Effects of Spatial Characteristics on Non-Standard Employment for Canada's Immigrant Population

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.

The impact of COVID-19 pandemic on electricity consumption and electricity demand forecasting accuracy: Empirical evidence from the state of Qatar

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically investigates the relationship between building electricity consumption and the number of daily infected cases in the State of Qatar. The effect of the pandemic on electricity usage was quantified during various periods of the pandemic years. Around 1 million electricity meter readings per year were considered for six different types of building usage between the years 2010 and 2021. The findings indicate that there was a gap between the actual and simulated electricity consumption during the pandemic years. Furthermore, the results show that the fluctuation in electricity consumption was correlated with the number of daily infected cases in some socioeconomic sectors. The changes in the pattern of electricity consumption during the pandemic years (2020–2021) affected the accuracy of the ML models in predicting electricity consumption in 2022.

Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar.

This study investigates the water - electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors. Due to inadequate research on spatial modelling of water - electricity consumption in the context of the COVID-19 pandemic, this study investigated geographical block-level variation in water and electricity consumption in Doha city of Qatar. Spatial analyses were performed to investigate the spatial differences in each sector. Five geospatial techniques in a Geographical Information System (GIS) context were used in the study. Moran's I, Anselin Local Moran's I, and Getis-Ord Gi∗ statistics tools were used to identify the hot spots and cold spots of water and electricity consumption in each sector. Furthermore, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to investigate the spatial relationship between water and electricity consumption during the pandemic year. The findings show that there is a distinction in water and electricity consumption at the block level across all sectors and over time. Hot spot and spatial regression analysis reveal spatial and temporal heterogeneities in the study area across the six socioeconomic sectors. The intensity of hot spots of water and electricity consumption are found in the southern and western parts of the city due to high population density and the concentration of the commercial and industrial areas. Furthermore, analyzing the spatiotemporal correlation between the water and electricity consumption across the six sectors shows variation within and between these sectors over space and time. The results show a positive relationship between water and electricity consumption in some blocks and over time of each sector. During the lockdown phase, strong positive correlation between water and electricity consumption have exist in the residential sector due to extra water and electricity footprints in this sector. Conversely, the water and electricity consumption were positively correlated but declined in the industrial and commercial sector due to the curtailment in production, economic activities, and reduction in people's mobility. Mapping the hot spot blocks and the blocks with high relationship between water and electricity consumption could provide useful insight to decision-makers for targeted interventions.

Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society;impacts spread further and reached the entire Islamic society and other societies, too.

Modeling electricity consumption patterns during the COVID-19 pandemic across six socioeconomic sectors in the State of Qatar

The propagation of the COVID-19 pandemic, and the associated measures taken by many countries to slow down the spread of the disease, has significantly affected all aspects of people's lives, including the global energy sector. This study aims to investigate the impact of the pandemic on the spatial patterns of electricity consumption in six socioeconomic sectors (residential (villa and flat), industrial, commercial, government, and productive farms) in the State of Qatar. The spatiotemporal patterns of electricity consumption have been assessed using various Geographic Information Systems (GIS) and spatial statistical modeling prior and during the pandemic. The results demonstrate variations in electricity consumption within and between the six sectors. The main changes in the electricity consumption within sectors during the pandemic year is during the lockdown phase. Spatially, some sectors are affected by the pandemic, and hence the pattern and the spatial and temporal distribution of electricity consumption has changed during the pandemic year compared to pre-pandemic years. The results also show that there were variations of spatial clustering of electricity consumption among these sectors. Most of the high-high clustering patterns are located in the mid-eastern and northeastern parts of Qatar. The highest variation in electricity consumption between sectors occurred in the productive farms due to its massive development during the pre-pandemic period and were not affected by the pandemic. There is a sharp decline in electricity consumption in both the industrial and commercial sectors during the pandemic year. Other sectors witnessed an increase in electricity consumption during the summer months, which was mainly due to travel restrictions imposed by many countries around the world. This analysis is vital for policymakers to detect the changes in electricity consumption patterns in the context of emergencies such as the pandemic.