Urban population size and road traffic collisions in Europe

Millions of road traffic collisions take place every year, leading to significant knock-on effects. Many of these traffic collisions take place in urban areas, where traffic levels can be elevated. Yet, little is known about the extent to which urban population size impacts road traffic collision rates. Here, we use urban scaling models to analyse geographic and road traffic collision data from over 300 European urban areas in order to study this issue. Our results show that there is no significant change in the number of road traffic collisions per person for urban areas of different sizes. However, we find individual urban locations with traffic collision rates which are remarkably high. These findings have the potential to inform policies for the allocation of resources to prevent road traffic collisions across the different cities

The effect of dragon-kings on the estimation of scaling law parameters

Scaling laws are used to model how diferent quantifable properties of cities, such as the number of road trafc accidents or average house prices, vary as a function of city population size, with parameters estimated from data. Arcaute et al. raised the issue of whether specifc cities with extremely large population sizes, known as dragon-kings, should be considered separately from other smaller cities when estimating the scaling law parameters since the two types of cities tend to display diferent behaviour. Through the analysis of randomly generated samples, we fnd that the inclusion of dragon-kings in the scaling analysis does not afect the estimated values for the parameters but only provided that all the data points satisfy the same scaling law. We also analyse randomly generated samples where data corresponding to a particular city deviates from the scaling law followed by the rest of the cities. We then show that deviations corresponding to dragon-king cities have the most signifcant efect on the estimated values of the scaling parameters. The extent of this efect also depends on which estimation procedure is used. Our results have important implications on the suitability of scaling laws as a model for urban systems

Scaling Beyond Cities

City population size is a crucial measure when trying to understand urban life. Many socio-economic indicators scale superlinearly with city size, whilst some infrastructure indicators scale sublinearly with city size. However, the impact of size also extends beyond the city’s limits. Here, we analyse the scaling behaviour of cities beyond their boundaries by considering the emergence and growth of nearby cities. Based on an urban network from African continental cities, we construct an algorithm to create the region of influence of cities. The number of cities and the population within a region of influence are then analysed in the context of urban scaling. Our results are compared against a random permutation of the network, showing that the observed scaling power of cities to enhance the emergence and growth of cities is not the result of randomness. By altering the radius of influence of cities, we observe three regimes. Large cities tend to be surrounded by many small towns for small distances. For medium distances (above 114 km), large cities are surrounded by many other cities containing large populations. Large cities boost urban emergence and growth (even more than 190 km away), but their scaling power decays with distance

Inferring urban polycentricity from the variability in human mobility patterns

The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept 'polycentric city' has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London

Reduced mobility? Urban exodus? Medium-term impacts of the COVID-19 pandemic on internal population movements in Latin American countries

The COVID-19 pandemic has impacted the national systems of population movement around the world. Existing work has focused on countries of the Global North and restricted to the immediate effects of COVID-19 data during 2020. Data have represented a major limitation to monitor change in mobility patterns in countries in the Global South. Drawing on aggregate anonymised mobile phone location data from Meta-Facebook users, we aim to analyse the extent and persistence of changes in the levels (or intensity) and spatial patterns of internal population movement across the rural-urban continuum in Argentina, Chile and Mexico over a 26-month period from March 2020 to May 2022. We reveal an overall systematic decline in the level of short- and long-distance movement during the enactment of nonpharmaceutical interventions in 2020, with the largest reductions occurred in the most dense areas. We also show that these levels bounced back closer to pre-pandemic levels in 2022 following the relaxation of COVID-19 stringency measures. However, the intensity of these movements has remained below pre-pandemic levels in many areas in 2022. Additionally our findings lend some support to the idea of an urban exodus. They reveal a continuing negative net balances of short-distance movements in the most dense areas of capital cities in Argentina and Mexico, reflecting a pattern of suburbanisation. Chile displays limited changes in the net balance of short-distance movements but reports a net loss of long-distance movements. These losses were, however, temporary, moving to neutral and positive balances in 2021 and 2022

“Urban Exodus” During COVID-19in Mexico? UsingDigital Data to AnalyzeMedium-Term Pandemic Impacts on Internal Population Movements

Previous workdocumented a decline of internal population movements andan increase in outflowsfrom large cities to less densely populated areas during COVID-19 inGlobal North countries. However, the impact of the pandemic on levels and patterns of human mobilityacross the rural-urban hierarchyin the Global South is yet to be established.Lack of data with temporal and spatialgranularityhas preventedus fromassessing this research gap.Drawing on locationdata ofFacebook users, weanalyse howthe intensityand patternsof long-distancemovements(>100 Km) were affectedduring April 2020-May 2022 acrossdifferent population densitycategoriesin Mexico.Wefind a decline of40% in the total number of long-distancemovements duringApril-December 2020, anda systematic decrease of outflows and inflows across the rural-urban hierarchy. Unlike inthe Global North, outflowsfrom large citiesdid not increase. The largest drop of outflows and inflowsoccurred in large cities, decliningby 50%. Only specific flows increased during COVID-19, as those from large cities to certain towns, and intra-rural movements. The intensity and patterns ofinternal population movementsacross the rural-urban hierarchyhave progressively returned to pre-pandemic levels during 2021 and 2022, as has occurred in Global North countries. However, therecovery has been slower inthe largeMexicancities

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Uncovering the behaviour of road accidents in urban areas

Different patterns in the incidence of road accidents are revealed when considering areas with increased levels of urbanization. To understand these patterns, road accident data from England and Wales is explored. In particular, the data are used to (i) generate time series for comparison of the incidence of road accidents in urban as opposed to rural areas, (ii) analyse the relationship between the number of road accidents and the population size of a set of urban areas, and (iii) model the likelihood of suffering an accident in an urban area and its dependence with population size. It is observed that minor and serious accidents are more frequent in urban areas, whereas fatal accidents are more likely in rural areas. It is also shown that, generally, the number of accidents in an urban area depends on population size superlinearly, with this superlinear behaviour becoming stronger for lower degrees of severity. Finally, given an accident in an urban area, the probability that the accident is fatal or serious decreases with population size and the probability that it is minor, increases sublinearly. These findings promote the question as to why such behaviours exist, the answer to which will lead to more sustainable urban policies