contribute substantially to the rising burden of cardiometabolic diseases worldwide. Cities are complex adaptive systems that continually exchange resources, shaping exposures relevant to human health such as air pollution, noise, and chemical exposures. In addition, urban infrastructure and provisioning systems influence multiple domains of health risk, including behaviors, psychological stress, pollution, and nutrition through various pathways (eg, physical inactivity, air pollution, noise, heat stress, food systems, the availability of green space, and contaminant exposures). Beyond cardiometabolic health, city design may also affect climate change through energy and material consumption that share many of the same drivers with cardiometabolic diseases. Integrated spatial planning focusing on developing sustainable compact cities could simultaneously create heart-healthy and environmentally healthy city designs. This article reviews current evidence on the associations between the urban exposome (totality of exposures a person experiences, including environmental, occupational, lifestyle, social, and psychological factors) and cardiometabolic diseases within a systems science framework, and examines urban planning principles (eg, connectivity, density, diversity of land use, destination accessibility, and distance to transit). We highlight critical knowledge gaps regarding built-environment feature thresholds for optimizing cardiometabolic health outcomes. Last, we discuss emerging models and metrics to align urban development with the dual goals of mitigating cardiometabolic diseases while reducing climate change through cross-sector collaboration, governance, and community engagement. This review demonstrates that cities represent crucial settings for implementing policies and interventions to simultaneously tackle the global epidemics of cardiovascular disease and climate change.

Usage factors influencing e-scooter energy consumption: An empirical investigation

Shared e-scooters are considered a sustainable urban travel solution, yet their environmental impact is notable, primarily due to a short lifespan and the use of Li-ion batteries. The objective of this paper is to explore the possible factors with an influence on e-scooter energy consumption. These influential factors include trip and rider attributes, built environment characteristics, road geometry, surface materials, light and meteorological conditions. This study empirically investigates factors influencing e-scooter battery energy consumption, utilizing real usage data from GPS-equipped free-floating e-scooters in four European cities at the individual trip level. GPS positions allowed for the specification of local road characteristics and ambient conditions. For this purpose Shuttle Radar Topography Mission, Open Street Maps and the Meteostat application were used. Energy consumption was explained with the use of linear regression models as a function of those parameters, while controlling user experience and battery usage intensity. An extensive dataset covering over 150,000 trips in Bordeaux, Hamburg, Malaga, and Paris and two months of observations was used to estimate the models. Results indicate that, while travel distance is the most influential factor, other usage aspects play a role. Cold weather, rain, wind, as well as stone-paved streets increase energy consumption. In addition there seems to be a battery age threshold beyond which energy consumption rises.

Road Traffic Injuries and the Built Environment in Bogotá, Colombia, 2015-2019: A Cross-Sectional Analysis.

Nine in 10 road traffic deaths occur in low- and middle-income countries (LMICs). Despite this disproportionate burden, few studies have examined built environment correlates of road traffic injury in these settings, including in Latin America. We examined road traffic collisions in Bogotá, Colombia, occurring between 2015 and 2019, and assessed the association between neighborhood-level built environment features and pedestrian injury and death. We used descriptive statistics to characterize all police-reported road traffic collisions that occurred in Bogotá between 2015 and 2019. Cluster detection was used to identify spatial clustering of pedestrian collisions. Adjusted multivariate Poisson regression models were fit to examine associations between several neighborhood-built environment features and rate of pedestrian road traffic injury and death. A total of 173,443 police-reported traffic collisions occurred in Bogotá between 2015 and 2019. Pedestrians made up about 25% of road traffic injuries and 50% of road traffic deaths in Bogotá between 2015 and 2019. Pedestrian collisions were spatially clustered in the southwestern region of Bogotá. Neighborhoods with more street trees (RR, 0.90; 95% CI, 0.82-0.98), traffic signals (0.89, 0.81-0.99), and bus stops (0.89, 0.82-0.97) were associated with lower pedestrian road traffic deaths. Neighborhoods with greater density of large roads were associated with higher pedestrian injury. Our findings highlight the potential for pedestrian-friendly infrastructure to promote safer interactions between pedestrians and motorists in Bogotá and in similar urban contexts globally.

Associations of perceived built environment characteristics using NEWS questionnaires with all-cause mortality and major cardiovascular diseases: The prospective urban rural epidemiology (PURE)-China study

BackgroundDespite increased literature focusing on the role of the built environment (BE) in health, few cohort studies have quantitatively analyzed neighborhood walkability environment in relation to the risk of death and cardiovascular disease (CVD). This longitudinal study aimed at evaluating the association between perceived BE attributeswith mortality and major CVD based on the Prospective Urban Rural Epidemiology study in China (PURE-China). MethodsThe PURE-China study recruited 47,931 participants aged 35–70 years from 12 provinces of China between 2005 and 2009. The perceived BE information, including land use, street, aesthetics, and safety, was collected using the neighborhood environment walkability scale (NEWS) questionnaire, with higher scores indicating a more favorable rating. Two primary outcomes are all-cause mortality and major CVD event. The Cox frailty model with random intercepts was used to assess the association between the perceived total BE/subscales score and outcomes. ResultsOf 32,163 participants included in this study, 19,253 (59.9 %) were women, and the mean (SD) age was 51.0 (9.5) years. After a median follow-up period of 11.7 years (IQR 9.4 – 12.2), we observed that one standard deviation higher of combined BE scores was related to a lower risk of all-cause mortality (HR = 0.85; 95 %CI, 0.80–0.90), and major CVD events (HR = 0.95; 95 %CI, 0.90–0.99). The subscales of perceived BE were related to a lower risk, although a few were not significant. Land use mix-diversity and safety from crime were the two most significant subscales. Stronger risks were observed among urban and female participants. ConclusionFavorable perceived BE characteristics were linked with a lower risk of all-cause mortality and major CVD events in Chinese population, especially in urban areas and females. Our findings can be used by policymakers to take action to mitigate the adverse effect of poor community conditions on health, such as improving local amenities and transport connectivity, providing building paths for walking, running and cycling

Ridesourcing regulation and traffic speeds: A New York case

Since 2019, New York City has enacted laws restricting ridesourcing services. This study investigates these regulations' impact on traffic using street-level speed records. We used a regression discontinuity design to compare weekly traffic speeds immediately before and after regulations. We find that the for-hire-vehicle driver application freeze, implemented in April 2019, significantly boosted street speeds in the immediate run. The effect varied widely across boroughs, road types, and built environment characteristics. Further evidence suggests a post-policy reduction in ridesourcing supply, indicating that the policy primarily impacted ridesourcing companies' scale and operations. However, the speed improvement appears to be temporary. Our findings offer valuable insights for crafting regulations targeting new urban mobility forms.

Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence.

Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities. This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence. In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.

Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction.

Accommodating spatio-temporal dependency in airline demand modeling

The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables.

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory

BackgroundObesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments. ObjectivesWe aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods. MethodsThe OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome. ResultsThe OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%. DiscussionThe index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

Measuring the Effect of Built Environment on Students’ School Trip Method Using Neighborhood Environment Walkability Scale

School trips affect different aspects, such as air pollution and urban traffic, and of personal wellbeing, such as students’ physical and mental health. The increasing concern about environmental sustainability has prompted a reevaluation of daily activities, including school transportation. While different factors that affect students’ school trips have been investigated in the literature, the effect of the built environment has been evaluated only sporadically in previous studies. To fulfil this knowledge gap, this study aims to investigate the effect of the built environment on students’ school trips by adapting and extending the well-known Neighborhood Environment Walkability Scale (NEWS) questionnaire. The questionnaire survey was conducted with parents from 36 schools in Yazd, Iran, providing a sample of 1688 students aged 7–18 years. The items from the NEWS questionnaire were placed in nine factors by performing factor analysis. The Multinomial Logit Regression model was applied to check the predictive power of these nine factors. It was found that the variables of land use mix-diversity, land use mix-access, crime, age, gender, household income and car ownership had a significant effect on students’ school trips. The more easily students have access to different places, the less they use public services and cars compared with the active travel mode. The use of public services and cars increases with the increase in crime rate along the route to school. The findings indicate that built environment features may impact students’ shift from traditional transportation modes to active alternatives, such as walking and cycling, contributing to the attainment of broader sustainability objectives.

The influence of the urban model on civic involvement and public time. A study applied to the commuting population of the Greater Metropolitan Area of San José, Costa Rica

A major problem for policymakers is to ensure that levels of citizen participation reflect the society in its diversity, but they must also ensure that those citizens who want to get involved have the means to do so. Despite empirical evidence of the role of urban planning in health, well-being, and livability, studies have not delved into the implications of how low-density sprawled urban forms influence civic involvement. This paper adds new evidence regarding the significance of urban form, built environment characteristics and more in general city planning, as intermediary factors between public and private life connected to civic engagement and public space use. Based on surveys and geographic data from the Greater Metropolitan Area of San José, in Costa Rica, the article investigates how the results of citizen participation change depending on whether residents are from a compact or dispersed study area, relating to urban rhythms and mobility metrics. Findings suggest that inhabitants of group A (lower-density neighborhoods) differ significantly from group B (compact-city areas) in four variables: means of mobility, commuting time, public space use, and civic activism, while having similar sociodemographic composition. Compared with residents of group A, group B report higher levels of use and knowledge of public spaces and civic participation. These two aspects constitute, the Urban Civic Participation Index, which, related to self-reported free time, forms what we define as Public Time. The main characteristics of the compact city (higher population and building density, high street connectivity, grid presence and proximity of services, mixed land use) were found to be positively related to the outcomes of accessibility to public space and community participation. In contrast, there are no significant differences in the availability of free time between the two groups. This study, along with others on the compact city, shows that there are other reasons to reduce daily commuting distances in cities and the need of urban time policies to redesign urban society's temporal dimension besides the known environmental impacts. It also raises the question of how low urban density affects people's time and disposition to participate in public life.

Urban vibrancy and air pollution: avoidance behaviour and the built environment

ABSTRACT Urban vibrancy can be captured by people’s out-of-home mobility. It is an essential indicator of the ‘liveliness’ of cities. Nowadays, many cities, however, are suffering from air pollution, which is harmful to human health. As such, health authorities are advising people, especially vulnerable groups with respiratory or other chronic diseases, to avoid out-of-home activities and travels on polluted days. With such avoidance behaviour, how would urban vibrancy decline in response to air pollution? How does the built environment moderate such declines? Applying spatial econometric approaches on panel data comprising social media check-in records, air quality index, meteorological conditions and built environment characteristics, this paper delves into the relationship between air pollution and urban vibrancy in Guangzhou using fine spatial (1km*1 km grids) and temporal (daily) data over two years from 2018 to 2019. The research findings provide clear evidence that air pollution is negatively associated with urban vibrancy. Furthermore, our study suggests that urban vibrancy of places with higher points of interest intensity and being farther away from the city centre are more affected by air pollution, whereas vibrancy of places with higher public transport convenience, smaller blocks, and higher functional mixture tend to be less vulnerable. The findings provide evidence-based implications for city governments to better understand and capture gains of urban vibrancy from pollution regulations and built environment design for sustainable urban development. The lessons learned are applicable to other cities with emerging air pollution problems.

Egyptian framework for an age-friendly built environment

In recent years, the Egyptian demographic has changed with an increasing number of older citizens who require special attention. The World Health Organization (WHO) identified 2020–2030 as the “Decade of Healthy Ageing” in response to the worldwide demographic changes. Many international organizations have identified the built environment features of age‐friendly communities, yet they have not been thoroughly analyzed in the Egyptian context. Today, urban designers and city planners have to establish age-friendly communities that can facilitate well-being in old age. The paper aims to provide “Egyptian framework for an age-friendly built environment E-FAB”, that can help architects, urban designers, and decision-makers evaluate Egyptian urban communities towards being age-friendly. The E-FAB is created and designed based on an analytical theoretical approach focused on active older people’s definitions, needs, their relation to the built and social environments, and previous practices of age-friendly cities and communities. In addition, a comparative analysis between previous studies in age-friendly communities. Then, the paper conducted semi-structured interviews targeting active older people, aiming to refine and rank the built environment features of the E-FAB by using SPSS. The findings revealed that the E-FAB is expected to be an initial simple evaluating framework that could contribute positively to improving Egyptian urban areas to be more friendly to older people and address their needs in the built environment.

Understanding factors influencing people with disabilities' participation in sports and cultural activities.

Improving people with disabilities' participation in sports and cultural activities benefits their physical and mental health. However, only a few studies have examined the factors that influence participation systematically. Using the survey data gathered from 4,319 disabled people living in a district in Wuhan, China, this study explored the impacts of sports and cultural activity participation in terms of individual physiological characteristics, socioeconomic factors, and built environmental features. The sports and cultural facility supply and the walkability index of their community environment were calculated to assess built environment features. Binary logistic regression models were also used to investigate the influence of the aforementioned variables. There is a significant positive correlation between sports and cultural activity participation and education (OR = 3.44, p < 0.01), employment status (OR = 2.04, p < 0.01), as well as the number of cultural facilities (OR = 1.33, p < 0.01) in the neighborhood area. No significant association was found between the inclination to participate frequently and individual psychological factors. Regarding people with disabilities' participation in sports and cultural activities, socioeconomic and built environment factors are more influential than individual psychological ones. The findings can give ideas for identifying targeted and comprehensive interventions to promote a healthy lifestyle for people with disabilities.

How do built environment characteristics influence bus use patterns across neighbourhood types in Beijing? A machine-learning analysis

There is a growing interest in the link between the built environment and travel behaviour in Chinese cities. However, the extent to which the built environment impacts transit use across various neighbourhoods under different planning and development strategies remains ambiguous. This study focuses on Beijing and identifies four distinct neighbourhood types: inner-city, transient, clustered suburban, and dispersed suburban. Utilising smart card data in conjunction with a machine-learning technique—XGBoost—we comprehensively explore the intricate connections between built environment characteristics and bus usage at the neighbourhood level. The results indicate that regional location, bus route availability, and density are more important in affecting bus trip frequency and duration. Meanwhile, the importance of these variables and associations with bus use differ substantially across different types of neighbourhoods. Notably, opposite effects are observed for road density and distance to the nearest subcentre in inner-city, transient, and suburban neighbourhoods. Based on these findings, there are important implications for neighbourhood planning to support bus usage. By considering the varying factors that impact bus use in different neighbourhoods, planners can develop more effective strategies to encourage and facilitate bus travel.