Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents' mental health

How does urban blue space affect human well-being? A study based on the stimulus-organism-response theory

With rapid urbanization and social change, mental health issues have surged. Urban blue spaces (UBSs) offer a potential tool to increase well-being, yet the way in which sensory stimuli shape landscape perception and well-being remains underexplored. Intergenerational integration, a crucial aspect of well-being, refers to shared experiences and social interactions among different age groups, improving cognition and reducing loneliness. However, the role of UBSs in facilitating such interactions remains insufficiently studied. This research, grounded in the Stimulus-Organism-Response (S-O-R) framework, examines how perceived multisensory stimuli (visual, auditory, olfactory, and tactile) influence landscape perception and well-being. Structural equation modeling (SEM) of survey data (n = 532) reveals that perceived visual, auditory, and tactile stimuli significantly enhance landscape perception and well-being, while olfactory stimuli have no significant effect. Landscape perception mediates the relationship between visual, auditory, and tactile stimuli and well-being, but not for olfactory stimuli. These findings underscore the importance of optimizing sensory environments in UBSs to enhance psychological restoration. The study provides empirical insights for urban planners and policymakers, advocating for nature-based strategies that enhance visual aesthetics, maintain site quality, integrate natural soundscapes, and improve water accessibility to maximize restorative benefits and foster intergenerational inclusion.

The moderating influence of safety on green space's health benefits in Chinese urban communities

Green spaces are known to promote physical and mental health, but their benefits may be undermined by poor community safety. Few studies have explored how crime victimization or perceived safety influence the health benefits of different green spaces, particularly in rapidly urbanizing developing countries. Using multi-year data from the China Labor-force Dynamics Survey (CLDS) comprising 24,834 observations across 242 urban communities, this study examines whether community safety moderates the health benefits of green spaces in urban China. Green space types and characteristics were assessed through the presence of park/square and green coverage ratio, while health was proxied by self-rated health (SRH). Community safety was measured by residents' crime victimization experiences and perceived community safety. Results from hierarchical linear model revealed positive associations between both green coverage ratio and community safety with SRH. Crime victimization diminishes the health benefits of park/square, whilst low perceived safety hinders the health benefits of green coverage. These findings highlight the importance of considering both green space types and objective/perceived community safety to promote the health benefits of urban green spaces.

Neighborhood social environment and mental health of older adults in China: the mediating role of subjective well-being and the moderating role of green space

Introduction

With the continuous development of the global aging trend, the mental health of older adults has been a concern by the world. The living space of older adults is limited due to the decline of their activity function. Neighborhood environment, especially the neighborhood social environment, has become an important factor affecting the mental health of older adults. Therefore, this study explores the mechanism that influences the social environment of the neighborhood and the mental health of older adults, the mediating effect of subjective well-being (SWB), and the moderating effect of green space.

Methods

Based on the 2018 China Labor Dynamics Survey, this study used the structural equation model to explore the mediating effect of neighborhood social environment (neighborhood ties, social trust, community security) on the mental health of older adults through SWB and the moderating effect of green space.

Results

Social trust and community security are both directly and positively associated with older adults' mental health. At the same time, neighborhood ties, social trust, and community security can promote the mental health of older adults by positively affecting SWB, while green space has an enhanced moderating effect between neighborhood ties and mental health.

Discussion

This study enriches the empirical research on neighborhood social environment and mental health. First of all, older adults living in communities with good safety conditions and high social trust are less affected by negative emotions and tend to have good mental health. Second, deeper neighborhood ties, higher social trust, and safer community environments help older adults to be less disturbed by negative situations, have a positive effect on their SWB, and indirectly promote mental health. At the same time, green space can provide a place for older adults to socialize, enhance the positive impact of neighborhood ties on SWB, and further promote the mental health of older adults. Finally, this study suggests that the government and community managers pay attention to the construction of neighborhood social environment and green space, and provide support for "healthy community" and "healthy aging" planning.

Locality-based social media: The impact of content consumption and creation on perceived neighborhood crime, safety, and offline crime prevention

Locality-based social media (LBSM) allow members of the community to exchange news, connect with local people, and raise awareness of problems such as crime. This study aims to better understand the influence of LBSM on perceptions of community crime, safety, and crime prevention. Drawing on survey data from 1000 Australians, we assess the extent to which frequency of exposure to crime on LBSM and intensity of engagement on LBSM influence perceptions of crime, safety, and offline crime prevention behaviors. LBSM content creators perceive less crime and feel safer compared to individuals who only consume content on LBSM. Creators of content are also more likely than consumers to engage in offline crime prevention action. Our findings highlight the need to encourage more balanced engagement across all members of community social media. Smaller groups that contain only local residents may be best suited to achieve this outcome.

The four stages of neighborhood trust: Classic grounded theory

INTRODUCTION

Neighborhoods are often overlooked as a determinant of health. Among recent research, the focus on "place-based effects," due to prolonged residential environmental exposure, has been of particular interest. These studies' purpose is to identify and examine how a healthy neighborhood is intentionally created to describe a transferable process-driven theory.

METHOD

A classic grounded theory approach was used in these studies. Data sources include individual in-depth interviews, historical documents, and a member-checking focus group, collected over 3-years.

RESULTS

Analysis generated the Four Stages of Neighborhood Trust Model, which is nested within the context of perceived neighborhood safety. The theory outlines a social process of four stages of neighborhood trust: (a) rules-based agreements, (b) shared values, (c) cooperation, and (d) neighborhood belonging.

CONCLUSIONS

We present the development of a process-driven theory that may be useful for public health nurses as they engage neighborhoods in health promotion activities. The stage of trust development will aid the nurse in identifying what is needed to move to the next stage in a healthy neighborhood process.

Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day-and-night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments.

Assessing the Impact of Urban Environments on Mental Health and Perception Using Deep Learning: A Review and Text Mining Analysis

Understanding how outdoor environments affect mental health outcomes is vital in today's fast-paced and urbanized society. Recently, advancements in data-gathering technologies and deep learning have facilitated the study of the relationship between the outdoor environment and human perception. In a systematic review, we investigate how deep learning techniques can shed light on a better understanding of the influence of outdoor environments on human perceptions and emotions, with an emphasis on mental health outcomes. We have systematically reviewed 40 articles published in SCOPUS and the Web of Science databases which were the published papers between 2016 and 2023. The study presents and utilizes a novel topic modeling method to identify coherent keywords. By extracting the top words of each research topic, and identifying the current topics, we indicate that current studies are classified into three areas. The first topic was "Urban Perception and Environmental Factors" where the studies aimed to evaluate perceptions and mental health outcomes. Within this topic, the studies were divided based on human emotions, mood, stress, and urban features impacts. The second topic was titled "Data Analysis and Urban Imagery in Modeling" which focused on refining deep learning techniques, data collection methods, and participants' variability to understand human perceptions more accurately. The last topic was named "Greenery and visual exposure in urban spaces" which focused on the impact of the amount and the exposure of green features on mental health and perceptions. Upon reviewing the papers, this study provides a guide for subsequent research to enhance the view of using deep learning techniques to understand how urban environments influence mental health. It also provides various suggestions that should be taken into account when planning outdoor spaces.

Mental health in China: exploring the impacts of built environment, work environment, and subjective perception

Introduction

The shifting living and working conditions have profound impacts on the residents' mental health. However, current research in this field has not remarkable investigated.

Methods

This study used the China Labor-force Dynamic Survey (CLDS) dataset from 2018 and relied on a regression model to examine the effects of the built environment, work environment, and subjective perception on the mental health of Chinese citizens. It also considers the circumstances of both migrants and local residents.

Results

This study revealed significant correlations between mental health and greening space rate, road network density, commuting time, work feelings, community trust, economic satisfaction, and other factors. Additionally, the mental health of local residents was shown to be significantly affected by community security, while it shows no significance in migrants. Furthermore, a significant spatial autocorrelation was found in terms of mental health within the central and eastern regions of China.

Discussion

The findings of this study offer valuable insights that can be used to facilitate measures aimed at improving the mental health of residents and promoting the development of healthy cities.

Area-level Measures of the Social Environment: Operationalization, Pitfalls, and Ways Forward

People's mental health is intertwined with the social environment in which they reside. This chapter explores approaches for quantifying the area-level social environment, focusing specifically on socioeconomic deprivation and social fragmentation. We discuss census data and administrative units, egocentric and ecometric approaches, neighborhood audits, social media data, and street view-based assessments. We close the chapter by discussing possible paths forward from associations between social environments and health to establishing causality, including longitudinal research designs and time-series social environmental indices.

Integrating street view images and deep learning to explore the association between human perceptions of the built environment and cardiovascular disease in older adults

Understanding how built environment attributes affect health remains important. While many studies have explored the objective characteristics of built environments that affect health outcomes, few have examined the role of human perceptions of built environments on physical health. Baidu Street View images and computer vision technological advances have helped researchers overcome the constraints of traditional methods of measuring human perceptions (e.g., these methods are laborious, time-consuming, and costly), allowing for large-scale measurements of human perceptions. This study estimated human perceptions of the built environment (e.g., beauty, boredom, depression, safety, vitality, and wealth) by adopting Baidu Street View images and deep learning algorithms. Negative binomial regression models were employed to analyze the relationship between human perceptions and cardiovascular disease in older adults (e.g., ischemic heart disease and cerebrovascular disease). The results indicated that wealth perception is negatively related to the risk of cardiovascular disease. However, depression and vitality perceptions are positively associated with the risk of cardiovascular disease. Furthermore, we found no relationship between beauty, boredom, safety perceptions, and the risk of cardiovascular disease. Our findings highlight the importance of human perceptions in the development of healthy city planning and facilitate a comprehensive understanding of the relationship between built environment characteristics and health outcomes in older adults. They also demonstrate that street view images have the potential to provide insights into this complicated issue, assisting in the formulation of refined interventions and health policies.

Insights into parents' perceived worry before and during the COVID-19 pandemic in Australia: inequality and heterogeneity of influences

BACKGROUND

Excessive worry is an invisible disruptive force that has adverse health outcomes and may advance to other forms of disorder, such as anxiety or depression. Addressing worry and its influences is challenging yet crucial for informing public health policy.

METHODS

We examined parents' worries, influences, and variability before and during COVID-19 pandemic and across geography. Parents (n = 340) and their primary school-aged children from five Australian states completed an anonymous online survey in mid-2020. After literature review, we conceptualised the influences and performed a series of regression analyses.

RESULTS

Worry levels and the variables contributing to parents' worry varied before to during the pandemic. The proportion of parents who were "very worried all the time" increased by 14.6% in the early days of the pandemic. During the pandemic, ethnic background modified parents' worry and parents' history of daily distress symptoms was a significant contributor (p < 0.05). Excessive exposure to news remained significant both before and during the pandemic. The primary predictor of parents' worry before COVID-19 was perceived neighbourhood safety, while the main predictor during COVID-19 was financial risk due to income change. Some variable such as neighbourhood safety and financial risk varied in their contribution to worry across geographical regions. The proportion of worried children was higher among distraught parents.

CONCLUSION

Parents' worry during the health pandemic was not triggered by the health risks factors but by the financial risk due to income change. The study depicts inequality in the impact of COVID-19 by ethnic background. Different policies and reported virus case numbers across states may have modified the behaviour of variables contributing to the geography of parents' worry. Exposure to stressors before the COVID-19 pandemic may have helped parents develop coping strategies during stressful events. Parents are encouraged to limit their exposure to stressful news. We advocate for parents-specific tailored policies and emphasise the need for access to appropriate mental health resources for those in need. Advancing research in geographical modelling for mental health may aid in devising much-needed location-targeted interventions and prioritising resources in future events.

Does social capital aid in leveling the income gradient in child mental health? A structural analysis of the left-behind and not-left-behind Chinese children

BACKGROUND

Few prior studies have investigated the income gradient in child mental health from a socio-environmental perspective. In an age when child mental health problems in a rapidly changing social environment have become a worldwide issue, an understanding of the socio-environmental mechanisms of the income disparities in child mental health outcomes is imperative and cost-effective.

METHODS

By conducting structural equation analyses with Chinese nationally representative survey data, this study explored the family income gradient in child depression and its potential socio-environmental pathways at the neighborhood, family and school levels, differentiating left-behind and not-left-behind children.

RESULTS

We found a robust family income gradient in depressive symptoms. Neighborhood cohesion mitigated the income gradient in depressive symptoms by playing a suppression role. School social capital acted as a mediator. Neighborhood trust, neighborhood safety and family social capital played no significant impact. The mitigating and mediating roles of social capital components were significant among only the not-left-behind children.

CONCLUSIONS

To reduce income-related inequalities in child mental health in the long run, integrating policies that directly reduce poverty with policies that improve distal socio-environments is necessary.

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.

Urban greenspace and mental health in Chinese older adults: Associations across different greenspace measures and mediating effects of environmental perceptions

This study aimed to contrast the associations of street view-, land use- and satellite-derived greenspace measures with older adults' mental health and to examine the mediating effects of neighborhood environmental perceptions (i.e., noise, aesthetics and satisfaction with recreational opportunities) to explain potential heterogeneity in the associations. Data of 879 respondents aged 60 or older in Dalian, China were used, and multilevel regression models were conducted in Stata. Results indicated that the Normalized Difference Vegetation Index (NDVI), vegetation coverage, park coverage and streetscape grasses were positively correlated with older adults' mental health. The associations of exposure metrics measured by overhead view were stronger than those measured by the street view. Streetscape grasses had a stronger association with older adults' mental health than streetscape trees. Noise, aesthetics and satisfaction with recreational opportunities mediated these associations, but the strength of the mediating effects differed across the greenspace measures. Our findings confirm the necessity of multi-measures assessment for greenspace to examine associations with older adults' mental health in Chinese settings and can contribute to the realization of health benefits of urban greenspace.

The Relationship Between Built Environment and Mental Health of Older Adults: Mediating Effects of Perceptions of Community Cohesion and Community Safety and the Moderating Effect of Income

Many studies revealed a significant correlation between low-density built environment and the mental health of older adults in developed countries. However, scholars and decision-makers recently began to pay close attention to the effect of this relationship in high-density built environments and in developing countries. Using point-of-interest (POI) data from Baidu and data on 20 communities in Guangzhou, China, which were collected through a questionnaire survey, this study aimed to examine the relationship between built environment and the mental health of older adults as well as the physiological–psychological mediating paths between the two, so as to enrich the research on population aging in the high-density urban context in developing countries. The findings indicated that facility accessibility and distance to parks significantly positively correlated with the mental health of older adults and the number of public transit stations, and the distance to these stations significantly negatively correlated with the mental health of older adults. Also, the perceptions of community cohesion and community safety had a significant mediating effect between the built environment and the mental health of older adults. Furthermore, the moderating effect analysis results verified the moderating effect of income: with an increase in income, the perception of community cohesion enhanced the protection of the mental health of older adults and reduced the mediating effect of the perception of community safety. The results provided a reference for policy-makers and urban planners in their efforts to plan and build health-supporting communities and a healthy aging society.

Coupling Coordination Evaluation of Lakefront Landscape Spatial Quality and Public Sentiment

The comprehensive quality evaluation of the lakefront landscape relies on a combination of subjective and objective methods. This study aims to evaluate the coupling coordination between spatial quality and public sentiment in Wuhan’s lakefront area, and explore the distribution of various coupling coordination types through machine learning of street view images and sentiment analysis of microblog texts. Results show that: (1) The hot and cold spots of spatial quality are distributed in a contiguous pattern, whereas the public sentiments are distributed in multiple clusters. (2) A strong coupling coordination and correlation exists between spatial quality and public sentiment. High green visibility, high sky visibility, and natural revetment have remarkable positive effects on public sentiment. In comparison, high water visibility has a negative effect on public sentiment, which may be related to the negative impact of traffic-oriented streets on the lakefront landscape. (3) Lakefront areas close to urban centers generally show a low spatial quality–high public sentiment distribution, which may be related to factors such as rapid urbanization. This study can help planners identify critical areas to be optimized through coupling coordination relationship evaluation, and provides a practical basis for the future development of urban lakefront areas.

Perceived neighborhood environment, social capital and life satisfaction among older adults in Shanghai, China

This study examined the mediator role of social capital on the association between perceived neighborhood environment and life satisfaction among older adults in urban China, and further tested the moderating effect of gender in the above paths (i.e., from neighborhood environment to life satisfaction; from neighborhood environment to social capital; from social capital to life satisfaction). We used quota sampling approach to recruit 472 respondents aged 60 years old or older in Shanghai in 2020. From the perspective of structural equation modeling, multiple group analysis was conducted to examine the proposed hypotheses. The measurement model of social capital was well established in urban Chinese community contexts. Based on the whole sample, the results of the mediation model showed that social capital played a mediation role in the association between neighborhood environment and life satisfaction. Furthermore, the results of multiple group analysis showed that the association between neighborhood environment and cognitive social capital was only significant among older women. The findings highlight the role of neighborhood environment and social capital in building age-friendly communities.

Effect of Urban Green Space in the Hilly Environment on Physical Activity and Health Outcomes: Mediation Analysis on Multiple Greenery Measures

Background: Green spaces reduce the risk of multiple adverse health outcomes by encouraging physical activity. This study examined correlations between urban green space and residents’ health outcomes in hilly neighborhoods: if they are mediated by social cohesion, visual aesthetics, and safety. Methods: We used multiple green space indicators, including normalized difference vegetation index (NDVI) extracted from satellite imagery, green view index (GVI) obtained from street view data using deep learning methods, park availability, and perceived level of greenery. Hilly terrain was assessed by the standard deviation of the elevation to represent variations in slope. Resident health outcomes were quantified by their psychological and physiological health as well as physical activity. Communities were grouped by quartiles of slopes. Then a mediation model was applied, controlling for socio-demographic factors. Results: Residents who perceived higher quality greenery experienced stronger social cohesion, spent more time on physical activity and had better mental health outcomes. The objective greenery indicators were not always associated with physical activity and might have a negative influence with certain terrain. Conclusions: Perceived green space offers an alternative explanation of the effects on physical activity and mental health in hilly neighborhoods. In some circumstances, geographical environment features should be accounted for to determine the association of green space and resident health outcomes.

Associations Between Street-View Perceptions and Housing Prices: Subjective vs. Objective Measures Using Computer Vision and Machine Learning Techniques

This study investigated the extent to which subjectively and objectively measured street-level perceptions complement or conflict with each other in explaining property value. Street-scene perceptions can be subjectively assessed from self-reported survey questions, or objectively quantified from land use data or pixel ratios of physical features extracted from street-view imagery. Prior studies mainly relied on objective indicators to describe perceptions and found that a better street environment is associated with a price premium. While very few studies have addressed the impact of subjectively-assessed perceptions. We hypothesized that human perceptions have a subtle relationship to physical features that cannot be comprehensively captured with objective indicators. Subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations. To test the hypothesis, we both subjectively and objectively measured six pairwise eye-level perceptions (i.e., Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity). We then investigated their coherence and divergence for each perception respectively. Moreover, we revealed their similar or opposite effects in explaining house prices in Shanghai using the hedonic price model (HPM). Our intention was not to make causal statements. Instead, we set to address the coherent and conflicting effects of the two measures in explaining people’s behaviors and preferences. Our method is high-throughput by extending classical urban design measurement protocols with current artificial intelligence (AI) frameworks for urban-scene understanding. First, we found the percentage increases in housing prices attributable to street-view perceptions were significant for both subjective and objective measures. While subjective scores explained more variance over objective scores. Second, the two measures exhibited opposite signs in explaining house prices for Greenness and Imageability perceptions. Our results indicated that objective measures which simply extract or recombine individual streetscape pixels cannot fully capture human perceptions. For perceptual qualities that were not familiar to the average person (e.g., Imageability), a subjective framework exhibits better performance. Conversely, for perceptions whose connotation are self-evident (e.g., Greenness), objective measures could outperform the subjective counterparts. This study demonstrates a more holistic understanding for street-scene perceptions and their relations to property values. It also sheds light on future studies where the coherence and divergence of the two measures could be further stressed.

A Small Area Estimation Method for Investigating the Relationship between Public Perception of Drug Problems with Neighborhood Prognostics: Trends in London between 2012 and 2019

The correlation of the public’s perception of drug problems with neighborhood characteristics has rarely been studied. The aim of this study was to investigate factors that correlate with public perceptions in London boroughs using the Mayor’s Office for Policing and Crime (MOPAC) Public Attitude Survey between 2012 and 2019. A subject-specific random effect deploying a Generalized Linear Mixed Model (GLMM) using an Adaptive Gaussian Quadrature method with 10 integration points was applied. To obtain time trends across inner and outer London areas, the GLMM was fitted using a Restricted Marginal Pseudo Likelihood method. The perception of drug problems increased with statistical significance in 17 out of 32 London boroughs between 2012 and 2019. These boroughs were geographically clustered across the north of London. Levels of deprivation, as measured by the English Index of Multiple Deprivation, as well as the percentage of local population who were non-UK-born and recorded vehicle crime rates were shown to be positively associated with the public’s perception of drug problems. Conversely, recorded burglary rate was negatively associated with the public’s perception of drug problems in their area. The public are influenced in their perception of drug problems by neighborhood factors including deprivation and visible manifestations of antisocial behavior.

Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China

The COVID-19 pandemic undoubtedly has a great impact on the world economy, especially the urban economy. It is urgent to study the environmental pathogenic factors and transmission route of it. We want to discuss the relationship between the urban living environment and the number of confirmed cases at the community scale, and examine the driving forces of community infection (e.g., environment, ecology, convenience, livability, and population density). Besides, we hope that our research will help make our cities more inclusive, safe, resilient, and sustainable. 650 communities with confirmed COVID-19 cases in Wuhan were selected as the research objects. We utilize deep learning semantic segmentation technology to calculate the Visible Green Index (VGI) and Sky View Factor (SVF) of street view and use Partial Least Squares Structural Equation Modeling (PLS-SEM) to study the driving forces of pandemic situation. Temperature and humidity information recorded by sensors was also used for urban sensing. We find that the more SVF has a certain inhibitory effect on the virus transmission, but contrary to our intuitive perception, higher VGI has a certain promotion effect. Also, the structural equation model constructed in this paper can explain the variance of 28.9% of the number of confirmed cases, and results (path coef.) demonstrate that residential density of community (0.517) is a major influencing factor for pandemic cases, whereas convenience of community living (0.234) strongly influence it. Communities with good suitability of community human settlement (e.g., construction time, price) are safer in the face of pandemic events. Does the influence of SVF and VGI on the results of the pandemic situation mean that sunlight can effectively block the spread of the virus? This spatial heterogeneity in different communities is helpful for us to explore the environmental transmission route of COVID-19.

Feelings of safety during daytime walking: associations with mental health, physical activity and cardiometabolic health in high vacancy, low-income neighborhoods in Detroit, Michigan

INTRODUCTION

Individuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one's neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health.

METHODS

In this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety.

RESULTS

In this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (β = - 2.34, p = 0.017), depression scores (β = - 4.22, p = 0.006), and BMI (β = - 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots.

CONCLUSION

Future research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity-via stress reduction and concomitant effects on cardiometabolic health.

Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China

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.

Does Green Space Really Matter for Residents' Obesity? A New Perspective From Baidu Street View

Despite a growing literature on the topic, the association between neighborhood greenness and body weight is inconsistent. The objective of this research is to examine the association between neighborhood greenness and residents' obesity levels in a high population density area. We accounted for three greenness features: green access, green exposure, and view-based green index. We used the novel technique of deep convolutional neural network architecture to extract eye-level information from Baidu Street View images to capture the urban vertical greenness level. The research involved a survey with 9,524 respondents from 40 communities in Shanghai. Generally, we found all aspects of horizontal greenery, vertical greenery, and proximity of green levels to be impactful on body weight; however, only the view-based green index consistently had an adverse effect on weight and obesity.