Using unstable data from mobile phone applications to examine recent trajectories of retail centre recovery.

The COVID-19 pandemic has changed the ways in which we shop, with significant impacts on retail and consumption spaces. Yet, empirical evidence of these impacts, specifically at the national level, or focusing on latter periods of the pandemic remain notably absent. Using a large spatio-temporal mobility dataset, which exhibits significant temporal instability, we explore the recovery of retail centres from summer 2021 to 2022, considering in particular how these responses are determined by the functional and structural characteristics of retail centres and their regional geography. Our findings provide important empirical evidence of the multidimensionality of retail centre recovery, highlighting in particular the importance of composition, e-resilience and catchment deprivation in determining such trajectories, and identifying key retail centre functions and regions that appear to be recovering faster than others. In addition, we present a use case for mobility data that exhibits temporal stability, highlighting the benefits of viewing mobility data as a series of snapshots rather than a complete time series. It is our view that such data, when controlling for temporal stability, can provide a useful way to monitor the economic performance of retail centres over time, providing evidence that can inform policy decisions, and support interventions to both acute and longer-term issues in the retail sector.

Changing patterns of SARS-CoV-2 infection through Delta and Omicron waves by vaccination status, previous infection and neighbourhood deprivation: a cohort analysis of 2.7 M people.

BACKGROUND: Our study examines if SARS-CoV-2 infections varied by vaccination status, if an individual had previously tested positive and by neighbourhood socioeconomic deprivation across the Delta and Omicron epidemic waves of SARS-CoV-2. METHODS: Population cohort study using electronic health records for 2.7 M residents in Cheshire and Merseyside, England (3rd June 2021 to 1st March 2022). Our outcome variable was registered positive test for SARS-CoV-2. Explanatory variables were vaccination status, previous registered positive test and neighbourhood socioeconomic deprivation. Cox regression models were used to analyse associations. RESULTS: Originally higher SARS-CoV-2 rates in the most socioeconomically deprived neighbourhoods changed to being higher in the least deprived neighbourhoods from the 1st September 2021, and were inconsistent during the Omicron wave. Individuals who were fully vaccinated (two doses) were associated with fewer registered positive tests (e.g., individuals engaged in testing between 1st September and 27th November 2021-Hazards Ratio (HR) = 0.48, 95% Confidence Intervals (CIs) = 0.47-0.50. Individuals with a previous registered positive test were also less likely to have a registered positive test (e.g., individuals engaged in testing between 1st September and 27th November 2021-HR = 0.16, 95% CIs = 0.15-0.18. However, the Omicron period saw smaller effect sizes for both vaccination status and previous registered positive test. CONCLUSIONS: Changing patterns of SARS-CoV-2 infections during the Delta and Omicron waves reveals a dynamic pandemic that continues to affect diverse communities in sometimes unexpected ways.

An open source delineation and hierarchical classification of UK retail agglomerations.

Town centres and high streets typically form the social and commercial cores of UK cities and towns, yet, there is no uniform definition of what a town centre or high street is. In this study the spatial delineations of retail agglomerations are generated using open-source data for England, Wales, Scotland and Northern Ireland. The extent and boundaries of these physical retail areas are identified based on the density and connectivity patterns of individual retail units over space. A high resolution hexagonal grid is superimposed over spatial clusters of retail points and a network-based algorithm used to identify mutually exclusive tracts. Agglomerations are then pruned and fine-tuned according to a series of heuristic rules. Our retail agglomerations represent local commerce areas with shopping amenities and are assigned to a hierarchical classification ranking from the largest Regional Centres, Major Town Centres and Town Centres, down to Small Local Centres and Retail Parks. The classification into one of eleven hierarchies is based on a combination of relative rank in the local area and absolute size of retail units within the area. These retail agglomeration boundaries, hierarchical classification and lookups form an open-source spatial data product available for wide use and research implementation.

Decomposing the Temporal Signature of Nitrogen Dioxide Declines during the COVID-19 Pandemic in UK Urban Areas.

On March 23, 2020, a national lockdown was imposed in the UK to limit interpersonal contact and the spread of COVID-19. Human mobility patterns were drastically adjusted as individuals complied with stay-at-home orders, changed their working patterns, and moved increasingly in the proximity of their home. Such behavioural changes brought about many spillover impacts, among which the sharp and immediate reduction in the concentration of nitrogen-based pollutants throughout the country. This work explores the extent to which urban Nitrogen Dioxide (NO2) concentration responds to changes in human behaviour, in particular human mobility patterns and commuting. We model the dynamic and responsive change in NO2 concentration in the period directly following national lockdown and respective opening orders. Using the national urban air quality monitoring network we generate a synthetic NO2 concentration series built from a time series of historic data to compare expected modelled trends to the actual observed patterns in 2020. A series of pre- and post-estimators are modelled to understand the scale of concentration responsiveness to human activity and varying ability of areas across the UK to comply with the lockdown closing and response to openings. Specifically, these are linked to workday commuting times and observed patterns of human mobility change obtained from Google mobility reports. We find a strong and robust co-movement of air pollution concentration and work-related mobility - concentrations of NO2 during typical weekday commuting hours saw a higher relative drop, moving in tandem with patterns of human mobility around workplaces over the course of lockdowns and openings. While NO2 concentrations remained relatively low around the time of reopening, particularly during commuting hours, there is a relatively fast responsiveness rate to concentrations increasing quickly in line with human activity. With one of the key Government advice for workers to take staggered transportation into work and lessen the burden of rush hours and adopting more flexible work-home arrangements, our results would suggest that reductions in NO2 in urban areas are particularly responsive to broader human patterns and dynamics over time as we transitioned towards new working routines.

Archetypes of Footfall Context: Quantifying Temporal Variations in Retail Footfall in relation to Micro-Location Characteristics.

The UK retail sector is constantly changing and evolving. The increasing share of online sales and the development of out-of-town retail provision, in conjunction with the 2008-09 economic crisis, have disproportionately impacted high streets and physical retail negatively. Understanding and adapting to these changes is fundamental to the vitality, sustainability and prosperity of businesses, communities and the economy. However, there is a need for better information to support attempts to revitalise UK high streets and retail centres, and advances in sensor technology have made this possible. Footfall provides a commonly used heuristic of retail centre vitality and can be increasingly estimated in automated ways through sensing technology. However, footfall counts are influenced by a range of externalities such as aspects of retail centre function, morphology, connectivity and attractiveness. The key contribution of this paper is to demonstrate how footfall patterns are expressed within the varying context of different retail centre architypes providing both a useful tool for benchmarking and planning; but also making a theoretical contribution to the understanding of retail mobilities. This paper integrates a range of contextual data to develop a classification of footfall sensor locations; producing three representations of sensor micro-locations across Great Britain: chain and comparison retail micro-locations, business and independent micro-locations and value-orientated convenience retail micro-locations. These three groups display distinct daily and weekly footfall magnitudes and distributions, which are attributed to micro-locational differences in their morphology, connectivity and function.

The Association between Fast Food Outlets and Overweight in Adolescents Is Confounded by Neighbourhood Deprivation: A Longitudinal Analysis of the Millennium Cohort Study.

The aim of our study is to utilise longitudinal data to explore if the association between the retail fast food environment and overweight in adolescents is confounded by neighbourhood deprivation. Data from the Millennium Cohort Study for England were obtained for waves 5 (ages 11/12; 2011/12; n = 13,469) and 6 (ages 14/15; 2014/15; n = 11,884). Our outcome variable was overweight/obesity defined using age and sex-specific International Obesity Task Force cut points. Individuals were linked, based on their residential location, to data on the density of fast food outlets and neighbourhood deprivation. Structural Equation Models were used to model associations and test for observed confounding. A small positive association was initially detected between fast food outlets and overweight (e.g., at age 11/12, Odds Ratio (OR) = 1.0006, 95% Confidence Intervals (CI) = 1.0002-1.0009). Following adjusting for the confounding role of neighbourhood deprivation, this association was non-significant. Individuals who resided in the most deprived neighbourhoods had higher odds of overweight than individuals in the least deprived neighbourhoods (e.g., at age 11/12 OR = 1.95, 95% CIs = 1.64-2.32). Neighbourhood deprivation was also positively associated to the density of fast food outlets (at age 11/12 Incidence Rate Ratio = 3.03, 95% CIs = 2.80-3.28).

Open data products-A framework for creating valuable analysis ready data.

This paper develops the notion of "open data product". We define an open data product as the open result of the processes through which a variety of data (open and not) are turned into accessible information through a service, infrastructure, analytics or a combination of all of them, where each step of development is designed to promote open principles. Open data products are born out of a (data) need and add value beyond simply publishing existing datasets. We argue that the process of adding value should adhere to the principles of open (geographic) data science, ensuring openness, transparency and reproducibility. We also contend that outreach, in the form of active communication and dissemination through dashboards, software and publication are key to engage end-users and ensure societal impact. Open data products have major benefits. First, they enable insights from highly sensitive, controlled and/or secure data which may not be accessible otherwise. Second, they can expand the use of commercial and administrative data for the public good leveraging on their high temporal frequency and geographic granularity. We also contend that there is a compelling need for open data products as we experience the current data revolution. New, emerging data sources are unprecedented in temporal frequency and geographical resolution, but they are large, unstructured, fragmented and often hard to access due to privacy and confidentiality concerns. By transforming raw (open or "closed") data into ready to use open data products, new dimensions of human geographical processes can be captured and analysed, as we illustrate with existing examples. We conclude by arguing that several parallels exist between the role that open source software played in enabling research on spatial analysis in the 90 s and early 2000s, and the opportunities that open data products offer to unlock the potential of new forms of (geo-)data.

The Evolution Characteristics and Influence Mechanism of Chinese Venture Capital Spatial Agglomeration.

As an emerging financial entity, venture capital has a significant impact on regional development. However, the research on venture capital mainly focuses on the fields of finance, management, and economics, and fewer researchers study venture capital from the perspective of geography and space. This research explored the evolution characteristics and influence mechanism of Chinese venture capital spatial agglomeration. The innovation of this paper lies in including the spatial effect and conducting a spatial econometric analysis of the spatial agglomeration of venture capital in China after the exploratory analysis of the factors affecting the spatial agglomeration of venture capital. Firstly, based on the data of study area, this paper found that the spatial distribution of venture capital in China had an obvious agglomeration characteristic by using multiple measurement methods. Secondly, by constructing the spatial econometric model based on three different spatial weight matrices, we found that the science and technology environment, financial environment, social environment, and entrepreneurial environment levels were the main factors to affect the agglomeration of venture capital. Thirdly, due to the degree of spatial agglomeration of venture capital being divided into three stages in terms of time dimension, after the regression analysis of different periods, we found that the factors which affected spatial agglomeration of venture capital changed significantly with the passage of time. In addition, from the regression results of eastern, central, and western region samples, we can see that the degree of spillover effect was the lowest in the central region, the highest in the western region, and the middle in the eastern region. At last, this paper provided useful policy enlightenment for enterprise innovation, industrial upgrading, and regional economic management.

A deep learning approach to identify unhealthy advertisements in street view images.

While outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements, which may encourage their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool [Formula: see text] Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th-18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.

Open data on health-related neighbourhood features in Great Britain.

Our study details the creation of a series of national open source low-level geographical measures of accessibility to health-related features for Great Britain. We create 14 measures across three domains: retail environment (fast food outlets, gambling outlets, pubs/bars/nightclubs, off-licences, tobacconists), health services (General Practitioners, pharmacies, dentists, hospitals, leisure centres) and the physical environment (green space and air quality). Using the network analysis process of Routino, postcode accessibility (km) to each of these features were calculated for the whole of Great Britain. An average score for each domain was calculated and subsequently combined to form an overall Index highlighting 'Access to Healthy Assets and Hazards'. We find the most accessible healthy areas are concentrated in the periphery of the urban cores, whilst the least accessible healthy areas are located in the urban cores and the rural areas. The open data resource is important for researchers and policy makers alike with an interest in measuring the role of spatial features on health.

Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.

The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' diagnoses and prescribes over the counter medicines for treatment. The self-care movement has important policy implications, perceived to relieve the National Health Service (NHS) burden, increasing patient subsistence and freeing resources for more serious ailments. However, there has been little research exploring how self-medication behaviours vary between population groups due to a lack of available data. The aim of our study is to evaluate how high street retailer loyalty card data can help inform our understanding of how individuals self-medicate in England. Transaction level loyalty card data was acquired from a national high street retailer for England for 2012-2014. We calculated the proportion of loyalty card customers (n ~ 10 million) within Lower Super Output Areas who purchased the following medicines: 'coughs and colds', 'Hayfever', 'pain relief' and 'sun preps'. Machine learning was used to explore how 50 sociodemographic and health accessibility features were associated towards explaining purchasing of each product group. Random Forests are used as a baseline and Gradient Boosting as our final model. Our results showed that pain relief was the most common medicine purchased. There was little difference in purchasing behaviours by sex other than for sun preps. The gradient boosting models demonstrated that socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Our study adds to the self-medication literature through demonstrating the usefulness of loyalty card records for producing insights about how self-medication varies at the national level. Big data offer novel insights that add to and address issues that traditional studies are unable to consider. New forms of data through data linkage may offer opportunities to improve current public health decision making surrounding at risk population groups within self-medication behaviours.

Developing an openly accessible multi-dimensional small area index of 'Access to Healthy Assets and Hazards' for Great Britain, 2016.

Health geographers have been long concerned with understanding how the accessibility of individuals to certain environmental features may influence health and wellbeing. Such insights are increasingly being adopted by policy makers for designing healthy neighbourhoods. To support and inform decision making, there is a need for small area national level data. This paper details the creation of a suite of open access health indicators, including a novel multidimensional index summarising 14 health-related features of neighbourhoods for Great Britain. We find no association of our overall index with physical health measures, but a significant association to mental wellbeing.

Lumbar discal cyst in an elite athlete.

Our patient, a 22-year-old starting wide receiver for an NCAA Division I football team, presented with low back pain and sciatica. A lumbar-spine MRI without contrast demonstrated findings suspicious for discal cyst. The patient was referred for surgery, and the lesion was resected. The rarity of discal cyst makes it difficult to diagnose because most radiologists are not aware of the entity. An organized approach to diagnosis can facilitate appropriate management.