Understanding inequalities at the is key to inform policy. Neighbourhoods are the key of society and urban spaces. Key social processes occur at the neighbourhood level. Yet, lack of offering longitudinal data on places over a long window of time in …
Existing empirical work has focused on assessing the effectiveness of non-pharmaceutical 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 short-time intervals. Drawing on a data set of 21 million observations from 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 highly dense population areas to low densely populated 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 pre-coronavirus levels after the easing of non-pharmaceutical interventions. Following these interventions, we also 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.
Immigration is a key ingredient for social cohesion and economic development. Yet, it is often portrayed as a major threat to national identity, values, economic stability and security, particularly during challenging times, like the COVID-19 pandemic and economic downturns. With the rise of new technology, much of the discussions around migration and anti-sentiment creation happens online, but little is understood about its development and structure. Using machine learning and network science methods, we aim to study the properties and structure of the tweet network of the debate around migration, analysing the spread, speed and key creators on this network. Key findings provide evidence of a rise in anti-migration sentiment and a small network but highly active number of anti-migrant accounts. We identified key ‘creators’ or ‘spreaders’ of anti-migration sentiment. Results also highlighted the presence of potential ‘counter-hate’ which manifested through a rise in positive sentiment towards migrants or migration following peaks of negative sentiment. An investigation into the role and influence of bots revealed a high number of suspended accounts in the anti-migration network. A scale-free topology of the network shows that a small number of active users drive the spread of anti-migration sentiment. This implies that a targeted approach to tackling online hate could limit the rise in hate crimes towards migrants. This research demonstrates the need for an ongoing framework to monitor and tackle anti-migration sentiment on social media.
Technological advances have enabled the emerge of "Big Data" through the production, processing, analysis and storage of large volumes of digital data. Data that could not previously be stored or used to be captured using analogue devices can now be digitally recorded. These data offer high geographic and temporal granularity, extensive coverage and instant information to measure and transform our understanding of human mobility. Yet, they also present major challenges. This talk identifies these challenges and discusses current practices and potential opportunities for future research.
During this presentation I aim to discuss what is impact, why impact is important for research and the society at large, different pathways to impacts and how academics can use their research to impact society and provide expertise and skills.
Technological advances have enabled the emerge of ‘Big Data’ through the production, processing, analysis and storage of large volumes of digital data. Data that could not previously be stored or used to be captured using analog devices can now be digitally recorded. This chapter identifies and discusses the existing and future challenges and opportunities of Big Data for human geography. Big Data offer high geographic and temporal granularity, extensive coverage and instant information to transform our understanding of human interactions and our social world. At the same time, Big Data present major epistemological, methodological and ethical challenges which need to be addressed to realise these opportunities. I identify the key challenges and actions for the future of human geography emerging from the use of Big Data.
Data have become a central pillar of society. Technological advances in computational power, storage and network platforms have enabled the production, processing, analysis and storage of large volumes of digital data. Information that previously could not be stored, or captured can now be digitally recorded. Digital data have become a key asset for government, businesses and individuals supporting their decision making processes and shaping human behaviour. A notable example has been the use of digital data to monitor the COVID-19 pandemic and inform the development of appropriate interventions. A key data stream has been location data from mobile phones. These data have enabled monitoring the geographic spread of COVID-19 in near-real time with technological companies, such as Apple and Google releasing regular mobility reports. More generally, mobile phone data are a rich source of information offering a unique opportunity to capture human behaviour at an unprecedented geographic and temporal resolution. Yet, key challenges remain, such as issues about privacy, representativeness, biases and the use of large, noisy and complex data sets.
As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the" 15-minutes city" have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure.
Data have become a central pillar of society. Technological advances in computational power, storage and network platforms have enabled the production, processing, analysis and storage of large volumes of digital data. Information that previously could not be stored, or captured can now be digitally recorded. Digital data have become a key asset for government, businesses and individuals supporting their decision making processes and shaping human behaviour. A notable example has been the use of digital data to monitor the COVID-19 pandemic and inform the development of appropriate interventions. A key data stream has been location data from mobile phones. These data have enabled monitoring the geographic spread of COVID-19 in near-real time with technological companies, such as Apple and Google releasing regular mobility reports. More generally, mobile phone data are a rich source of information offering a unique opportunity to capture human behaviour at an unprecedented geographic and temporal resolution. Yet, key challenges remain, such as issues about privacy, representativeness, biases and the use of large, noisy and complex data sets.