Inequalities in the London bicycle sharing system revisited: impacts of extending the scheme to poorer areas but then doubling prices

Cycling confers transport, health and environmental benefits, and bicycle sharing systems are an increasingly popular means of promoting urban cycling. Following the launch of the London bicycle sharing system (LBSS) in 2010, women and residents of deprived areas were under-represented among initial users. This paper examines how the profile of users has changed across the scheme's first 3 years, using total-population registration and usage data. We find that women still make fewer than 20% of all 'registered-use' LBSS trips, although evidence from elsewhere suggests that the introduction of 'casual' use has encouraged a higher overall female share of trips. The proportion of trips by registered users from 'highly-deprived areas' (in the top tenth nationally for income deprivation) rose from 6% to 12%. This was due not only to the 2012 LBSS extension to some of London's poorest areas, but also to a steadily increasing share of trips by residents of highly-deprived areas in the original LBSS zone. Indirect evidence suggests, however, that the twofold increase in LBSS prices in January 2013 has disproportionately discouraged casual-use trips among residents of poorer areas. We conclude that residents in deprived areas can and do use bicycle sharing systems if these are built in their local areas, and may do so progressively more over time, but only if the schemes remain affordable relative to other modes

Retail Vibrancy and the Composition of British High Streets

This abstract addresses one element of the Consumer Data Research Centre’s work exploring the vibrancy of retail areas in Britain. In this case the term ‘retail vibrancy’ encompasses the economic, social and community-developmental health of high streets. Specifically, the research will detail the creation of a database to encapsulate the prevailing demographic characteristics of high streets in order to explore their relationships to high street store turnover, footfall, and vacancy rate

An Investigation of the Impact and Resilience of British High Streets Following the COVID-19 Lockdown Restrictions

British high streets have faced significant economic and cultural challenges as a consequence of the COVID-19 pandemic. This is predominantly due to government enforced restrictions which required all 'non-essential' retail to close, resulting in a significant change in the way consumers interacted with high streets. While all premises related to the retail or hospitality sector were forced to close, leading to rising vacancy rates, some high streets were more resilient to the economic shock than others. In this paper we detect some of the unforeseen consequences of the pandemic on British high streets and create a measure of resilience. The impact of the lockdown restrictions have resulted in some high streets, notably Spring Street in Paddington, London, experiencing disproportionate decline. Others including Northolt Road in Harrow, London were able maintain their occupancy. This study provides a typology of high street resilience incorporating the impact of the COVID-19 lockdown restrictions and links the impact of government policy to the economic performance of high streets. The outcomes from this research address both local and national policy contexts as the resilience typology has the potential to assist in funding allocation for recovery and regeneration projects

Vibrancy and Classification of Retail Areas

The term ‘retail vibrancy’ encompasses the economic, social and community health of high streets. This research will develop a vibrancy classification of British high streets and their surrounding retail areas by utilising vacancy and occupier turnover data. The paper compares micro level cluster analysis of high street areas to wider regional and national trends in an attempt to pinpoint areas of high streets that need targeted intervention to meet the needs of the surrounding population

Spatial and social disparities in the decline of activities during the COVID-19 lockdown in Greater London

We use data on human mobility obtained from mobile applications to explore the activity patterns in the neighbourhoods of Greater London as they emerged from the first wave of COVID-19 lockdown restrictions during summer 2020 and analyse how the lockdown guidelines have exposed the socio-spatial fragmentation between urban communities. The location data are spatially aggregated to 1 km2 grids and cross-checked against publicly available mobility metrics (e.g. Google COVID-19 Community Report, Apple Mobility Trends Report). They are then linked to geodemographic classifications to compare the average decline of activities in the areas with different sociodemographic characteristics. We found that the activities in the deprived areas dominated by minority groups declined less compared to the Greater London average, leaving those communities more exposed to the virus. Meanwhile, the activity levels declined more in affluent areas dominated by white-collar jobs. Furthermore, due to the closure of non-essential stores, activities declined more in premium shopping destinations and less in suburban high streets

Getting to the Point? Rethinking Arrows on Maps

Introduction. Maps help to form public opinion and build public morale. When the war is over, they will contribute to shaping the thought and action of those responsible for the reconstruction of a shattered world. Hence it is important in these times that the nature of the information they set forth should be well understood (Wright, 1942: 527)

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

The Influence of Shape Constraints on the Thresholding Bandit Problem

We investigate the stochastic Thresholding Bandit problem (TBP) under several shape constraints. On top of (i) the vanilla, unstructured TBP, we consider the case where (ii) the sequence of arm's means $(\mu_k)_k$ is monotonically increasing MTBP, (iii) the case where $(\mu_k)_k$ is unimodal UTBP and (iv) the case where $(\mu_k)_k$ is concave CTBP. In the TBP problem the aim is to output, at the end of the sequential game, the set of arms whose means are above a given threshold. The regret is the highest gap between a misclassified arm and the threshold. In the fixed budget setting, we provide problem independent minimax rates for the expected regret in all settings, as well as associated algorithms. We prove that the minimax rates for the regret are (i) $\sqrt{\log(K)K/T}$ for TBP, (ii) $\sqrt{\log(K)/T}$ for MTBP, (iii) $\sqrt{K/T}$ for UTBP and (iv) $\sqrt{\log\log K/T}$ for CTBP, where $K$ is the number of arms and $T$ is the budget. These rates demonstrate that the dependence on $K$ of the minimax regret varies significantly depending on the shape constraint. This highlights the fact that the shape constraints modify fundamentally the nature of the TBP

Delineating Europe\u27s Cultural Regions: Population Structure and Surname Clustering

Surnames (family names) show distinctive geographical patterning and in many disciplines remain an underutilized source of information about population origins, migration and identity. This paper investigates the geographical structure of surnames, using a unique individual level database assembled from registers and telephone directories from 16 European countries. We develop a novel combination of methods for exhaustively analyzing this multinational data set, based upon the Lasker Distance, consensus clustering and multidimensional scaling. Our analysis is both data rich and computationally intensive, entailing as it does the aggregation, clustering and mapping of 8 million surnames collected from 152 million individuals. The resulting regionalization has applications in developing our understanding of the social and cultural complexion of Europe, and offers potential insights into the long and short-term dynamics of migration and residential mobility. The research also contributes a range of methodological insights for future studies concerning spatial clustering of surnames and population data more widely. In short, this paper further demonstrates the value of surnames in multinational population studies and also the increasing sophistication of techniques available to analyze them