Design of public open space: Site features, playing, and physical activity.

Not enough studies have examined how specific design features of public open space, such as movable site features, are associated with people's physical activity level or playfulness. To fill this gap, this study uses deep learning-based methods to extract visitors' movement trajectories (n = 18,592) from a time-lapse video of a promenade in Hong Kong. The trajectories are classified into different groups based on a set of movement indicators. Multinomial logistic regression is used to examine the relationship between trajectory types and the level of interaction with different site features. A one-way analysis of variance (ANOVA) is also used to compare the average amount of physical activity among different trajectory types. The results show that interaction with semi-fixed or movable site features is associated with higher odds of people having "playful" trajectories than other types of trajectories. People with "sporty" trajectories and "playful" trajectories on average have the highest amount of physical activity.

The co-benefits of electric mobility in reducing traffic noise and chemical air pollution: Insights from a transit-oriented city.

Traffic noise is a growing threat to the urban population. Prolonged exposure to traffic noise has been linked to negative health consequences such as annoyance, sleep disturbances and cardiovascular diseases. While electric vehicles are known to have lower noise profiles, the impacts of electric mobility on traffic noise, especially for electrified heavy-duty vehicles, have not been thoroughly examined. This study aims to examine the impacts of both electric light-duty vehicles and electric buses on traffic noise levels in a highly urbanized city. Traffic noise along the source line and pedestrian network was first estimated and mapped to illustrate its spatiotemporal variations. Then, scenario analysis was used to compare the impacts. Population potentially benefiting from reduced traffic noise in the neighbourhoods and the associated health impacts were also estimated. Results indicate that electric buses have a greater potential to reduce traffic noise, with a maximum reduction of 4.4 dBA during daytime in the urban cores. With all bus fleet electrified, around 60% of the population can benefit from a reduction of 1 dBA at the street environment, 15.3% for 1-2 dBA, and 4.3% for more than 2 dBA. The estimated reduction of preventable deaths and preventable cases of diseases per 100,000 population are 4.15 and 112.99 respectively. The findings shed important insights into prioritizing bus routes to be electrified in urban areas for maximizing health co-benefits.

Urban visual intelligence: Uncovering hidden city profiles with street view images.

A longstanding line of research in urban studies explores how cities can be understood through their appearance. However, what remains unclear is to what extent urban dwellers' everyday life can be explained by the visual clues of the urban environment. In this paper, we address this question by applying a computer vision model to 27 million street view images across 80 counties in the United States. Then, we use the spatial distribution of notable urban features identified through the street view images, such as street furniture, sidewalks, building façades, and vegetation, to predict the socioeconomic profiles of their immediate neighborhood. Our results show that these urban features alone can account for up to 83% of the variance in people's travel behavior, 62% in poverty status, 64% in crime, and 68% in health behaviors. The results outperform models based on points of interest (POI), population, and other demographic data alone. Moreover, incorporating urban features captured from street view images can improve the explanatory power of these other methods by 5% to 25%. We propose "urban visual intelligence" as a process to uncover hidden city profiles, infer, and synthesize urban information with computer vision and street view images. This study serves as a foundation for future urban research interested in this process and understanding the role of visual aspects of the city.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

Introduction: This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Methods: Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results: Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Discussion: Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Using computer vision and machine learning to identify bus safety risk factors.

In road safety research, bus crashes are particularly noteworthy because of the large number of bus passengers involved and the challenge that it puts to the road network (with the closure of multiple lanes or entire roads for hours) and the public health care system (with multiple injuries that need to be dispatched to public hospitals within a short time). The significance of improving bus safety is high in cities heavily relying on buses as a major means of public transport. The recent paradigm shifts of road design from primarily vehicle-oriented to people-oriented urge us to examine street and pedestrian behavioural factors more closely. Notably, the street environment is highly dynamic, corresponding to different times of the day. To fill this research gap, this study leverages a rich dataset - video data from bus dashcam footage - to identify some high-risk factors for estimating the frequency of bus crashes. This research applies deep learning models and computer vision techniques and constructs a series of behavioural and street factors: pedestrian exposure factors, pedestrian jaywalking, bus stop crowding, sidewalk railing, and sharp turning locations. Important risk factors are identified, and future planning interventions are suggested. In particular, road safety administrations need to devote more efforts to improve bus safety along streets with a high volume of pedestrians, recognise the importance of protection railing in protecting pedestrians during serious bus crashes, and take measures to ease bus stop crowding to prevent slight bus injuries.

Impact of heat on all-cause and cause-specific mortality: A multi-city study in Texas.

BACKGROUND: Studies on the health effects of heat are particularly limited in Texas, a U.S. state in the top 10 highest number of annual heat-related deaths per capita from 2018 to 2020. This study assessed the effects of heat on all-cause and cause-specific mortality in 12 metropolitan statistical areas (MSAs) across Texas from 1990 to 2011. METHODS: First, we determined the heat thresholds for each MSA above which the relation between temperature and mortality is linear. We then conducted a distributed lag non-linear model for each MSA, followed by a random effects meta-analysis to estimate the pooled effects for all MSAs. We repeated this process for each mortality cause and age group to achieve the effect estimates. RESULTS: We found a 1 °C temperature increase above the heat threshold is associated with an increase in the relative risk of all-cause mortality of 0.60% (95%CI [0.39%, 0.82%]) and 1.10% (95%CI [0.65%, 1.56%]) for adults older than 75. For each MSA, the relative risk of mortality for a 1 °C temperature increase above the heat threshold ranges from 0.10% (95%CI [0.09%, 0.10%]) to 1.29% (95%CI [1.26%, 1.32%]). Moreover, elevated temperatures showed a slight decrease in cardiovascular mortality (0.37%, 95%CI [-0.35%, 1.09%]) and respiratory disease (1.97%, 95%CI [-0.11%, 4.08%]), however this effect was not considered statistically significant.. CONCLUSION: Our study found that high temperatures can significantly impact all-cause mortality in Texas, and effect estimates differ by MSA, age group, and cause of death. Our findings generate critical information on the impact of heat on mortality in Texas, providing insights for policymakers on resource allocation and strategic intervention to reduce heat-related health effects.

Travel behaviour changes under Work-from-home (WFH) arrangements during COVID-19.

Life, including working style and travel behaviour, has been severely disrupted by the COVID-19 pandemic. The unprecedented number of work-from-home (WFH) employees after the outbreak of COVID-19 has attracted much scholarly attention. As it is generally believed that WFH arrangements are not ephemeral, it is imperative to study the impacts of WFH on travel behaviour and its impact on sustainable transport in the post-pandemic era. In relation, this study uses a set of longitudinal GPS tracking data in Switzerland to examine changes in trip characteristics (i.e. travel distance, travel time), travel behaviours (i.e. travel frequency, peak hour departure, trip destination, travel mode), and activities (i.e. trip pattern diversity, trip purpose, and time spent at home). Two groups of participants (WFH and Non-WFH) are identified and compared through three periods (pre-COVID, during lockdown, and post lockdown) from September 2019 to October 2020. Results show that more significant reductions of trip distance, travel time, travel frequency, morning peak hours trips, trips to the CBD are observed among the WFH group. These changes helped to mitigate negative transport externalities. Meanwhile, active transport trips, trip pattern diversity, leisure trips, and time spent at home also increased more significantly for the WFH group when compared to their counterparts. Hence, promoting WFH may not only be beneficial to teleworkers but also to the wider community through more sustainable transport. Future research direction and policy implications are also discussed.

An assessment of the effect of green signal countdown timers on drivers' behavior and on road safety at intersections, based on driving simulator experiments and naturalistic observation studies.

INTRODUCTION: Motor-vehicle crashes at signalized intersections are a significant traffic safety problem. To address this problem, many Asian cities have installed signal countdown displays at signalized intersections, aiming to assist drivers to make correct decisions in response to traffic signals. METHOD: In this study, we assessed the short-term and long-term effects of green signal countdown timers (GSCTs) on road safety, using a combination of driving simulator experiments and naturalistic observations. RESULTS: In our driving simulator experiments, 80 participants drove at 50 km/h in scenarios in which a car either approached a signalized intersection alone or following another car. In naturalistic observations, short-term (1-week) and long-term (1-year) intersection safety in the presence and absence of GSCTs were compared. These observations revealed that GSCTs reduced the number of red-light-running violations over the short term, but not over the long term. In fact, GSCTs appeared to lead to an overall increase in rear-end crash risk at intersections, as their presence resulted in drivers exhibiting more sudden acceleration and braking, and altered intersection-crossing speeds and patterns. CONCLUSIONS: The results suggest that GSCTs worsen safety at signalized intersections, and thus their removal should be considered.

A metropolitan-scale, three-dimensional agent-based model to assess the effectiveness of the COVID-19 Omicron wave interventions in a hyperdense city: a case study of Hong Kong.

We simulated the COVID-19 Omicron spread in Hong Kong, China, by building a novel three-dimensional agent-based model that incorporates its vertically expanded, hyperdense urban environment. The model examined the effectiveness of the 'zero-COVID' interventions (i.e., Compulsory Universal Testing (CUT) and citywide lockdown) that were for debate during the Omicron wave in Hong Kong. We found that such stringent interventions would be effective only with even faster and stricter implementation. Therefore, flexible long-term strategies should also be considered to contain and prevent future infectious diseases.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

Spatio-temporal variations of traffic congestion under work from home (WFH) arrangements: Lessons learned from COVID-19.

Traffic congestion has been a persistent problem in cities globally. Theoretically, commuting-related congestion can be relieved by promoting working from home (WFH). Amid the COVID-19 pandemic, WFH arrangement has been encouraged or enforced to reduce the spread of the coronavirus. Under these circumstances, it was reported that traffic congestion has been alleviated in many cities. However, changes in congestion patterns within a city have not been studied in-depth. In this study, we analysed the congestion index (CI) at peak hours, when commuting-related congestion is typically most serious, throughout different waves of the pandemic in Hong Kong. Results show that under WFH arrangement, peak-hour congestion has been alleviated. Within a day, morning peak congestion was more relieved. Spatially, significant drops in CI were found not only in the central business district and urban cores but also in some new town areas. This paper has significant implications for urban planners in creating more sustainable cities that duly consider the commuting needs of residents, and cautions against the optimism that WFH can relieve urban transport problems despite jobs-housing imbalance. While the WFH arrangement has potentials to ease commuting congestion, future e-working and transport measures need to take spatial and temporal dimensions into account.

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science.

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

Do neighbourhood characteristics matter in understanding school children's active lifestyles? A cross-region multi-city comparison of Glasgow, Edinburgh and Hong Kong.

Many studies have explored the influence of individual and neighbourhood factors on active school travel (AST), this novel study is the first to examine how AST and formal extracurricular activities are associated with children's active lifestyles. The aims of this study were to (a) create an active lifestyle variable (ALIFE) measured in terms of total weekly minutes of AST and extracurricular activities, and (b) explore how ALIFE is associated with different attributes at the individual, household and neighbourhood levels, and how these relationships differ for children aged 10 and 11 years old across the three cities: Glasgow, Edinburgh and Hong Kong. We found environmental factors to be important indicators of lower AST, for example greater parking facility density. The most substantial contribution to children's overall ALIFE was household income, those from the lowest household group having almost 2 h less ALIFE per-week than those from the highest income.

Identification of superspreading environment under COVID-19 through human mobility data.

COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space-time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a "risk map of superspreading environment" (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.

Global association between satellite-derived nitrogen dioxide (NO2) and lockdown policies under the COVID-19 pandemic.

The COVID-19 pandemic has severely affected various aspects of life, at different levels and in different countries on almost every continent. In response, many countries have closed their borders and imposed lockdown policies, possibly bringing benefits to people's health with significantly less emission from air pollutants. Currently, most studies or reports are based on local observations at the city or country level. There remains a lack of systematic understanding of the impacts of different lockdown policies on the air quality from a global perspective. This study investigates the impacts of COVID-19 pandemic towards global air quality through examining global nitrogen dioxide (NO2) dynamics from satellite observations between 1 January and 30 April 2020. We used the Apriori algorithm, an unsupervised machine learning method, to investigate the association among confirmed cases of COVID-19, NO2 column density, and the lockdown policies in 187 countries. The findings based on weekly data revealed that countries with new cases adopted various lockdown policies to stop or prevent the virus from spreading whereas those without tended to adopt a wait-and-see attitude without enforcing lockdown policies. Interestingly, decreasing NO2 concentration due to lockdown was associated with international travel controls but not with public transport closure. Increasing NO2 concentration was associated with the "business as usual" strategy as evident from North America and Europe during the early days of COVID-19 outbreak (late January to early February 2020), as well as in recent days (in late April) after many countries have started to resume economic activities. This study enriches our understanding of the heterogeneous patterns of global associations among the COVID-19 spreading, lockdown policies and their environmental impacts on NO2 dynamics.

Neighbourhood environment and depressive symptoms among the elderly in Hong Kong and Singapore.

BACKGROUND: Geriatric depression is a growing public health issue worldwide. This study aims at identifying the relevant neighbourhood attributes, separate from the individual-level characteristics, that are related to the onset of depressive disorders among the geriatric population. METHODS: This study adopts a structural equation modelling (SEM) approach to understand the effect of the neighbourhood environment on geriatric depression, as identified by data collected from community-dwelling elderly living in Hong Kong and Singapore. Using network buffers as the unit of analysis, different features of the neighbourhood environment are captured and analysed. SEM also examines the strength and direction of the relationships using different parameters at both the individual and neighbourhood levels, as well as the prevalence of depressive symptoms among the elderly. RESULTS: The total sample size is 347, with 173 and 174 elderly people in Hong Kong and Singapore respectively. The results show that in addition to one's physical health status, both objective and subjective neighbourhood factors including the size of parks, land use mix, walkability, and connectivity are all statistically significant influential factors in geriatric depression. In particular, enhancing walkability and providing more parks at the neighbourhood level can bring mental health benefits. CONCLUSIONS: Public health policy initiatives aimed at tackling geriatric depression can be achieved by adopting a holistic and integrative approach to better prepare the neighbourhood environment in an ageing society.

Mobility impairment, social engagement, and life satisfaction among the older population in China: a structural equation modeling analysis.

PURPOSE: Revealing the relationship between mobility impairment and life satisfaction can help to propose effective interventions to secure mobility and life satisfaction. However, the relationship remains unclear and lacks quantitative evidence in China. This study therefore assesses the association of mobility impairment, social engagement, and life satisfaction among the older population in China. METHODS: Based on the sample of China Health and Retirement Longitudinal Survey database in 2013, a structural equation modeling is established. The sample size is 4245 with 55.9% with mobility impairment. RESULTS: The model shows that the length of suffering from disability is significantly related to mobility impairment (ß = 0.058, p < 0.001). Mobility impairment is inversely related to social engagement (ß = -0.300, p < 0.001) and life satisfaction (ß = -0.311, p < 0.001). Social engagement is positively related to life satisfaction (ß = 0.211, p < 0.001). Moreover, the relationships have some differences for the seniors with different sociodemographic characteristics and living in different residential areas. CONCLUSIONS: As seniors get older, they tend to have more severe mobility impairment and participate less in social activities. Those with higher mobility impairment are more likely to report lower life satisfaction partly because they usually participate less in social activities. Different strategies are suggested to be adopted to improve the life satisfaction of the older population from the aspects of promoting mobility and social engagement, including improving the design of transport facilitates, providing assistive facilities for the seniors with severe mobility impairment, promoting the accessibility of community leisure and healthcare services, and constructing more community senior activity centers.

Land use and traffic collisions: A link-attribute analysis using Empirical Bayes method.

Road traffic collisions represent one of the major public health problems among the leading causes of deaths globally. This paper examines several approaches in detecting hazardous road locations, and discusses the spatial distribution of these locations as well as their relationships with different land uses in Hong Kong. Two most commonly used methodologies in detecting hazardous road locations are used: the hot spot and hot zone methodologies. Both methodologies are performed using raw collision count, excess collision count and Empirical Bayes (EB) estimations. The EB estimation uses land use characteristics near the road network in defining the reference groups. Finally all the approaches are compared by a test to assess their stability. The results show that for different hazardous road location detection methodologies, the best fit estimation methods on sites are different. The results confirm some land use impacts in previous studies, and suggest some further patterns on road safety. The findings are useful in understanding the complex interrelationships between land use and road safety, and in facilitating planners and policy makers to build safer cities.

Safety in numbers for cyclists beyond national-level and city-level data: a study on the non-linearity of risk within the city of Hong Kong.

OBJECTIVE: This paper examines the relationship between bicycle collisions and the amount of cycling at the local level. Most previous research has focused on national and city comparisons, little is known about differences within a city (the mesoscale). METHODS: This study mainly used three types of data sets relating to bicycle collisions, use of bicycles and local neighbourhood characteristics in Hong Kong. In particular, bicycle usage, measured as bicycle-kilometres travelled, was estimated from travel surveys following the activity-based approach. Negative binomial regression models were established to model the relationship between the amount of cycling and the occurrence of bicycle collisions at the spatial scale of the Tertiary Planning Unit, which is the smallest planning unit of the city. RESULTS: The numbers of bicycle collisions went up with the increasing use of bicycles, but the increase in the number of collisions in a given community was less than a linear proportion of the bicycle flow. When other local neighbourhood variables are controlled, the amount of cycling is a statistically significant variable in accounting for the number of collisions. CONCLUSIONS: Even in a highly motorised city where bicycles are a minor transport mode, cyclists are less likely to be involved in road collisions in communities with higher cycling volume. Since cycling activities are likely to vary within a city, a more local-based approach in promoting cycling is needed. In particular, the higher safety risks in neighbourhoods of low bicycle usage, especially at an initial stage of promoting cycling, need to be addressed properly.