© 2017 The Authors While the influence of weather on public transport performance and ridership has been the topic for some research, the real-time response of transit usage to variations in weather conditions is yet to be fully understood. This paper redresses this gap by modelling the effect that local weather conditions exert on hourly bus ridership in sub-tropical Brisbane, Australia. Drawing on a transit smart card data set and detailed weather measurements, a suite of time-series regression models are computed to capture the concurrent and lagged effects that weather conditions exert on bus ridership. Our findings highlight that changes in particularly temperature and rainfall were found to induce significant hour-to-hour changes in bus ridership, with such effects varying markedly across both a 24 h period and the transit network. These results are important for public transport service operations in their capacity to inform timely responses to real-time changes in passengers’ travel demand induced by the onset of particular weather conditions

Existing empirical work has focused on assessing the effectiveness of nonpharmaceutical interventions on human mobility to contain the spread of COVID‐19. Less is known about the ways in which the COVID‐19 pandemic has reshaped the spatial patterns of population movement within countries. Anecdotal evidence of an urban exodus from large cities to rural areas emerged during early phases of the pandemic across western societies. Yet, these claims have not been empirically assessed. Traditional data sources, such as censuses offer coarse temporal frequency to analyse population movement over infrequent time intervals. Drawing on a data set of 21 million observations from Meta‐Facebook users, we aim to analyse the extent and evolution of changes in the spatial patterns of population movement across the rural–urban continuum in Britain over an 18‐month period from March 2020 to August 2021. Our findings show an overall and sustained decline in population movement during periods of high stringency measures, with the most densely populated areas reporting the largest reductions. During these periods, we also find evidence of higher‐than‐average mobility from high‐density population areas to low‐density areas, lending some support to claims of large‐scale population movements from large cities. Yet, we show that these trends were temporary. Overall mobility levels trended back to precoronavirus levels after the easing of nonpharmaceutical interventions. Following these interventions, we found a reduction in movement to low‐density areas and a rise in mobility to high‐density agglomerations. Overall, these findings reveal that while COVID‐19 generated shock waves leading to temporary changes in the patterns of population movement in Britain, the resulting vibrations have not significantly reshaped the prevalent structures in the national pattern of population movement. As of 2021, internal population movements sit at an intermediate level between those observed pre‐ and early phases of the pandemic.

Existing empirical work has analysed the impacts of COVID‐19 on mortality, fertility and international migration. Less is known about the ways in which the COVID‐19 pandemic has influenced the patterns of internal migration. Anecdotal reports of mass migration from large cities to less populated areas have emerged, but lack of data has prevented empirically assessing this hypothesis. Drawing on geographically granular administrative population register data, we aim to analyse the extent of change in the patterns of internal migration across the urban hierarchy in Spain during 2020. Our results show a decline of 2.5% in the number of internal migration moves, particularly during the early stages of the pandemic, returning to pre‐pandemic levels in late 2020. Results also reveal unusually large net migration losses in core cities and net migration gains in rural areas. Net migration losses in cities and gains in rural areas particularly accumulated following the elimination of the strict lockdown measures in June. Yet, these net losses and gains trended to pre‐pandemic levels in late 2020, and movements between cities, and between cities and suburbs, continued to dominate the internal migration system. Thus, while the COVID‐19 pandemic exerted notable changes in the geographic balance of internal migration flows, these changes appear to have been temporary and did not significantly alter the existing structures of the national migration system.

Population decline is projected to become widespread in Europe, with the continental population set to reverse its longstanding trajectory of growth within the next 5 years. This represents unfamiliar demographic territory. Despite this, literature on decline remains sparse and our understanding porous. Particular epistemological deficiencies stem from a lack of both cross‐national and temporal analyses of population decline. This study seeks to address these gaps through the novel application of sequence and cluster analysis techniques to examine variations in population decline trajectories since 2000 in 696 subnational areas across 33 European territories. The methodology allows for a holistic understanding of decline trajectories capturing differences in the ordering, timing, magnitude and spatial structure of population decline. We identify a typology of population decline distinguishing seven distinct pathways to depopulation and chart their geographies. Results revealed differentiated pathways of depopulation in continental subregions, with consistent and rapid declines in the east, persistent but moderate declines in central Europe, accelerating declines in the south and decelerating population declines in the west. Results also revealed differentiated patterns of depopulation across the rural–urban continuum, with urban and populous areas experiencing a deceleration in population decline, while decline accelerates or stabilises in rural areas. Small‐ and mid‐sized areas displayed heterogeneous depopulation trajectories, highlighting the importance of local contextual factors in influencing trajectories of population decline.

The current surge in forced migration to Europe is probably the largest and most complex since the Second World War. As population aging accelerates and fertility falls below replacement level, immigration may be seen as a key component of human capital to address labor and skill shortages. Receiving countries are, however, hesitant about the contribution that forced migrants can make to the local economy. Coupled with increasing pressure on welfare services, they are associated with increased job competition and crime. Underutilization of immigrants’ skills is, however, a waste of resources that countries can scarcely afford. Understanding the labor market integration process of forced migrants is thus critical to develop policies that unleash their full skills potential and ultimately foster local economic productivity. While prior studies have examined the employment and salary outcomes of these immigrants at a particular point in time post-migration, they have failed to capture the temporal dynamics and complexity of this process. Drawing on administrative data from Sweden, we examine the occupational pathways of forced migrants using sequence analysis from their arrival in 1991 through to 2013. Findings reveal polarized pathways of long-term labor market integration with over one-third of refugees experiencing a successful labor market integration pathway and an equally large share facing a less fruitful employment outcomes. Our findings suggest education provision is key to promote a more successful integration into the local labor market by reducing barriers of cultural proximity and increasing the occurrence of entrepreneurship activity.

We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution.