While numerous studies have examined the negative effects of noise on people's noise-related annoyance, limited attention has been paid to the spatio-temporal nonstationarity of noise levels and annoyance during work. To address this research gap, we employed Global Positioning System (GPS) devices, mobile noise sensors, and activity diaries to collect continuous noise exposure data from 482 participants in Hong Kong over one working day and one non-working day, capturing both noise exposure and perceived noise annoyance during at-home and workplace-based work episodes. We then examined how annoyance responses to work-related noise vary by spatial (at home vs. workplace-based) and temporal contexts (working vs. non-working days), with particular focus on differences across different chronotype groups. The results revealed that: (a) significant differences exist in both noise exposure and annoyance between at-home and workplace-based work; (b) work noise exposure differs significantly between working and non-working days, whereas perceived noise annoyance does not; and (c) different chronotype groups exhibit distinct work patterns, noise exposure levels and annoyance. In particular, morning-type individuals experience lower work noise exposure and annoyance and demonstrate a moderating effect on the association between work noise and perceived annoyance. These findings enhance our understanding of potential stationarity bias in work-related noise exposure and perceived annoyance across spatial and temporal contexts, and provide valuable insights for the development of noise management policies tailored to different chronotype groups.

Extensive research on the relationships between environmental factors and older adults' health can inform urban planning for healthy and age-friendly cities. Past studies have predominantly considered such relationships as stable over space, with less emphasis paid to spatial non-stationarity and space-based rationale. Within Chinese cities' socio-economic spatial patterns, the different potential environment-health pathways corresponding to different space-based populations remain unclear. Further, limited types of environmental factors have been investigated, while diverse combinations of multiple environments have received less attention. This study thus examined how salient environmental factors may vary across spatial types and the spatial non-stationarity in the health-environmental relationship. Multi-source data characterized Guangzhou's multiple environments, and older adults' health patterns were mapped using self-rated health data. Subsequently, relationships between multi-environmental factors and older adults' health across spatial types were explored by ridge models. The results indicated that (i) There were obvious spatial differences in older adults' health, and significant relationships between multi-environmental factors and older adults' health were observed. (ii) Spatial nonstationarity in such relationships was evident, driven by space-based physicosocial contexts. (iii) Dominant factors notably varied across spatial types. The findings provide space-based planning insights for healthy and age-friendly cities.

People's emotional states and associated environmental exposure in geographic and social science are often assessed through recall measurements, such as the Day Reconstruction Method (DRM), which can overlook the need for real-time understanding and may encounter recall bias. This study examines recall bias in the relationship between mobility-based greenery and people's short-term happiness across activity contexts, including homes, workplaces, recreational activity sites, and travel routes. It compares Geographic Ecological Momentary Assessment (GEMA) with the DRM. GEMA is a method that integrates Ecological Momentary Assessment (EMA) with Global Positioning Systems (GPS). The results indicate that people's short-term happiness and mobilitybased greenery exposure were underestimated as measured via the DRM's recall data, particularly for outdoor recreational activity sites and travel routes. The GEMA effectively mitigates the recall bias issues inherent in the DRM.

Primary healthcare (PHC) acts as a cornerstone of public health. The 15-min city concept, advocating convenient access to essential urban services such as PHC within a 15-min walk, has gained traction globally. However, there remains a lack of understanding regarding the 15-min accessibility to PHC services, crucial for physically vulnerable individuals requiring regular medical attention. Previous healthcare accessibility studies often use the traditional floating catchment area (FCA) method, which overlooks demand and service supply inflation within catchment areas, potentially leading to inaccuracies in accessibility estimates. This study addresses the gap in understanding fine-grained 15-min accessibility to PHC services by employing an enhanced two-step floating catchment area (E2SFCA) method, which considers the inflation effect. Additionally, our study incorporates hot spot analysis (Getis-Ord Gi*), bivariate local Moran's I (Bi-LISA), and the Gini index to reveal inter- and intraparish accessibility inequalities across the 7 parishes in Macau. Findings highlight Nossa Senhora de Fatima parish as having the highest concentration of low-income public housing estates and significant inter- and intraparish 15-min PHC accessibility inequalities. This emphasizes the need for policymakers to consider integrating PHC facilities when developing public housing estates for low-income residents.

While some research has examined the time-lagged effect of restorative soundscape in specific contexts (e.g., parks), how the time-lagged effect of noise annoyance during people’s daily activities may vary across different temporal, spatial, and social contexts remains largely unknown. To address this research gap, we utilized Ecological Momentary Assessment (EMA) data to measure people’s real-time noise annoyance and activity diary data to assess their time-lagged noise annoyance. Real-time noise exposure was captured by portable noise sensors. We employed fixed effects ordered panel logistic regression to examine the effects of different thresholds of noise levels on people’s time-lagged noise annoyance, and how it varied across different temporal, spatial, and social contexts. The results indicated that: (1) there were significant time-lagged effects between participants’ real-time noise exposure and their time-lagged noise annoyance; (2) participants’ time-lagged noise annoyance associated with an activity was influenced by its temporal, spatial, and social contexts, particularly on weekdays; (3) participants’ time-lagged noise annoyance was significantly associated with measured noise levels, with the highest coefficient for 65 dB, followed by 70 dB; and (4) there were significant interaction effects between noise levels and temporal-spatial-social contexts on participants’ time-lagged noise annoyance (particularly when noise levels exceeded 70 dB). These findings enhance our understanding and have crucial implications for the implementation of noise control policies, which should consider not only noise levels but also the time-lagged effects of noise, particularly on weekdays, at outdoor recreational activity sites, as well as the potential vulnerabilities of individuals experiencing noise exposure in isolation.

Numerous studies have examined the relationship between workplace-based stationary sound levels and people's work satisfaction. However, few have considered individual-based dynamic sound levels in broader pre-work activity contexts, such as homes and commuting routes besides workplaces. To address this research gap, this study applied the temporality of environmental exposure and examined the time-lagged and cumulative effects of sound levels of pre-work activities in different activity contexts on people's work satisfaction. Individual-based continuous sound levels data and context-based work satisfaction data were collected using portable sound level sensors, Global Positioning Systems, and activity diary data. Partial least squares path analysis was used to examine the effect pathways of sound levels in broader activity contexts on people's work satisfaction. The study found that (1) Sound levels during work had a significant negative direct effect on people's work satisfaction. (2) Sound levels from pre-work commuting exhibited negative direct time-lagged effects on people's work satisfaction, while sound levels during pre-work dining had a positive direct time-lagged effect. (3) Sound levels during pre-work sleep had a significant negative indirect time-lagged effect on people's work satisfaction, mediated by sleep satisfaction. (4) Sound levels during pre-work commuting significantly strengthened the negative effects of sound levels during work on people's work satisfaction, whereas sound levels during dining significantly weakened these negative effects. These results indicate that individual-based mobile sound levels sensing can effectively capture exposure across various activity contexts and help examine its association with people's work satisfaction.

Public transport (PT) plays a crucial role as a fundamental urban amenity, facilitating access to destinations beyond walking distance. The concept of the x-minute city underscores the importance of having essential urban amenities like PT within a short active travel time, i.e., good PT walking accessibility. However, significant gaps persist in understanding two key aspects within the framework of the x-minute city: (1) PT walking accessibility that integrates high-granularity three-dimensional (3D) walkability constraints (including indoor footpaths) and accounts for inter-modal ridership weight differences, referred to as enhanced 3D PT walking accessibility (E3D-PTWA); and (2) vulnerability distribution arising from low E3D-PTWA and low income-a socioeconomic condition often accompanied by heightened PT dependency-thereby compounding vulnerability. This study addresses these gaps by focusing on Hong Kong as a case study of a transit-dependent and topographically complex city. Using the two-step floating catchment area (2SFCA) method and median-based measure, we compute E3D-PTWA at Hong Kong's fine-grained census unit, i.e., Large Subunit Groups (LSUGs), and its associated inequality and vulnerability across different districts. The results from this research reveal that districts in the New Territories and Kowloon exhibit a higher proportion of vulnerable LSUGs with both limited E3D-PTWA and low income, while districts on Hong Kong Island tend to have a lower percentage of such vulnerable groups. These findings emphasize the need to address the unequal distribution of PT services among the population and improve E3D-PTWA to promote sustainable and livable urban environments.

Air pollution has been broadly acknowledged as a significant contributor to various health issues. However, air pollution has conventionally been measured by fixed monitoring stations with limited spatiotemporal resolution. This research leverages real-time mobile sensing to conduct the spatiotemporal assessment of air pollution distribution and inequality in Hong Kong across multiple temporal levels including daytime/nighttime, weekday/weekend, and four seasons. Using the population-weighted exposure method and the Gini coefficient, the study reveals that districts like Sha Tin, Central & Western, Yau Tsim Mong, and Sham Shui Po consistently exhibit elevated pollution levels compared to the Hong Kong average across various timeframes. Furthermore, regarding intra-district pollution inequality, Large Subunit Groups (LSUGs) near the border with Mainland China within the districts of North, Tai Po, Tuen Mun, and Yuen Long in the New Territories, as well as LSUGs within Wan Chai near the district boundary with Central & Western on Hong Kong Island, and LSUGs within Kowloon City near the district boundary with Yau Tsim Mong and Sham Shui Po in Kowloon, exhibit higher levels of air pollution exposure compared to their intra-district LSUG counterparts located further inland. These findings help policymakers formulate targeted interventions to improve air quality across Hong Kong.

Equal exposure to quality-built environments fosters livable, inclusive cities. The neighborhood effect averaging problem (NEAP) suggests that daily mobility plays a crucial role in shaping environmental exposure. This study aims to unveil the NEAP in built-environment quality exposure. Street-view image data and mobile phone signaling data are coupled to measure built-environment quality in people's residential space and activity space. Subsequently, a conditional process analysis model is employed to investigate how daily mobility, income, and built-environment quality in residential space influence built-environment quality in activity space. The results indicate that (1) there is a significant disparity in built-environment quality exposure, although the disparity in activity space is smaller than that in residence; (2) income exerts a dual influence on built-environment quality in activity space through direct and indirect pathways, and the pathways could be moderated by high mobility; and (3) neighborhood effect averaging is evident at the individual level and manifests as a derived phenomenon associated with income groups. The findings provide insights for better serving environmental equality.

It is common to observe the epidemic risk perception (ERP) and a decline in subjective well-being (SWB) in the context of public health events, such as Corona Virus Disease 2019 (COVID-19). However, there have been few studies exploring the impact of individuals’ ERP within living space on their SWB, especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic. In this paper, we conducted a study with 789 participants in urban China, measuring their ERP within living space and examining its influence on their SWB using path analysis. The results indicated that individuals’ ERP within their living space had a significant negative effect on their SWB. The density of certain types of facilities within their living space, such as bus stops, subway stations, restaurants, fast food shops, convenience shops, hospitals, and public toilets, had a significantly negative impact on their SWB, mediated by their ERP within living space. Additionally, participation in out-of-home work and other activities not only increased individuals’ ERP within living space, but also strengthened its negative effect on their SWB.

Recent research has become increasingly interested in the on-linear associations between noise levels and people’s short-term noise annoyance. However, there has been limited investigation into measuring short-term noise annoyance and how different activity contexts may affect these non-linear associations. To address this research gap, this study measured people’s short-term noise annoyance using real-time Ecological Momentary Assessment (EMA) data and the Day Reconstruction Method’s (DRM) recalled data. Corresponding noise levels were captured using Global Positioning Systems and portable noise sensors. Employing the Shapley additive explanations method, we examined the non-linear associations between noise level and people’s real-time and recalled noise annoyance across different activity contexts. The results indicated that 1) People had greater sensitivity to noise levels in real-time annoyance (non-linear association threshold: 60 dB) compared to recalled annoyance, which had a higher non-linear association threshold of 70 dB. 2) The non-linear associations between noise level and people’s real-time/recalled noise annoyance varied between different activity contexts. People tended to be more sensitive to noise in real-time annoyance than recalled annoyance on travel routes and at workplaces. 3) Among the factors examined, the contribution of noise level varied across activity contexts. Noise level contributed more significantly to people’s real-time noise annoyance in outdoor recreational sites and on travel routes. These findings enhance our understanding of the non-linear association between noise level and people’s short-term noise annoyance, moving beyond the linear paradigm. Policymakers should consider the non-linear relationships and different activity contexts when implementing noise control measures.

Ecological conservation red line is an important initiative for ecological civilization construction in China. Studies and works have been conducted to delineate the ecological conservation red line using remote sensing and manual survey methods. However, the traditional ecological assessment methods ignore the impact of dynamic elements on the ecological environment, and a permanent monitoring system for ecological conservation red line is still necessary. Therefore, this paper aimed to build a daily monitoring system of the ecological conservation red line from the perspective of ecological soundscape, combining deep learning and traditional Geographic Information System (GIS) methods. We analyzed the main factors affecting the ecological soundscape to provide a benchmark for selecting sites for permanent soundscape monitoring. Initially, this study developed the first classification standard of soundscape elements for daily monitoring of ecological conservation red line concerning the "ecological-production-living spaces" concept and existing literature on soundscape and built a deep learning model for training, with training and validation accuracies of 89.88% and 72.41%, respectively. The identification results of this model confirmed that the solution of intelligent monitoring through soundscape is reliable in daily monitoring. Then, the deep learning model was employed to classify and predict the soundscape collected from the case sites and effectively identify the areas with severe ecological encroachment by calculating the proportion of ecological soundscape elements, performing the simulation of daily monitoring, which is important guidance for the actual ecological conservation red line monitoring. Finally, a regression model was used to analyze the spatial characteristics of ecologically vulnerable areas, with an R 2 of 0.472. The regression model showed that nighttime light intensity had the greatest influence on ecological soundscape, followed by the distance from residential areas, spatial centrality, and road distance. These factors should be considered when implementing permanent soundscape monitoring sites. In addition, Normalized Difference Vegetation Index (NDVI) and topography were not significant in the regression model, which confirmed the shortcomings of considering only the ecological background and ignoring the dynamic biological elements. Daily monitoring should capture dynamic encroachments so that major ecological damage can be detected before it occurs. Therefore, the innovative integration of soundscape elements into the monitoring system meets the basic needs of monitoring ecological conservation red lines daily.

With the rapid growth in both urbanization and the ageing of the population, elderly migrants have become a more prominent group in urban China. Previous studies have shown that elderly migrants are a vulnerable group in terms of subjective well-being (SWB) and studies have emphasized the role of their socioeconomic status (SES) and family-related attributes (FRA) on their SWB. However, there is less attention on whether the perceived residential environment (PRE) to which elderly migrants are exposed and their social interactions have some effect on their SWB. To fill this research gap, street view images and questionnaire survey data from Guangzhou, China were collected. The association between PRE, social interactions, and elderly migrants' SWB was examined within a comprehensive framework using structural equation models. The results indicated that elderly people who had migrated within China had lower levels of SWB than Guangzhou-born elderly adults. Social interactions mediated the effects of SES, FRA, and perceived environment on the SWB of elderly migrants. After controlling for SES and FRA, a livelier and safer PRE was directly positively associated with elderly migrants' SWB (coefficient = 0.181 at the 1% level) and indirectly associated with elderly migrants' SWB through social interactions with local friends (coefficient = 0.035 at the 1% level) and with neighbours (coefficient = 0.014 at the 5% level). These results suggest that increased social interactions and the creation of a better PRE would benefit elderly migrants' SWB in the context of active ageing.

A tremendous amount of research use questionnaires to obtain individuals' fear of crime and aggregate it to the neighborhood level to measure the spatial distribution of fear of crime. However, the cost of using questionnaires to measure the large-scale spatial distribution of fear of crime is high. The built environment is known to influence people's perceptions, including fear of crime. This study develops a machine learning model to link built environment extracted from street view images to fear of crime obtained from questionnaires, and then applies this model to extrapolate fear of crime for neighborhoods without the questionnaires. Using massive street view images and a survey among 1,741 residents in 80 neighborhoods in Guangzhou, China, this study developed a novel systematic approach to measuring large-scale spatial fear of crime at the neighborhood level for 1,753 neighborhoods. This is the first study to measure fear of crime at the neighborhood level for a metropolitan area of nearly 20 million people. The integration of survey data and street view images provides an opportunity to develop a more effective way to measure the spatial distribution of fear of crime. This approach could be applied to map other types of perceptions at a spatial resolution of the neighborhood level.

While there are plenty of studies on the effects of neighborhood and park greenness on personal overall satisfaction and walking behavior, the relationship between street greenness exposure and walking satisfaction has received limited attention. Also, the possible pathways by which street greenness exposure affects walking satisfaction need to be further examined. To fill these research gaps, we measured eye-level street greenness using street view images, machine learning techniques and global position systems. A structural equation model was used to examine the mediating effects of objective noise and PM2.5 exposure and related subjective annoyance, on the relationship between street greenness exposure and people's walking satisfaction. The results showed that street greenness exposure not only had a significant direct effect on walking satisfaction, but also has a significant indirect effect on walking satisfaction through subjective environmental annoyances (including noise and PM2.5 annoyances) rather than through objective noise and PM2.5 exposures. Besides physical activity and social interaction, the indirect effect of street greenness exposure on walking satisfaction through subjective environmental pollution annoyance accounted for about 17.39% of the total effect and cannot be ignored. These results suggest that the urban greenness layout policy should not only consider residential greenness but should improve people's environmental perception and walking satisfaction by allocating more greenness on streets with high noise and PM2.5 levels.

近年来,国内关于空间活力影响因素的研究层出不穷,但缺乏对人本导向下公众感知的街道空间品质等非物质层面因素的考虑.文章利用手机信令数据测度空间活力,基于深度学习和公众街景感知的评分结果测度空间品质,并将POI丰富度、时间可达性、房屋租金等因素作为控制变量,系统分析了空间品质与空间活力的关系.研究结果显示:从全局看,空间品质对空间活力的分布具有一定的解释力,但相关性较弱;从局部看,不同维度的空间品质对空间活力的影响具有较强的空间异质性,在城市空间中呈现较为复杂的关系;在不同类型的空间中,空间品质对空间活力影响作用的差异主要与区位相关,且空间品质优化对空间活力的提升作用需要具备一定的物质先决条件.因此,在实际的空间改造工作中,应根据空间现有的物质条件判定是否可以采取品质优化的方式提升活力;需考量空间安全感、热闹感、美丽感、脏乱感等维度的品质在不同类型的空间中对居民活动的差异化影响,并因地制宜地采取改造策略.

Previous literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known about the effect of street-view neighborhood greenery on perceived neighborhood safety. Using a deep learning approach, we derived greenery from a massive set of street view images in central Guangzhou. We further tested the relationships and mechanisms between street-view greenery and fear of crime in the neighborhood. Results demonstrated that a higher level of neighborhood street-view greenery was associated with a lower fear of crime, and its relationship was mediated by perceived physical incivilities. While increasing street greenery of the micro-environment may reduce fear of crime, this paper also suggests that social factors should be considered when designing ameliorative programs.

流动老年人是中国老龄化和城市化家庭迁移背景下一个不可忽视的群体,日常活动及其派生的社会交往和健康行为是影响他们身心健康的重要方面.通过梳理已有研究,将流动老年人分为务工型、休闲度假型、随迁型3种类型,并提出了基于日常活动的流动老年人身心健康影响框架,即不同类型的流动老年人由于养老模式的不同,决定了其日常活动及其派生的社会交往和健康行为存在一定的差异,进而影响他们的身心健康.同时,流动老年人的日常活动和健康行为,又受宏观层面户籍、养老、就医体系制度环境、中观层面活动空间环境和微观层面老年人不同社会经济属性的影响.因此,在"十四五"规划提出的积极老龄化战略下,需要为流动老年人提供差异化分类公共服务,优化其日常活动环境,提供精细化监测和管理日常活动与健康行为技术支持等,以积极应对流动老年人日常活动和身心健康面临的问题和挑战.

Public health has become a key issue in urban study particularly under the background of China's urban transformation.Previous research has little concerned about residents' health from a perspective of daily activities, and most of the existing studies were conducted in cities of Western developed countries.Based on a questionnaire survey, a structural equation model was applied to explore the relationship between the built environment, daily activities, and health of different gender groups.It is concluded that there exist different paths and degrees of influence of different gender groups.Built environment and daily activities have greater influence on women' s health.Health disparities also exist between female subgroups.As a vital variable, daily activity helps to understand the gender difference of effects on health.These conclusions broaden the framework of health influencing factor and mechanism study, and deepen the understanding of health in gender roles from a perspective of daily activities.

The relationship between car travel and built environment is one of the hot issues in the urban studies.The existing researches mainly focus on the respective effects on car travel of individual demographic variables and built environment variables on car travel.Under the background of China's urban transformation and the social space differentiation,a growing number of scholars have examined the relationship between residents' demographics and their residential built environment.Quantifying the relative roles of the individual social attributes and the built environment in influencing car travel has a policy implication.This study applied a multilevel 1ogit model which contains individual-level variables and neighbourhood-level variables to explore the impact on car use.It is concluded that urban residents' working-day car travel is influenced by multilevel variables associated to neighbourhood types.Most of the variation in the travel mode choice is caused by the difference among neighbourhoods.Travel mode choices have a strong neighbourhood contextual effect on car travel.As for the individual level,lower income and education level,collective unit profession,less minors in a family may help refrain the choice of automobile travel.As for the community level,improving bus stops density,building density,land mixing degree and commercial accessibility may help reduce car use ratio of working-day activities' travel.The mechanism of the relationship between two kinds of variables lies in the strength of market effect on urban residential space reconstruction.The residents who have similar social and economic attributes tend to choose the same type of neighbourhood,which has a similar built environment.These conclusions help us to have a better understanding of the mechanism behind the urban residents' working-day car travel and provide suggestions to alleviate the traffic problem by adjusting the multilevel variables in the similar type of community.