ABSTRACT
Using microdata from Statistics Canada's Labour Force Survey (LFS) and Population Census, this paper explores how spatial characteristics are correlated with temporary employment outcomes for Canada's immigrant population. Results from ordinary least square regression models suggest that census metropolitan areas and census agglomerations (CMAs/CAs) characterized by a high share of racialized immigrants, immigrants in low-income, young, aged immigrants, unemployed immigrants, and immigrants employed in health and service occupations were positively associated with an increase in temporary employment for immigrants. Furthermore, findings from principal component regression models revealed that a combination of spatial characteristics, namely CMAs/CAs characterized by both a high share of unemployed immigrants and immigrants in poverty, had a greater likelihood of immigrants being employed temporarily. The significance of this study lies in the spatial conceptualization of temporary employment for immigrants that could better inform spatially targeted employment policies, especially in the wake of the structural shift in the nature of work brought about by the COVID-19 pandemic.
ABSTRACT
The general consensus is that physical activity can prevent and manage lifestyle-induced chronic diseases, and moderate-to-vigorous physical activity (MVPA) has been included in several guidelines of WHO as an indicative intensity standard. Numerous studies have confirmed that improving the spatial quality of urban parks can be very helpful in supporting physical activities, and that the quality of parks is significantly related to the intensity of physical activities. However, few studies have explored the spatial characteristics of activating physical activities. Using a modified System for Observing Play and Recreation in Communities (SOPARC), this study examines the relationship between spatial characteristics and MVPA through a binary logistic regression model. The results reveal that: firstly, inconsistent with other similar studies, the most observed group in the park is the adults rather than the seniors, and the proportion of the females (51%) is higher;secondly, the distribution of MVPA in different groups shows that the seniors have less interaction with other groups, and they have a significant spatial attachment. Thirdly, in functionality, large lawn and jogging trails have been proved to be the most effective features to promote the occurrence of MVPA;among the activity, except for the significant correlation between equipped and MVPA, other attributes can be proved to encourage MVPA as well as those in comfort. In conclusion, our results can contribute to the planning and design of the urban park as well as the further management and allocation of the space and facilities under the vision of promoting public health. © 2023 by the authors.
ABSTRACT
The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social platforms, such as Twitter, Facebook and WeChat. In this work, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. The co-evolution between disease and information is explored via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. High correlation between disease and information data is observed, and also people care about the spread of disease only when it comes to their neighborhood. Regard to the content of the information, we obtain that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, two machine learning algorithms, i.e., linear regression and random forest, are introduced to further predict the number of infected using characteristics, such as disease spatial related and information-related features. We obtain that both the disease spatial related characteristics of nearby cities and information-related characteristics can help to improve the prediction accuracy. The methodology proposed in this paper may shed light on new clues of emerging infections prediction. © 2013 IEEE.
ABSTRACT
The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social platforms, such as Twitter, Facebook and WeChat. In this work, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. The co-evolution between disease and information is explored via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. High correlation between disease and information data is observed, and also people care about the spread of disease only when it comes to their neighborhood. Regard to the content of the information, we obtain that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, two machine learning algorithms, i.e., linear regression and random forest, are introduced to further predict the number of infected using characteristics, such as disease spatial related and information-related features. We obtain that both the disease spatial related characteristics of nearby cities and information-related characteristics can help to improve the prediction accuracy. The methodology proposed in this paper may shed light on new clues of emerging infections prediction. Author
ABSTRACT
The need of supporting meditation through digital technology has been increased especially after COVID-19. By combining the olfactory experience provided by the ambient incense connected with virtual reality technology, we propose to bring the aesthetic and affective aspects of smell to the users for meditation in the digital era. TranScent aims to provide users a hybrid composition of sensory experiences that transcends the spatial and temporal characteristics in their surroundings. It lets the users meditate with the incense burnt in the real world while immersing in the audiovisual virtual environment. Rather than emphasizing on the mobility in fast pace, it focuses on giving users the stillness atmosphere for meditation practice through olfactory art with virtual reality. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.