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COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability.

Low-lying coastal cities are widely acknowledged as the most densely populated places of urban settlement; they are also more vulnerable to risks resulting from intensive land use and land cover change, human activities, global climate change, and the rising sea levels. This study aims to predict how urban growth is affected by sea level rise (SLR) in the Australian context. We develop an urban cellular automata model incorporating urban planning policies as potential drivers or constraints of urban growth under different SLR scenarios and adaption strategies. Drawing on data capturing the socioeconomic and environmental factors in South East Queensland, Australia, our model is positioned to address one core research question: how does SLR affect future urban growth and human resettlement? Results show that urban growth in coastal regions varies depending on the extent to which the sea level rises and is affected by a combination of factors relating to urban planning and human adaptation strategies. Our study demonstrates the complexity of urban growth in coastal regions and the nuanced outcomes under different adaptation strategies in the context of rising sea levels.

Over the past two decades, migration from Mainland China (MC) to Australia has become increasingly significant. In contrast to previous migrant waves, post-2000 MC-born migrants in Australia are more likely to be highly skilled and/or financially independent, and often migrate under skilled, business and investment visa programs. Though scholarship has explored various facets of MC-born migrants’ settlement in Australian cities, the spatial dynamics of settlement, and change over time associated with these shifts in socio-economic profile, have remained unexplored. This paper draws on three census periods, and employs a local indicator of spatial autocorrelation together with a geographically weighted regression. We conclude that the settlement patterns of MC-born migrants in Australia do not follow the ‘straight-line’ assumptions of spatial assimilation theory, nor are they tied explicitly to socio-economic factors in the majority of capital cities. Instead, the settlement patterns of MC-born migrants vary by city: Sydney and Melbourne display the most spatially segmented settlement patterns; while Adelaide and Perth exhibit increasing levels of concentration as the number of MC-born migrants grows. Results indicate that while MC-born migrants are somewhat likely to cluster at the early stage of group formation, within-group spatial patterns are articulated by social attributes as the group grows in size and significance in a metropolitan context.