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Automobile dependence and physical inactivity have become common health challenges for residents in large suburban residential areas. Limited literature has examined the associations between the built environment and active travel in such residential areas and the differences in these associations among residents from different neighborhoods. To avoid inaccurate results potentially derived from residence-based measures, we adopt a mobility-based approach for environmental exposure assessment. Using GPS data from 530 trips made by 98 participants in a large residential area in Shanghai, we investigate the relationships between neighborhood types, pollution perceptions, built environment features and active travel. The results indicate that residents in affordable and relocation housing make fewer active trips than those in market-rate housing, while the built environment seems to mitigate this difference. Sports facilities promote active travel while commercial facilities and road intersections discourage it. We identify significant interactions between the percentage of green space and neighborhood type, as well as floor area ratio and air pollution perception. Interventions promoting active travel include active-travel-friendly design for commercial facilities and road intersections, the provision of more sports facilities, a careful increase in floor area ratio, and the provision of more green space that is attractive to residents from different neighborhoods., (Copyright © 2023. Published by Elsevier Ltd.)

The built environment has been identified as a key factor for health intervention and obesity prevention. However, it is still unclear to what extent the built environment is associated with obesity and general health and to what extent such an association is mediated through variation in physical activity. This study aims to examine the associations between individual characteristics, the built environment, physical activity, general health and body mass index to reveal the pathways through which the built environment is associated with the prevalence of obesity. Using data from 1,788 adults aged 18 to 65 in Queensland from Wave 16 of the Household, Income, and Labour Dynamics in Australia survey, we use geographic information system-based methods to quantify built environment factors in 5D dimensions: Density, Diversity, Design, Distance and Destination accessibility. We then employ multi-level mixed-effect models to test the hypothesised relationships between individual characteristics, the built environment, physical activity, general health and body mass index. The results indicate that physical activity is positively associated with general health and negatively associated with the prevalence of obesity. Adjusting for individual characteristics, we find that built-environment factors have direct effects on physical activity but indirect effects on general health and obesity. Among these factors, greater green space exposure plays a key role in enhancing general health and reducing obesity. Low-density and car-dependent neighbourhoods can be activity-friendly and mitigate obesity if these neighbourhoods are also equipped with easy access to green space., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The rapid development of information and communications technology (ICT) in recent years leads to the emergence of cyber space and results in the fragmentation of human activities to some extent. Nevertheless, few studies focused on the exploration of fragmentation, the new characteristic of human activities, partially due to the limited access to proper datasets. In this work, massive Call Detail Records (CDR) data and Uniform Resource Locator (URL) data from millions of mobile phone users are used to extract the activity fragmentation in both cyber and physical spaces. The relationships between activity fragmentation in the two spaces and built environment factors are further examined at different geographical scales. The results show that: 1) the spatial distributions of physical human activity fragmentation (PHAF) and cyber human activity fragmentation (CHAF) are inversely correlated; 2) compared with PHAF, CHAF are less associated with the built environment, probably because cyber activities are less restricted in time and space; 3) CHAF are more influenced by physical fragmentations and the relationships are stable across different cites, indicating that cyber space cannot be completely detached from physical space; and 4) the impact of built environment on CHAF and PHAF are different across different cities. The findings not only shed deeper light on activity fragmentation, but also enlighten the future exploration of its association with both cyber and physical spaces. [ABSTRACT FROM AUTHOR]

Time allocation is closely related to life quality and is a potential indicator of urban space utilization and sociospatial differentiation. However, existing time allocation studies focus on how time is allocated to various activities but pay less attention to where individuals allocate their time. In the context of China's transformation, this study examines the differences in time allocation in different urban spaces between low- and non-low-income groups based on two methods, descriptive statistics and social area analysis. The results show that low-income participants' daily activities (especially work) are highly dependent on the central city area. However, they are at a disadvantage in accessing the central city area. Nevertheless, non-low-income individuals have diversified activity spaces and can better choose locations according to the purpose of activities and make fuller use of various types of urban areas. This study indicates that there are social differences in time allocation and urban space utilization among different income groups. The results obtained with regression models reveal that in addition to income, activity characteristics and built environment characteristics are significant factors affecting the differences. Social policies should support the equitable distribution of urban resources for different social groups, especially for vulnerable groups who live in affordable housing. [ABSTRACT FROM AUTHOR]

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Automatically identifying potential congestion spots in cities has significant practical implications for efficient urban development and management. It requires the ability to examine the relationships between urban built environment features and traffic congestion situations. This article presents a novel and effective approach for achieving the task based on a machine‐learning technique and publicly available street‐view imagery and point‐of‐interest (POI) data. The proposed multiple‐graph‐based convolutional network architecture can: (a) extract essential urban built environment features from street‐view imagery and neighboring POIs; (b) model the spatial dependencies between traffic congestion on road networks via graph convolution; and (c) evaluate the risk level of road intersections to emerging congestion situations based on local built environment features. We apply the model to Wuhan in China, and predict the potential congestion spots across the city. The results confirm that the model prediction is highly consistent (about 85.5%) when compared to the ground‐truth data based on traffic indices derived from a big taxi GPS trajectory dataset. This research enhances the understanding of traffic congestion situations under various geographic, societal, and economic contexts based on easily accessible road, street‐view, and POI datasets at large spatiotemporal scales. [ABSTRACT FROM AUTHOR]

Although exposure to air/noise pollution and greenspace has been found to significantly affect people's physical and mental health outcomes, there is still a lack of knowledge on what built-environment and socioeconomic factors are significantly associated with people's tri-exposure to air/noise pollution and greenspace. This study analyzes the associations between built-environment and socioeconomic factors and the tri-exposure to greenspace and air/noise pollution in Hong Kong. Based on individual-level activity data, real-time GPS trajectories, and exposure data collected by portable sensors as well as remote sensing satellite imagery, we employ multinomial logistic regression to determine the socioeconomic and built-environment factors that are significantly associated with the type of participants' tri-exposure at the grid cell level. The results show that higher transit nodal accessibility, building density, building height and land-use mix are significantly associated with a higher likelihood of being disadvantaged in terms of tri-exposure to air/noise pollution and greenspace. While more advantageous tri-exposures are significantly related to higher median monthly household income and sky view factor. Old high-rise high-density neighborhoods are more likely to be triply disadvantaged with low greenspace exposure but high air pollution and noise pollution exposure. The findings provide policymakers with critical reference in terms of addressing the inequalities in the tri-exposure outcomes. • This study used individual-level real-time GPS/exposure data and satellite imagery. • It examined individuals' tri-exposure to greenspace and air/noise pollution. • It analyzed the associations between built-environment and socioeconomic factors and tri-exposures. • Disadvantaged tri-exposures are associated with higher transit nodal accessibility, building density/height and land-use mix. • Advantageous tri-exposures are related to higher household income and sky view factor. [ABSTRACT FROM AUTHOR]

Increasingly, ‘place’, including physical and geographical characteristics as well as social meanings, is recognized as an important factor driving individual and community health risks. This is especially true among marginalized populations in low and middle income countries (LMIC), whose environments may also be more difficult to study using traditional methods. In the NIH-funded longitudinal study Mapa de Salud, we employed a novel approach to exploring the risk environment of female sex workers (FSWs) in two Mexico/U.S. border cities, Tijuana and Ciudad Juárez. In this paper we describe the development, implementation, and feasibility of a mix of quantitative and qualitative tools used to capture the HIV risk environments of FSWs in an LMIC setting. The methods were: 1) Participatory mapping; 2) Quantitative interviews; 3) Sex work venue field observation; 4) Time-location-activity diaries; 5) In-depth interviews about daily activity spaces. We found that the mixed-methodology outlined was both feasible to implement and acceptable to participants. These methods can generate geospatial data to assess the role of the environment on drug and sexual risk behaviors among high risk populations. Additionally, the adaptation of existing methods for marginalized populations in resource constrained contexts provides new opportunities for informing public health interventions. [ABSTRACT FROM AUTHOR]

Low-carbon travel of urban residents is an important issue of public concern for sustainable development of global cities under climate change. The carbon emissions generated by school trips occupy a significant proportion of the carbon emissions of people's travel and are increasing year by year. In this paper, we review the progress in research on carbon emissions generated by school trips, the socio-spatial differences in school trips, and the influence mechanisms and optimization simulations of carbon emissions from school trips. Two major dilemmas are observed in the human-environment relationship of low-carbon school trips. First, the dramatic reconstruction of social space in cities and the imbalanced development of public service supply in the rapid urbanization process have led to the socio-spatial heterogeneity of the complex environment of school trips, thus making it difficult to form an effective model of governance of urban space that is conducive to low-carbon school trips. Second, the interaction mechanisms related to school trips remain unclear due to the dynamic and multi-scale nature of the geographic environment, which hinders a clear description of the effects of the built environment on low-carbon school trips and the in-depth simulation of the optimization path. Therefore, in future research, researchers should focus on multi-paradigm crossover and fusion, carry out prospective exploration on the human-environment interactions associated with low-carbon school trips, enrich the research methods of human-environment relationship, discuss the mechanisms of the complete built environment on low-carbon school trips, simulate the spatial nested structure and refine the governance model to meet the needs for low-carbon school trips. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions. [ABSTRACT FROM AUTHOR]

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed. [ABSTRACT FROM AUTHOR]

The socio-spatial segregation experienced by migrants has attracted considerable attention and an increasing number of studies have examined segregation in migrants' daily activity space recently. However, research on activity diversity and spatial contact between local residents and migrants has been limited. This paper fills this knowledge gap by investigating the differences in the extensity, intensity, diversity and exclusivity of activity spaces among local residents, urban migrants and rural migrants based on their routine activities in suburban Shanghai, China. It finds that rural migrants have low daily mobility and are physically constrained, and there is spatial sorting of activity locations among different social groups. Neighborhood environment significantly influences activity space-based segregation: People who live in neighborhoods with higher POI density and better access to commercial establishments and public spaces have small activity spaces, while those who live in neighborhoods with mixed land use, better access to public transit, and higher street connectivity have more diverse activity participation. Neighborhoods with better public spaces and a lower land use mix promote shared activity spaces. This study uncovers the segregation suffered by migrants by examining the usage of urban space and spatial interactions among social groups, enhancing our understanding of activity space-based segregation in developing countries. • We analyze the activity space-based segregation emphasizing diverse locations visited in residents' daily lives. • There is significant activity space-based segregation among hukou groups. • Rural migrants have limited daily mobility and are physically constrained in activity space. • Local residents tend not to share activity spaces with migrants. • Residential neighborhood attributes significantly influence activity space-based segregation. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics. [ABSTRACT FROM AUTHOR]

Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services. [ABSTRACT FROM AUTHOR]

The rapidly aging population demands more public and academic attention to both the physical and psychological needs of older adults. Walking is one of the most favored forms of transportation and exercise for older adults. Hence, the conditions of walkways and the built environment are critical for the development of an older adults-friendly society. In this research, considering six indicators of greenness, walkability, safety, imageability, enclosure, and complexity, a novel quantitative evaluation model based on multi-source geospatial big datasets is proposed for assessing the age-friendliness of streets' walking environment from both the older adults' and experts' perspectives and has also been successfully applied in the case study of Xuhui District in Shanghai, China. Based on the survey with both groups of older adults and experts, the age-friendliness of the streets' walking environment from both perspectives was obtained, analyzed, and compared. The results show that among all sub-districts in Xuhui District, Tianping Road Sub-district and Hunan Road Sub-district have the best walking environment; and the roads surrounding expressways or arterial roads typically have lower levels of age-friendliness, while sub-arterial roads and access roads generally exhibit higher levels of age-friendliness according to both older adults and experts. It is also noted that streets with high age-friendliness for older adults tend to be outstanding in walkability and greenness, whereas experts prefer streets with high safety and walkability. In addition, the comprehensive analysis of the impact factors on age-friendliness further reveals a series of hidden significant influencing factors associated with the different preferences between older adults and experts. It is believed that our findings are useful for supporting policy-makers on how to improve the age-friendliness of streets and build age-friendly cities. • A novel quantitative evaluation model is proposed for assessing the age-friendliness of streets from dual perspectives. • Tianping Road Sub-district has the best walking environment in Xuhui District according to both older adults and experts. • Sub-arterial roads and access roads generally exhibit higher levels of age-friendliness than roads surrounding expressways. • Older adults give top priority to walkability and greenness of streets while walking, whereas experts prefer safety. [ABSTRACT FROM AUTHOR]

Research on environmental exposure and its impacts on people's mood has attracted increasing attention. Most studies focus on the spatiality of geographic contexts, but they neglect the influence of temporality in the relationships between environments and mood. To this end, a survey was conducted in January 2019 in Guangzhou, China, and measured data (micro-environments, built environments, EMA records, GPS trajectories, and activity logs) covering a weekday were collected from 125 participants. Then, multiple linear regression models are employed to examine and compare the associations between environments and mood based on three possible types of temporal responses (cumulative response, momentary response, and time-lagged response). The results indicate that there are great differences in environmental mood effects based on different types of temporal responses. Specifically, (i) for three types of temporal responses, exposure to PM 2.5 and noise have mood-blunting effects, whereas exposure to green spaces has mood-augmenting effects. (ii) For two types of temporal responses, higher temperature (in winter) may positively influence individual mood based on cumulative and time-lagged response, and higher POI density can positively affect mood based on cumulative and momentary response. (iii) Relative humidity may not have time-related effects on mood. Although all three types of temporal responses are observed in this study, the most significant manifestation is momentary response. These findings not only enrich theoretical research on environmental mood effects and temporality, but also inform the practice of more refined and humanistic urban planning, environmental governance, and public services. • Temporality of geographic contexts should be emphasized in environmental effects research. • All three types of temporal responses exist and have a role in affecting mood. • Momentary response is the most significant manifestation rather than cumulative and time-lagged response. • Various environmental factors have diverse time-related effects on mood. [ABSTRACT FROM AUTHOR]

To date, most studies have assessed individual exposure to neighborhood physical disorder (NPD) through the static residence-based approach, which ignores elements of human mobility and may lead to inaccurate estimates. This study assessed individual exposure to neighborhood physical disorder through the mobility-based approach using wearable cameras. The use of this approach allowed us to leverage innovative tools to accurately assess exposure to NPD in individuals' activities in space-time. We assessed the volunteers' exposure to neighborhood physical disorder by manually auditing pictures taken by wearable cameras on an online browser-based assessment platform. The results illustrated that wearable cameras can clearly capture the exposure while volunteers were engaged in travel behaviors. We also compared the proposed approach (mobility-based, using wearable cameras to take photos) with other approaches (with consideration of travel behaviors to varying degrees, using street view images) to demonstrate that wearable cameras can record individual exposure to neighborhood physical disorder accurately and conveniently, and the assessment results might be significantly different from those obtained by other approaches. Thus, the proposed approach is of great significance. • We proposed a mobility-based approach to assessing individuals' exposure to neighborhood physical disorder. • Assessing exposure by mobility-based approach produce different results than by static residence-based approach. • Wearable cameras can be used to assess individuals' exposure to neighborhood physical disorder accurately and conveniently. [ABSTRACT FROM AUTHOR]

Space–time fixity constraint is an important concept in transport geography, but the influence of the built environment around both people’s residence and activity locations is not clear. Due to the housing reform and rapid suburbanization in China, various types of residential communities and diverse built environments coexist in the suburbs. Comparing how people’s space–time fixity/flexibility varies among different community types and built environments can thus enhance our understanding of the transition process in Chinese cities. This study investigates how space–time fixity/flexibility and their relationships with the built environment vary among different types of residential communities in Beijing suburbs. Activity-travel diary and 7-day GPS tracking data of 709 respondents in Shangdi-Qinghe area of Beijing collected in 2012 were used. We investigate how variations in space–time flexibility are associated with built environment factors and four different community types: danwei communities, commodity housing communities, affordable housing communities and relocated housing communities, controlling for personal, household and activity attributes. The results suggest the influences of the built environments at residential place and activity place are different, and the relationships between space–time fixity and the built environments of different community types are different. Space–time fixity is not so sensitive to the built environment for residents in danwei communities and affordable housing communities. Gender and age differences in space–time fixity are not consistent with what was observed in Western countries. This seems to reflect the influence of unique social, cultural and family norms in China. [ABSTRACT FROM AUTHOR]

The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics. • COVID-19 clusters are significantly larger in suburbs than that in downtown. • Higher mixed-use and green/open spaces density are associated with smaller cluster sizes. • Metro stations with high centrality present the highest risk in the downtown. • Commercial and public service places exhibit the highest risk in the suburbs. • The worker dormitories in suburbs need specific attention. [ABSTRACT FROM AUTHOR]