Urban exodus? Understanding human mobility in Britain during the COVID-19 pandemic using Meta-Facebook data.

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.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Spatial Variation in the Quality of American Community Survey Estimates.

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

Geographical characterisation of British urban form and function using the spatial signatures framework.

The spatial arrangement of the building blocks that make up cities matters to understand the rules directing their dynamics. Our study outlines the development of the national open-source classification of space according to its form and function into a single typology. We create a bespoke granular spatial unit, the enclosed tessellation, and measure characters capturing its form and function within a relevant spatial context. Using K-Means clustering of individual enclosed tessellation cells, we generate a classification of space for the whole of Great Britain. Contiguous enclosed tessellation cells belonging to the same class are merged forming spatial signature geometries and their typology. We identify 16 distinct types of spatial signatures stretching from wild countryside, through various kinds of suburbia to types denoting urban centres according to their regional importance. The open data product presented here has the potential to serve as boundary delineation for other researchers interested in urban environments and policymakers looking for a unique perspective on cities and their structure.

Functional signatures in Great Britain: A dataset.

The spatial distribution of activities and agents within cities, conceptualised as an urban function, profoundly affects how different areas are perceived and lived. This dataset introduces the concept of functional signatures - contiguous areas of a similar urban function delineated based on enclosed tessellation cells (ETC) - and applies it to the area of Great Britain. ETCs are granular spatial units, which capture function based on interpolations from open data inputs stretching from remote sensing to land use, census and points of interest data. The spatial extent of each signature type is defined by grouping ETCs using cluster analysis, based on similarity between their functional profiles, inferred by the data linked to each cell. This approach results in a dataset that reflects urban function as a composite of aspects, rather than a singular use, and is built up from granular spatial units. Furthermore, the underlying data are sourced from available open data products, which together with a method and code fully available, yields a fully reproducible pipeline and makes our dataset and open data product. Both the final classification composed of 17 types of functional signatures and the underlying data collected on the level of enclosed tessellation cells are included in the release and described in this report.

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.