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Engagement with natural areas has increased during the Covid-19 pandemic, and this may well form one of the enduring legacies of this time. A better understanding of human interactions with urban greenspace, and how patterns of use have changed, including inequalities of use, will be crucial for decision makers to adequately manage and direct resources within these natural spaces as we recover from the pandemic. Current evidence on use of natural spaces is limited and does not easily support site-specific analysis or with fine spatio-temporal distinctions. Coupled with difficulties on primary data gathered throughout the pandemic, there is a general knowledge gap on how changing behaviour has reshaped the use of natural areas and what inequalities have arisen in this dynamic. Through the case study of Glasgow’s open spaces, with a specific focus on one urban park, we apply new forms of urban big data from mobile devices to show how the use of greenspace has changed through the restrictions imposed during Covid-19 pandemic. The research findings will help park managers, urban planners, and policymakers better design the recovery and renewal of our cities after the pandemic.

The suburbanization of poverty has been noted in many advanced industrial nations including the UK. Theory focuses on economic and labour market restructuring combined with processes of market- and/or state-led housing change. This paper examines the contributions of housing and welfare reforms. In the UK, housing policy has driven low-income households increasingly to find accommodation in the private rental sector at the same time that welfare reforms have constrained the rents these households can afford. This paper traces the spatial consequence of these reforms, drawing on a novel combination of Government data and a database of private rental adverts. Up to 2011, the shift from social to private renting for low-income households was relatively neutral in its impacts on suburbanization. Since then, low-income households in private renting have been increasingly pushed to less central locations as rents in more central areas have risen faster. The role played by housing and welfare policy in the suburbanization of poverty needs wider consideration. [ABSTRACT FROM AUTHOR]

The past decade has seen the growth of Short-Term Lets (STLs) through Airbnb and other platforms. Measuring the scale of this activity is challenging as platforms appear unwilling to share data. Most research on Airbnb uses data from commercial sources but this raises issues of equity in access and transparency. Open data exist, but geographic and temporal coverage is limited. We consider whether these open data can be used to study the STLs sector, using a city in Scotland. Using 2017 data to construct: (i) property-level estimates of income; and (ii) through comparisons with rents in the long-term rental market, an area-level vulnerability index to identify neighbourhoods at greatest risk of properties switching from long-term to short-term renting. This index is strongly associated with the growth of Airbnb listings over two years. We conclude that open data can provide a valuable basis for research and policy. We discuss strategies for improving knowledge about this sector including through new data collection. [ABSTRACT FROM AUTHOR]