Influence Of Built Environment Research Articles

Morally “loaded” labels in the built environment influence perceptions and social judgments

Morally “loaded” labels in the built environment influence perceptions and social judgments

Built Environment Influences on Emotional State Loneliness among Young Adults during Daily Activities: An Experience Sampling Approach

Loneliness among young adults is a growing concern worldwide, posing serious health risks. While the human ecological framework explains how various factors such as socio-demographic, social, and built environment characteristics can affect this feeling, still, relatively little is known about the effect of built environment characteristics on the feelings of loneliness that young people experience in their daily life activities. This research investigates the relationship between built environment characteristics and emotional state loneliness in young adults (aged 18–25) during their daily activities. Leveraging the Experience Sampling Method, we collected data from 43 participants for 393 personal experiences during daily activities across different environmental settings. The findings of a mixed-effects regression model reveal that built environment features significantly impact emotional state loneliness. Notably, activity location accessibility, social company during activities, and walking activities all contribute to reducing loneliness. These findings can inform urban planners and municipalities to implement interventions that support youngsters’ activities and positive experiences to enhance well-being and alleviate feelings of loneliness in young adults. Specific recommendations regarding the built environment are (1) to create spaces that are accessible, (2) create spaces that are especially accessible by foot, and (3) provide housing with shared facilities for young adults rather than apartments/studios.

Research on the Spatiotemporal Characteristics of Block Vitality based on LBS Data and the Impact Mechanisms of the Built Environment -Taking the Third Ring Road in Fuzhou City as an example

Using location-based services (LBS) data from the third ring area in Fuzhou city, this study analyzes the spatiotemporal characteristics of block vitality by measuring the density of active population gatherings. It explores the spatiotemporal heterogeneity of block vitality based on a geographically and temporally weighted autoregressive model (GTWAR), considering built environment features such as POI density, functional mix, and road network density. Additionally, it investigates their interactions using a geographic detector and compares differences between weekdays and weekends across five daily periods. Key findings include:(1) The overall urban vitality in the region exhibits a spatial pattern of “one core, two centers, multiple sub-centers, and multiple clusters” and undergoes spatiotemporal dynamic changes across five periods, characterized by ‘dispersion, agglomeration, deep agglomeration, and dispersion.” (2) The marginal effects of POI density, functional mix, and road network density on block vitality exhibit instability in both spatial and temporal dimensions.(3)Interaction effects between two factors exert a more significant impact on block vitality than the effects of individual factors, with interaction types including dual-factor enhancement and nonlinear enhancement.(4)Differences exist in the spatiotemporal characteristics of block vitality, built environment influences, and their interactions between weekdays and weekends.

Does the built environment influence location- and trip-based sedentary behaviors? Evidence from a GPS-based activity space approach of neighborhood effects on older adults

ObjectiveEvidence on the influence of built environments on sedentary behaviors remains unclear and is often contradictory. The main limitations encompass the use of self-reported proxies of sedentary time (ST), the scarce consideration of the plurality of sedentary behaviors, and environmental exposures limited to the residential neighborhood. We investigated the relationships between GPS-based activity space measures of environmental exposures and accelerometer-based ST measured in total, at the place of residence, at all locations, and during trips. MethodsThis study is part of the CURHA project, based on 471 older adults residing in Luxembourg, who wore a GPS receiver and a tri-axial accelerometer during 7 days. Daily ST was computed in total, at the residence, at all locations and during trips. Environmental exposures included exposure to green spaces, walking, biking, and motorized transportation infrastructures. Associations between environments and ST were examined using linear and negative binomial mixed models, adjusted for demographics, self-rated health, residential self-selection, weather conditions and wear time. ResultsParticipants accumulated, on average, 8 h and 14 min of ST per day excluding sleep time. ST spent at locations accounted for 83 % of the total ST. ST spent at the residence accounted for 87 % of the location-based ST and 71 % of the total ST. Trip-based ST represents 13 % of total ST, and 4 % remained unclassified. Higher street connectivity was negatively associated with total ST, while the density of parking areas correlated positively with total and location-based ST. Stronger associations were observed for sedentary bouts (uninterrupted ST over 20 and 30 min). ConclusionImproving street connectivity and controlling the construction of new parking, while avoiding the spatial segregation of populations with limited access to public transport, may contribute to limit ST. Such urban planning interventions may be especially efficient in limiting the harmful uninterrupted bouts of ST among older adults.

Urban Quality of Life: A Systematic Literature Review

The built environment has great influence over the sustainability of societies as well as over people’s quality of life. Quality of life (QoL) is a broad concept that has different definitions across diverse bodies of knowledge. The social–cultural environment and the characteristics of the built environment influence people’s perception of QoL. This study aims to identify and analyse the factors that impact QoL and sustainable development in the urban context. A systematic literature review was developed to understand QoL concepts and to identify urban indicators that contribute to the multidimensional evaluation of urban QoL. The results include (1) a holistic overview of QoL concepts and indicators; (2) the proposal of a holistic urban QoL concept; (3) the identification of urban QoL dimensions and indicators that contribute to urban QoL evaluation. The main contribution of this study is its discussion of the multidimensional nature of QoL, including objective and subjective dimensions.

Untangling the relationships among residential environment, destination choice, and daily walk accessibility

Accessibility to opportunities is strongly correlated with travel behaviors. This research, in particular, centered on individual daily accessibility, which quantifies the total number of opportunities of each type that can be reached within a certain travel time from the destinations an individual visited in a day. Existing studies rarely focused on the interrelationships among people’s socio-demographic characteristics, residential built environment, destination choice, and daily accessibility simultaneously. Using survey data from California, the present study develops a daily accessibility indicator summarizing the individual daily total number of opportunities that is reachable within a given travel time based on individual daily activities. Subsequently, a structural equation model jointly estimates the relationships among these factors showing that people’s social status and the residential built environment influence directly daily accessibility to retail opportunities significantly. The residential built environment has no significant direct impact on daily ocean-view open space accessibility. Both social status and residential built environment have a significant indirect impact on daily accessibility via the mediator number of distinct destinations showing the importance of designing cities with increased destination variety that are better served by non-motorized networks and public transportation.

Built environment influence on the incidence of elderly pedestrian collisions in a medium-large city in southern Brazil: a spatial analysis

Trauma disproportionately affects vulnerable road users, especially the elderly. We analyzed the spatial distribution of elderly pedestrians struck by vehicles in the urban area of Maringa city, from 2014 to 2018. Hotspots were obtained by kernel density estimation and wavelet analysis. The relationship between spatial relative risks (RR) of elderly run-overs and the built environment was assessed through Qualitative Comparative Analysis (QCA). Incidents were more frequent in the central and southeast regions of the city, where the RR was up to 2.58 times higher. The QCA test found a significant association between elderly pedestrian victims and the presence of traffic lights, medical centers/hospitals, roundabouts and schools. There is an association between higher risk of elderly pedestrians collisions and specific elements of built environments in Maringa, providing fundamental data to help guide public policies to improve urban mobility aimed at protecting vulnerable road users and planning an age-friendly city.

Longitudinal analysis of public transport usage by older people using a latent Markov model

ABSTRACT Understanding trends in public transport usage by older people over the years is important for the long-term planning and development of age-friendly public transport. Despite extensive research on travel patterns of older people, very limited studies have explored the evolution oftheir public transport usage over the years. Using six years of smartcard transaction data and anonymous cardholder membership data from Shizuoka, Japan, we develop a latent Markov model to account for unobserved heterogeneity and to analyze the evolution of public transport usage by older people. The results indicate that age, gender, and residential built environment influence trends in publictransport usage by older adults over the years. There are five latentstates for older men and women, respectively, demonstrating theexistence of unobserved heterogeneity. Propensity to use public transport affected by unobserved factors tends to retain the same, especially for older people in the latent states of very low, low, and very high propensities.

Influence of Built Environment in Hygienic City in China on Self-rated Health of Residents

ObjectiveTo assess the subjective perception of residents on the built environment in hygienic cities and its relation to the self-rated health (SRH) status of residents, providing a basis for a better promotion on construction of health-supportive environments. MethodsThe online survey was adopted with the respondents recruited from residents living in Chaoyang District of Beijing in January 2021. With SRH level as the dependent variable, two-category logistic regression analysis was conducted to analyze the impact of the built environment in hygienic cities on the SRH status of residents. ResultsA total of 1,357 respondents were enrolled in this study. After controlling confounding factors, four aspects in the built environment in hygienic cities were detected remarkable influences on the SRH level of residents, including enough green space in the living area [odds ratio (OR) = 1.395, 95% confidence interval (95% CI): 1.055–1.845], clean and hygienic living environment (OR = 1.472, 95% CI: 1.107–1.956), residents’ confidence in drinking water safety in the living area (OR = 1.856, 95% CI: 1.354–2.544) and residents’ confidence in food safety in the living area (OR = 1.405, 95% CI: 1.027–1.921). ConclusionRegarding city construction, the government should focus more on the subjective perception of residents on built environments to build a supportive environment benefiting the health of residents.

Influence of Built Environment on Micromobility–Pedestrian Accidents

The use of micromobility (MM), a form of sustainable urban mobility which has expected effects such as reducing traffic congestion and greenhouse gases, has been rapidly increasing across the world. However, this growth has resulted in a considerable number of MM-related accidents. Most previous studies have explored MM user injuries to improve the safety of MM users, but the threat to pedestrians by MM is not yet fully understood. Therefore, this study aims to identify built environment factors which contribute to MM–pedestrian accidents by using MM–pedestrian crash data in Seoul, Korea from 2020 to 2021. Setting the spatial unit of analysis as a hexagonal grid with an apothem of 150 m, we developed the SZINB (spatial zero-inflated negative binomial) models for the accidents, controlling spatial autocorrelation, zero-inflated, and overdispersion. The model results showed that road intersections, sidewalks, and subway entrances have significant impacts on MM–pedestrian accidents. Thus, it should be suggested that safety measures for both MM and pedestrians are reducing MM speed limits in intersections, preventing MM use on sidewalks through modified sidewalk designs, and installing MM stations near subway stations.

Community time-activity trajectory modeling based on Markov chain simulation and Dirichlet regression

Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identical activity trajectories shared among the same group of people and neglects the influence of the environment. To further improve human time-activity trajectory modeling, this paper constructs community time-activity trajectory and analyzes how social-demographic and built environment influence people's activity trajectory based on Markov Chains and Dirichlet Regression. We use the New York area as a case study and gather data from American Time Use Survey, Policy Map, and the New York City Energy & Water Performance Map to evaluate the proposed method. To validate the regression model, Box's M Test and t-test are performed with 80% data training the model and the left 20% as the test sample. The modeling results align well with the actual human behavior trajectories, demonstrating the effectiveness of the proposed method. It also shows that both social-demographic and built environment factors will significantly impact a community's time-activity trajectory. Specifically: 1) Diversity and median age both have a significant influence on the proportion of time people assign to education activity. 2) Transportation condition affects people's activity trajectory in the way that longer commute time decreases the proportion of biological activity (eg. sleeping and eating) and increases people's working time. 3) Residential density affects almost all activities with a significant p-value for all biological needs, household management, working, education, and personal preference.

Understanding the Effect of Built Environment on Travel Behavior at Multiple Geographic Scales

Understanding the effect of built environment on travel behavior has been an important research issue in transportation, geography, and urban planning. Geographic scales for travel behavior research have started attracting attentions in recent years. In this research, we proposed a method to extract suitable geographic scales of built environment influence on travel behavior with different travel purposes. We designed a modeling framework to examine different types of corresponding scales that affect the relationships between travel behavior and the built environment. The scale-correspondence covers the aspects of geography, model, and indicators. In a real travel data supported case study, the results show a stronger and more explainable influence from built environment on travel distance and travel mode choices at scale-corresponding models than that with the non-scale-corresponding models. We discovered that all three types of scale correspondence can reduce the geographic-contextual uncertainty in both travel and built environment contexts. The results of this study offer methodological insights that may facilitate further research investigating travel behaviors.

Differences in Rural Built Environment Perceptions Across Demographic and Social Environment Characteristics.

The benefits of physical activity to health and obesity prevention are well established. However, attributes of the built environment influence participation in physical activity. The purpose of this study is to assess differences in perceptions of neighborhood walkability across demographic characteristics and social environment factors among rural residents. In a telephone survey, adult respondents (N = 448) across nine rural counties in a southeastern state answered questions about perceived neighborhood walkability, demographic characteristics, and their neighborhood social environment. Study recruitment for a convenience sample occurred through collaborations with local community organizations. Prevalence of destinations and barriers were estimated according to demographic and neighborhood social environment characteristics. Multiple logistic regression models assessed the association of demographic and neighborhood social environment characteristics with neighborhood walkability and calculated adjusted prevalence. Relaxing places to walk were the most often reported destinations (62.0%), followed by retail destinations (45.7%), and communal destinations (42.6%). Traffic was the most reported barrier to safe walking (40.4%), followed by animals (37.5%), and crime (30.5%). Perceptions of retail and communal destinations varied by age and race. Perceptions of traffic and crime as barriers varied by race, weight status, and income. Community belonging and social cohesion were associated with lower perceptions of barriers. Study findings present demographic characteristics and social environment attributes as key factors that shape perceived neighborhood walkability. Findings can help inform programmatic efforts and environmental change strategies to improve walking in rural areas.

Built Environments And Child Health in WalEs and AuStralia (BEACHES): a study protocol

IntroductionChildhood obesity and physical inactivity are two of the most significant modifiable risk factors for the prevention of non-communicable diseases (NCDs). Yet, a third of children in Wales and Australia...

How does street space influence crash frequency? An analysis using segmented street view imagery

Road crashes in metropolitan areas are challenging to prevent because they stem from the interactions of drivers and other system users in intricate built environments. Recent theories indicate that features of the built environment may induce unsafe driving by shaping users’ expectations and behaviors. The availability of street view imagery and methods of scene parsing create new possibilities for understanding how features of the built environment influence crash incidence. Most previous crash research using street imagery has applied manual processing methods. In this paper, we develop and apply automated machine parsed imagery in conjunction with self-explaining roads theory to consider how the street space visible to drivers influences crash frequency, using data from Columbus, Ohio, USA. While controlling for road network and area characteristics, we model the association of individual street elements with crash frequency. We then conduct a cluster analysis to define four types of street spaces, which are used in a subsequent model. We find that an Open Road type of metropolitan street space, characterized by more visible sky, roadway, and signage are associated with the greatest increase in crashes, and that the majority of these spaces exist on arterial or collector class road segments. We theorize that the visual similarity of this type of street space to highways promotes faster, less careful driving, which combines with their mixed land uses to make them the least safe. This points to the importance of traffic calming for such roads in high-activity areas, and the need to differentiate environments of non-highways from highways to promote careful driving.

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

The objective of this study was to identify pedestrian crash hotspots and assess the built environment to identify factors that increase the probability of a roadway corridor becoming a hotspot. Therefore, this research attempted to identify the pedestrian hotspots and then answer the question, do locations with a “specific built environment” have a higher probability of becoming a pedestrian crash hotspot? The study used 4 years of pedestrian crash data (2011 to 2014) from Miami-Dade County, one of the top three leading counties in the United States with the highest pedestrian fatalities. Pedestrian crash hotspots were first identified in the ArcGIS environment, integrating spatial analysis. The Bayesian complementary log-log (cloglog) model was then used to develop a hotspot risk prediction model, in which the likelihood of a corridor becoming a hotspot was linked to the built environment, and to demographic and socioeconomic factors. The density of bus stops, shopping centers, healthcare facilities, hotels, alcohol sales establishments, households without vehicles, traffic volume, presence of sidewalks, and presence of medians were found to significantly increase the likelihood of a corridor becoming a pedestrian crash hotspot. The methodological framework and findings of this study could be used while developing site-specific proactive measures to improve pedestrian safety.

Influence of Built Environment on Subway Trip Origin and Destination: Insights Based on Mobile Positioning Data

An accurate understanding of the relationship between subway trips and the built environment is crucial to meet people’s travel demands and promote the coordinated development of urban land. However, existing literature mainly examines this relationship at the station level, ignoring the variations between the surrounding areas of the station. Therefore, the present study aims to explore the influencing factors and spatial variations of subway trips at the grid level. First, a method was proposed to extract the subway trips using mobile positioning data in Chengdu, China. Then, two geographically weighted regression (GWR) models were adopted to examine the spatially varying relationships between the subway trip origin and destination and selected explanatory variables. The results show that the hotel, company, residential, tourist, bus, subway, road density, and transfer stations variables positively affect trip origin and destination. However, distance to the nearest subway station has a negative impact. Besides, the goodness of fit of the GWR model is better than that of the global regression model, indicating that the influence of the built environment on trip origin and destination varies across space. This study can guide planning departments and transportation agencies to implement target policies and create a convenient travel environment at the micro-level.

Temporal change in relationships between urban structure and surface temperature

Surface temperature influences human health directly and alters the biodiversity and productivity of the environment. While previous research has identified that the composition of urban landscapes influences the physical properties of the environment such as surface temperature, a generalizable and flexible framework is needed that can be used to compare cities across time and space. This study employs the Structure of Urban Landscapes (STURLA) classification combined with remote sensing of New York City’s land surface temperature (LST). These are then linked using machine learning and statistical modeling to identify how greenspace and the built environment influence urban surface temperature. Further, changes in urban structure are then connected to changes in LST over time. It was observed that areas with urban units composed of largely the built environment hosted the hottest temperatures while those with vegetation and water were coolest. Likewise, this is reinforced by borough-level spatial differences in both urban structure and heat. Comparison of these relationships over the period between 2008 and 2017 identified changes in surface temperature that are likely due to the changes in the presence of water, low-rise buildings, and pavement across the city. This research reinforces how human alteration of the environment changes LST and offers units of analysis that can be used for research and urban planning.

Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction

Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation.

Geographies of infections: built environment and COVID-19 pandemic in metropolitan Melbourne.

This paper uses spatial statistical techniques to reflect on geographies of COVID-19 infections in metropolitan Melbourne. We argue that the evolution of the COVID-19 pandemic, which has become widespread since early 2020 in Melbourne, typically proceeds through multiple built environment attributes – diversity, destination accessibility, distance to transit, design, and density. The spread of the contagion is institutionalised within local communities and postcodes, and reshapes movement practices, discourses, and structures of administrative politics. We demonstrate how a focus on spatial patterns of the built environment can inform scholarship on the spread of infections associated with COVID-19 pandemic and geographies of infections more broadly, by highlighting the consistency of built environment influences on COVID-19 infections across three waves of outbreaks. A focus on the built environment influence seeks to enact visions of the future as new variants emerge, illustrating the importance of understanding geographies of infections as global cities adapt to ‘COVID-normal’ living. We argue that understanding geographies of infections within cities could be a springboard for pursuing sustainable urban development via inclusive compact, mixed-use development and safe public transport.