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Understanding urban vibrancy has been considered crucial to promoting human activities and interactions in public open spaces. Recent advancements in urban big data have facilitated the potential to understand and measure vibrancy patterns throughout cities. While streets are considered the center stage of human activity, previous studies have often overlooked their multifaceted nature and their association with urban vibrancy. In this study, we incorporate multi-source big data and combine a set of features that comprehensively describe the scale, function, and topology of street segments in two Seoul districts: Jung-gu and Gangnam-gu. Using these features, we employ a machine learning clustering technique to classify them into five distinct typologies. Then, with street-level aggregated mobile phone tracking data, we investigate whether street typology characteristics are associated with urban vibrancy with respect to age groups, time of day, and day types (weekends/weekdays). The results show varying relationships between street characteristics with age-, time- and day-vibrancy measures by the identified street typology. Further, we contrast the results of the two districts to evaluate urban vibrancy differences in organic and planned urban layouts. This study enables a more nuanced understanding of urban streets to better comprehend their impact on people's use of street space. The derived novel insights could assist planners and designers to better pinpoint street management solutions for different age- and time-dependent needs based on the complexities in urban vibrancy dynamics. [ABSTRACT FROM AUTHOR]

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]

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking. Measurement(s) Particulate matter, gaseous pollutants Technology Type(s) Low-cost environmental sensing platform (City Scanner) Sample Characteristic - Environment City Sample Characteristic - Location Global [ABSTRACT FROM AUTHOR]

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]

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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.

Evaluating the balance between a city’s physical and socioeconomic environmental development is crucial for creating sustainable and livable urban spaces. Although they might appear contradictory, they jointly support the comprehensive sustainable urban development strategy. Traditional methods usually focus on assessing this balance from a specific perspective, such as how neighborhood greenery shapes real estate value. Yet, they fail to deliver a holistic balance assessment in developing the physical and socioeconomic dimensions. To fill this gap, this study introduces a research framework that measures this balance through house prices based on transferred bias. Using house price as an indicator shaped by both physical and socioeconomic environments, the framework first constructs a series of deep learning models to estimate house prices through street view images for each city. These models capture the relationship between neighborhood appearance and house price. Second, by leveraging transfer inference, we introduce neighborhood appearance from one city into the model trained from another city. This process identifies the transferred bias, which is the disparity between inaccurate inference resulting from a mismatched neighborhood appearance and the trained model. Through transferred bias, we can quantify the differences in physical and socioeconomic environments across cities and evaluate the urban balances of these two environments. The results show that the transferred bias effectively quantifies the disparities among cities in physical and socioeconomic environments, thereby facilitating further investigation into the urban balance between these two environments. [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]