Urban studies related to previous pandemics and impacts on cities focused on vulnerable categories including poor and marginalized groups. We continue this tradition and analyze unemployment outcomes in a context of a multi-dimensional social disadvantage that is unfolding during the ongoing public health crisis. For this, we first propose an approach to identify communities by social disadvantage status captured by several key metrics. Second, we apply this methodology in the study of the effect of social disadvantage on unemployment during the COVID-19 and measure the COVID-19-related economic impact using the most recent data on unemployment. The study focuses upon vulnerable communities in in the southeastern US (Tennessee) with a concentration of high social vulnerability and rural communities. While all communities initially experienced the impact that was both sudden and severe, communities that had lower social disadvantage pre-COVID were much more likely to start resuming economic activities earlier than communities that were already vulnerable pre-COVID due to high social disadvantage with further implications upon community well-being. The impact of social disadvantage grew stronger post-COVID compared with the pre-pandemic period. In addition, we investigate worker characteristics associated with adverse labor market outcomes during the later stage of the current economic recession. We show that some socio-demographic groups have a systematically higher likelihood of being unemployed. Compared with the earlier stages, racial membership, poverty and loss of employment go hand in hand, while ethnic membership (Hispanics) and younger male workers are not associated with higher unemployment. Overall, the study contributes to a growing contemporaneous research on the consequences of the COVID-19 recession. Motivated by the lack of the research on the spatial aspect of the COVID-19-caused economic recession and its economic impacts upon the vulnerable communities during the later stages, we further contribute to the research gap.

Urban sprawl is a global phenomenon commonly known for its wide-ranging negative consequences for urban sustainability. In this chapter, we identify drivers of sprawl, provide examples, and distinguish sprawl from urban growth. We discuss how sprawl is countered by smarter alternatives to urban growth. Among the consequences, we highlight the connections of urban blight and sprawl, which are given little attention, particularly in the urban studies literature on sprawl. Finally, we discuss the vulnerability and resiliency of the urban form in the face of an infectious COVID-19 pandemic.

In this paper, DTFL an image classifier based on Decision Tree and Fuzzy Logics is proposed. At the beginning of classification using DTFL, each pixel is located at the highest level of a decision tree where it belongs to the combination of all classes. DTFL transfers a pixel to a lower level of the decision tree where the pixel belongs to a combination of fewer classes. Decision-making about transfers is based on fuzzy logic with seven different membership functions including triangular-shaped, trapezoidal-shaped, π-shaped, bell-shaped, Gaussian, differential S-shaped and multiplicative S-shaped. Eventually, pixels will reach the lowest level of the decision tree where it belongs to only one class. For accuracy assessment, DTFL was used to classify a GeoEye-1 image. The overall accuracy of 96.14% and a kappa coefficient of 96.06% were reached by DTFL. In comparison, the overall accuracy of 89.91% and a kappa coefficient of 89.77% were reached by a Maximum Likelihood Classifier, MLC. In the case of applying a threshold in MLC to reach the same accuracy as DTFL, 8.73% of pixels take the non-classified label while using DTFL all the pixels get a proper label. The results indicate that the proposed classifier extracts more information from images.