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As cities resume life in public space, they face the difficult task of retaining outdoor activity while decreasing exposure to airborne viruses, such as the novel coronavirus. Even though the transmission risk is higher in indoor spaces, recent evidence suggests that physical contact outdoors also contributes to an increased virus exposure. Given that streets constitute the largest percentage of public space in cities, there is an increasing need to prioritise their use to minimise transmission risk. However, city officials currently lack the assessment tools to achieve this. This article evaluates the extent to which street segments are associated with spatiotemporal variations of potential exposures of pedestrians to virus transmission. We develop a multi-component risk score that considers both urban form and human activity along streets over time, including (a) an assessment of pedestrian infrastructure according to the average width of pavements, (b) a measure of accessibility for each street based on its position in the street network, (c) an activity exposure score that identifies places along streets where exposure could be higher and (d) an estimate of the number of pedestrians that will pass through each street during weekdays and weekends. We use Amsterdam in the Netherlands as a case study to illustrate how our score could be used to assess the exposure of pedestrians to virus transmission along streets. Our approach can be replicated in other cities facing a similar challenge of bringing life back to the streets while minimising transmission risks. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, physical distancing, mobility restrictions and self-isolation measures were implemented around the world as the primary intervention to prevent the virus from spreading. Urban life has undergone sweeping changes, with people using spaces in new ways. Stockholm is a particularly relevant case of this phenomenon since most facilities, such as day care centres and schools, have remained open, in contrast to cities with a broader lockdown. In this study, we use Twitter data and an online map survey to study how COVID-19 restrictions have impacted the use of different locations, services and amenities in Stockholm. First, we compare the spatial distribution of 87,000 geolocated tweets pre-COVID-19 and during the COVID-19 pandemic. Second, we analyse 895 survey responses asking people to identify places they 'still visit', 'use more', 'avoid' and self-report reasons for using locations. The survey provides a nuanced understanding of whether and how restrictions have affected people. Service and seclusion were found to be important; therefore, the accessibility of such amenities was analysed, demonstrating how changes in urban habits are related to conditions of the local environment. We find how different parts of the city show different capacities to accommodate new habits and mitigate the effects of restrictions on people's use of urban spaces. In addition to the immediate relevance to COVID-19, this paper thus contributes to understanding how restrictions on movement and gathering, in any situation, expose more profound urban challenges related to segregation and social inequality. [ABSTRACT FROM AUTHOR]

The article examines the urban planning on information and communication technologies (ICT) in the cities of São Paulo, Brazil. It was found that the ICT initiatives in the São Paulo cities were fragmented and were not being connected to economic development, city stature or administrative urbanization. The authors commented that the urban leaders do know where to look for inspiration, what they are going to implement and what the outcomes of such development will be. The five structural limitations in the implementation of ICTs for urban development in São Paulo cities are discussed.

Aim: To test the association between sociodemographic and social characteristics with COVID-19 cases and deaths in small and large Brazilian cities. Methods: This ecological study included COVID-19 data available in State Health Secretaries (managed by brasil.io API) and three national databases (IBGE, DATASUS and Embrapa). Temporal spread of COVID-19 in Brazil during the first year considered as outcome: a) days until 1st case in each city since 1st in the country; b) days until 1,000 cases/100,000 inhabitants since 1st case in each city; c) days until 1st death until 50 deaths/100,000 inhabitants. Covariates included geographic region, city social and environmental characteristics, housing conditions, job characteristics, socioeconomic and inequalities characteristics, and health services and coverage. The analysis were stratified by city size into small (<100,000 inhabitants) and large cities (≥100,00 inhabitants). Multiple linear regressions were performed to test associations of all covariates to adjust to potential confounders. Results: In small cities, the first cases were reported after 82.2 days and 1,000 cases/100,000 were reported after 117.8 days, whereas in large cities these milestones were reported after 32.1 and 127.7 days, respectively. For first death, small and large cities took 121.6 and 36.0 days, respectively. However, small cities were associated with more vulnerability factors to first case arrival in 1,000 cases/100,000 inhabitants, first death and 50 deaths/100,000 inhabitants. North and Northeast regions positively associated with faster COVID-19 incidence, whereas South and Southeast were least. Conclusion: Social and built environment characteristics and inequalities were associated with COVID-19 cases spread and mortality incidence in Brazilian cities. [ABSTRACT FROM AUTHOR]

Air pollution is a major threat to public health. However, two issues have not been adequately addressed in most conventional Land Use Regression models for air pollution prediction: 1). A combination of urban forest involvement and urban form representation; 2). Scale sensitivity analysis of model variables. Here, we apply lacunarity to investigate the spatial sensitivity of predictors, incorporate 2-D and 3-D urban form to comprehensively characterize the urban environment, and examine the tree diversity impacts on air pollution distribution using unique NO 2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California. We find that lacunarity-optimized models could reduce the computation burden by extracting the upper limits of the spatial heterogeneity of predictors while keeping the model accuracy simultaneously. Furthermore, there are synthetic effects between the urban form and tree diversity on NO 2 distribution, and such effect directions could be non-monotonic. Finally, although the increase in tree diversity could facilitate the reduction of regional NO 2 concentration, it is essential to seek a balance between tree diversity and tree dominance to effectively improve air quality on the city scale. The findings are useful for environmental scientists striving for better air quality and urban planners caring for the well-being of cities. [Display omitted] • Examined the interaction between the urban forest, physical environment, and air quality with mobile measurements. • Characterized the urban environment through both 2D and 3D urban morphological measurements. • Quantified spatial heterogeneity of regression predictors using lacunarity analysis. • The synthetic effects of the urban form and tree diversity on NO 2 distribution could be non-monotonic. • Seek a balance between tree diversity and tree dominance is essential to effectively improve air quality on the city scale. [ABSTRACT FROM AUTHOR]