Residential density and 20-minute neighbourhoods: A multi-neighbourhood destination location optimisation approach.

The concept of 20-min neighbourhoods, a planning intervention to promote local living and active travel, has recently become a key urban planning priority in Melbourne, Australia. The Victorian Government defines this concept as being able to reach daily local living destinations within a 20-min round-trip walk from home, which is approximately 800m of walking for each trip leg. Similar concepts, such as 10- or 15-min cities, have been explored in other cities worldwide. However, research to date has largely focused on measuring accessibility to destinations in developed areas, with few studies examining what is needed to build a city of 20-min neighbourhoods, i.e., investigating multiple neighbourhoods, shared destinations, and the role of residential density. In this study, we used optimisation models to examine the relationship between residential density and the targets of 20-min neighbourhoods in a hypothetical greenfield development scenario. We defined different targets for various destinations in terms of the percentage of the population aimed to have access to that destination within 800 m, ranging from 95% for the smallest destinations to 70% for the largest. Our results demonstrate that at least 25 dwellings per hectare (assuming 2.6 persons per dwelling) are needed to provide access to the destinations within 1.2 km and 35 dwellings per hectare to provide access within 1 km. Furthermore, we show that the cost of building destinations and the land required for them when delivering 20-min neighbourhoods at 30 dwellings per hectare is almost half of what is required when building at 15 dwellings per hectare.

Supporting pandemic disease preparedness: Development of a composite index of area vulnerability.

Although pandemics are rare, planning and preparation for responding to them plays a crucial role in preventing their spread. The management and control of pandemics such as COVID-19 relies heavily on a country's health capacity. Measuring vulnerability to pandemics in geographical areas could potentially delay a pandemic's exponential growth and reduce the number of cases, which would alleviate the disease impact on communities and the health care sector. The aim of this study is to generate an area-level COVID-19 Pandemic Vulnerability Index (CPVI) and to assess its correlation with COVID-19 cases. Data were collected for Local Government Areas (LGAs) across Australia from different sources including Australia Bureau of Statistics, Australian Institute of Health and Welfare, and General Transit Feed Specification. Based on recent official reports about the COVID-19 outbreak, 18 factors were identified as influencing vulnerability to the disease within LGAs. Using factor analysis, four latent factors were identified and named as sociodemographic, medical conditions, transportation, and land use. Predicted factor scores were summed to generate a CPVI for each LGA. The CPVI was evaluated by correlating with confirmed cases of COVID-19 standardised by adult population in New South Wales and Victoria, the two Australian states with the highest numbers of confirmed cases. There was a statistically significant correlation between the CPVI and COVID-19 in New South Wales (r = 0.49) and Victoria (r = 0.48). LGAs scoring higher on the CPVI also had a higher absolute number of cases. The CPVI could be used by policymakers to identify at-risk areas and to develop preparedness and response plans to help mitigate the spread of COVID-19 and future pandemics.

Could smart research ensure healthy people in disrupted cities?

BACKGROUND: Since the late 19th century, city planners have struggled to cope with new types of urban transport and mobility that threatened the existing system, or even rendered it obsolete. PURPOSE: As city planners confront the range of disruptive urban mobilities currently on the horizon, this paper explores how we can draw on a vast body of evidence to anticipate and avoid unintended consequences to people's health and wellbeing. METHODS: This commentary involved a rapid review of the literature on transport disruption. RESULTS: We found that to avoid the unintended consequences of disruption, research, policy and practice must think beyond single issues (such as the risk of chronic disease, injury, or traffic management) and consider the broader consequences of interventions. For example, although autonomous vehicles will probably reduce road trauma, what will be the negative consequences for physical inactivity, sedentary behavior, chronic disease, land use, traffic congestion and commuting patterns? Research is needed that considers and informs how to mitigate the range of potential harms caused by disruptive mobilities. CONCLUSION: In the face of new disruptive mobilities, we must: (a) draw on existing evidence to shape new regulations that address the 'who, when and where' rules of introducing new mobilities (such as electric assisted bicycles (e-bikes) and scooters (e-scooters)) of which the health repercussions can be easily anticipated; (b) monitor and evaluate the implementation of any interventions through natural experiment studies; and (c) use innovative research methods (such as agent-based simulation and health-impact-assessment modelling) to assess the likely effects of emerging disruptive mobilities (e.g., autonomous vehicles) on health and wellbeing and on the environment.

Friendship Network and Dental Brushing Behavior among Middle School Students: An Agent Based Modeling Approach.

By using a standard questionnaire, the level of dental brushing frequency was assessed among 201 adolescent female middle school students in Tehran. The initial assessment was repeated after 5 months, in order to observe the dynamics in dental health behavior level. Logistic Regression model was used to evaluate the correlation among individuals' dental health behavior in their social network. A significant correlation on dental brushing habits was detected among groups of friends. This correlation was further spread over the network within the 5 months period. Moreover, it was identified that the average brushing level was improved within the 5 months period. Given that there was a significant correlation between social network's nodes' in-degree value, and brushing level, it was suggested that the observed improvement was partially due to more popularity of individuals with better tooth brushing habit. Agent Based Modeling (ABM) was used to demonstrate the dynamics of dental brushing frequency within a sample of friendship network. Two models with static and dynamic assumptions for the network structure were proposed. The model with dynamic network structure successfully described the dynamics of dental health behavior. Based on this model, on average, every 43 weeks a student changes her brushing habit due to learning from her friends. Finally, three training scenarios were tested by these models in order to evaluate their effectiveness. When training more popular students, considerable improvement in total students' brushing frequency was demonstrated by simulation results.