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This paper outlines the creation of the London Output Area Classification (LOAC) from the 2021 Census, set within the broader context of geodemographic classification systems in the United Kingdom. The LOAC 2021 was developed in collaboration with the Greater London Authority (GLA) and offers an enhanced, statistically robust typology adept at capturing the unique spatial, socio-economic and built characteristics of London's residential neighbourhoods. The paper asserts the critical importance of nuanced, area-specific geodemographic classifications for urban areas with unique geography relative to the national extent.

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.

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.

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 (NO(2)) concentration responds to changes in human behaviour, in particular human mobility patterns and commuting. We model the dynamic and responsive change in NO(2) concentration in the period directly following national lockdown and respective opening orders. Using the national urban air quality monitoring network we generate a synthetic NO(2) 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 NO(2) 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 NO(2) 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 ...

Retail centres are important tools for understanding the distribution and evolution of the retail sector at varying geographical scales. This paper presents a framework through which formal definitions and typologies of retail centres, such as those in the UK, can be extended to the US. Using Chicago as a case study and data from SafeGraph, we present a retail centre delineation method that combines Hierarchical-DBSCAN with 'H3', and demonstrate the usefulness of a non-hierarchical approach to retail classification. In addition, we show that the dynamicity and comprehensibility of retail centres make them an effective tool through which to better understand the impacts of COVID-19 on retail centre 'health', demonstrating significant scope for a comprehensive delineation of the scale, extent and characteristics of American retail centre agglomerations, providing a tool through which to monitor the evolution of American retail.

Early attempts to classify shopping activity often took a relatively simple approach, largely driven by the lack of reliable data beyond fascia name and retail outlet counts by centre. There seems to be a consensus amongst contemporary scholars, commercial research consultancies and retailers that more comprehensive classifications would generate better-informed debate on changes in the urban economic landscape, as well as providing the basis for a more effective comparison of retail centres across time and space, particularly given the availability of new data sources and techniques and in the context of the transformational changes presently affecting the retail sector. This paper seeks to demonstrate the interrelationship between supply and demand for retailing services by integrating newly available data sources within a rigorously specified classification methodology. This in turn provides new insight into the multidimensional and dynamic taxonomy of consumption spaces within Great Britain. First, such a contribution is significant in that it moves debate within the literature past simple linear scaling of retail centre function to a more nuanced understanding of multiple functional forms; and second, in that it provides a nationally comparative and dynamic framework through which the evolution of retail structures can be evaluated. Using non-hierarchical clustering techniques, the results are presented in the form of a two-tier classification with 5 distinctive 'coarse' clusters and 15 more detailed and nested sub-clusters. The paper concludes that more nuanced and dynamic classifications of this kind can help deliver more effective insights into changing role of retailing and consumer services in urban areas across space and through time and will have implications for a variety of stakeholders.

"An explosive new interdisciplinary field of urban research. This textbook delves into the challenges and opportunities of using new and emerging forms of data to study cities. Topics explored include: data and urban computing infrastructure; sensors and human dynamics; urban modeling; agent-based modeling; [and] visualization and GIS. Over recent years, the way that data are used to understand urban systems has changed dramatically. Cities are constantly adapting to incorporate new technology, and this has fast become a key tool to analysing how cities work. Spanning current and future cities, interviews with key urban analysts, reflective questions and technical case studies, 'Urban analytics' equips the reader with a greater understanding of theory and the technical skills needed for practice." -- rear cover