Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions. © 2020 Tsinghua University Press.

Analysis of Pandemic Atmosphere Pollution Data Using Virtual Sensors in Sao Paulo City

Virtual sensing models have been used to generate synthetic data and provide complementary information. However, with the increase in cases related to COVID-19 and the lockdown, a problematic factor is that virtual sensing models may produce different results than they should since the environment has become better with the decrease in traffic conditions and industrial production. Therefore, this article will evaluate virtual sensing models for the city of Sao Paulo, using pre-pandemic and pandemic data in a lockdown scenario. As a result, we analyzed that even with these behavioral changes in the city, the pre-pandemic model produced similar results to the lockdown period model.

The Theoretical, Practical, and Technological Foundations of the 15-Minute City Model: Proximity and Its Environmental, Social and Economic Benefits for Sustainability

Conventional and emerging paradigms of urbanism require new responses under the current circumstances, especially in relation to the integration of sustainability dimensions and technology advances. The escalating rate of urbanization, coupled with the climate emergency, fundamentally indeed disrupt the challenges that urbanism research and practice deal with, calling for adopting more innovative approaches to urban planning and design. With cities contributing around 65% of Greenhouse Gas (GHG) emissions and experiencing an unprecedented growth of population, contemporary urban policy needs to be redefined and re-assessed accordingly. While numerous urban models, such as the Compact City, the Eco-City, the Sustainable City, and the Smart City, have emerged in response to the challenges of sustainability and urbanization, the 15-Minute City has recently gained a steep popularity. This paper explores the theoretical, practical, and technological foundations of the 15-Minute City, with a particular focus on the proximity dimension of mixed land-use and its environmental, social, and economic benefits of sustainability as supported by smart technologies. We argue that this evolving model of urbanism has the potential to gain more expansion and success in regard to building more sustainable, efficient, resilient, equitable, and inclusive cities in line with the global agendas of Sustainable Development Goal (SDG) 11, as it adds a strategic value to the amalgam of the prevailing and emerging paradigms of urbanism and their synergies with respect to increasing the benefits of sustainability while emphasizing its environmental dimension.

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

The COVID-19 pandemic is a major global public health problem that has caused hardship to people’s normal production and life. Predicting the traffic revitalization index can provide references for city managers to formulate policies related to traffic and epidemic prevention. Previous methods have struggled to capture the complex and diverse dynamic spatio-temporal correlations during the COVID-19 pandemic. Therefore, we propose a deep spatio-temporal meta-learning model for the prediction of traffic revitalization index (DeepMeta-TRI) using external auxiliary information such as COVID-19 data. We conduct extensive experiments on a real-world dataset, and the results validate the predictive performance of DeepMeta-TRI and its effectiveness in addressing underfitting.

Particle Swarm Optimization for Adaptive Social-distance of Neighborhood in the IoT and COVID-19 Era

While it is well understood that the emerging Social Internet of Things (SIoT) offers a description of a new world of billions of humans which are intelligently communicate and interact with each other. SIoT presents new challenges for suggesting useful objects with certain services for people. This is due to the limitation of social networks between human and objects, such as the evaluation of the various patterns inherent in human walk in cities. In this study we focus services on the problem of recommendation on SIoT which is very important for many applications such as urban computing, smart cities, and health care. The optimized results of swarm of certain infected people COViD-19 introduced in this paper aims at finding a given region of interest. Guided by a fitness function, the particle swarm optimization (PSO) algorithm has proved its efficiency to explore the search space and find the optimal solution. However, in real world scenarios in which the peoples are simulated as particles, there are practical constraints that should be taken into considerations. The most two significant constraints are (1) given the social-distance, the measurement of input variable fluctuations and their possibility of occurring via probability distribution function over the whole particles. (2) given the limited the communication range of particle/people/users, therefore, the spread of the diseases are simulated and evaluated using neighborhood particle swarm optimization (NPSO). © 2021 IEEE.