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Cities have specialised in particular urban functions throughout history, with consequential implications for urban and regional patterns of economic and social change. This specialisation takes place within overall national city size distributions and is manifest in different but often similarly variegated residential structures. Here we develop a novel and consistent methodological approach for measuring macro-scale city size and micro-scale residential differentiation using individual digital census records for the period 1881–1901. The use of family names and neighbourhood classification of dominant economic and social roles makes it possible to relate the changing city size distribution in Great Britain to patterns of urban growth and residential differentiation within urban areas. Together, we provide an integrated and consistent methodology that links the classification of all major urban area growth in Great Britain to attendant intra-urban geodemographic changes in urban residential structures. We suggest ways in which this manifests social and economic change across the settlement system for both new and long-established residents.

This paper argues that frequently updated data on the nature of residential moves and the circumstances of movers in the United Kingdom are insufficient for many research purposes. Accordingly, we develop previous research reported in this Journal to re-purpose consumer and administrative data in order to develop annual estimates of residential mobility between all UK neighbourhoods. We use a unique digital corpus of linked individual and household-level consumer registers compiled by the UK Consumer Data Research Centre, comprising over 143 million unique address records pertaining to the entire UK adult population over the period 1997–2016. We describe how records pertaining to individuals vacating a property can be assigned to their most probable residential destination, based on novel methods of matching names, assessing household composition, and using information on the date and probable distance of residential moves. We believe that the results of this analysis contribute highly granular, frequently updated estimates of residential moves that can be used to chart population-wide outcomes of residential mobility and migration behaviour, as well as the socio-spatial characteristics of the sedentary population.

Analysis of changing patterns of ethnic residential segregation is usually framed by the coarse categorisations of ethnicity used in censuses and other large-scale public sector surveys and by the infrequent time intervals at which such surveys are conducted. In this paper, we use names-based classification of Consumer Registers to investigate changing degrees of segregation in England and Wales over the period 1997–2016 at annual resolution. We find that names-based ethnic classification of the individuals that make up Consumer Registers provides reliable estimates of the residential patterning of different ethnic groups and the degree to which they are segregated. Building upon this finding, we explore more detailed segregation patterns and trends of finer groups at annual resolutions and discover some unexpected trends that have hitherto remained unrecorded by Census-based studies. We conclude that appropriately processed Consumer Registers hold considerable potential to contribute to various domains of urban geography and policy.

We develop bespoke geospatial routines to typify 88,457 surnames by their likely ancestral geographic origins within Great Britain. Linking this taxonomy to both historic and contemporary populatio...

Empirical analysis of social mobility is typically framed by outcomes recorded for only a single, recent generation, ignoring intergenerational preconditions and historical conferment of opportunity. We use the detailed geography of relative deprivation (hardship) to demonstrate that different family groups today experience different intergenerational outcomes and that there is a distinct Great Britain-wide geography to these inequalities. We trace the evolution of these inequalities back in time by coupling family group level data for the entire Victorian population with a present day population-wide consumer register. Further geographical linkage to neighbourhood deprivation data allows us to chart the different social mobility outcomes experienced by every one of the 13,378 long-established family groups. We identify clear and enduring regional divides in England and Scotland. In substantive terms, use of family names and new historical digital census resources are central to recognising that geography is pivotal to understanding intergenerational inequalities., Intergenerational preconditions and historical conferment of opportunity play a role in social mobility. This study considers the geography of relative deprivation to show how different family groups across Great Britain experience different intergenerational outcomes.

ObjectiveTo identify ethnic differences in proportion positive for SARS-CoV-2, and proportion hospitalised, proportion admitted to intensive care and proportion died in hospital with COVID-19 during the first epidemic wave in Wales.DesignDescriptive analysis of 76 503 SARS-CoV-2 tests carried out in Wales to 31 May 2020. Cohort study of 4046 individuals hospitalised with confirmed COVID-19 between 1 March and 31 May. In both analyses, ethnicity was assigned using a name-based classifier.SettingWales (UK).Primary and secondary outcomesAdmission to an intensive care unit following hospitalisation with a positive SARS-CoV-2 PCR test. Death within 28 days of a positive SARS-CoV-2 PCR test.ResultsUsing a name-based ethnicity classifier, we found a higher proportion of black, Asian and ethnic minority people tested for SARS-CoV-2 by PCR tested positive, compared with those classified as white. Hospitalised black, Asian and minority ethnic cases were younger (median age 53 compared with 76 years; pConclusionsThis study adds to the growing evidence that ethnic minorities are disproportionately affected by COVID-19. During the first COVID-19 epidemic wave in Wales, although ethnic minority populations were less likely to be tested and less likely to be hospitalised, those that did attend hospital were younger and more likely to be admitted to intensive care. Primary, secondary and tertiary COVID-19 prevention should target ethnic minority communities in Wales.

ObjectivesTo study ethnic inequalities in ambulatory care sensitive conditions (ACSC) in England.DesignObservational study of inpatient hospital admission database enhanced with ethnicity coding of patient surnames. The primary diagnosis of the first episode in spells with emergency admission were coded with definitions for acute ACSC, chronic ACSC and vaccine-preventable diseases.SettingNational Health Service England.Participants916 375 ACSC emergency admissions in 7 39 618 patients were identified between April 2011 and March 2012.Main outcome measuresORs of ACSC for each ethnic group relative to the White British majority group adjusted for age, sex and area deprivation.ResultsAcute ACSC admission risk adjusted for age and sex was particularly high among Other (OR 1.73; 95% CI 1.69 to 1.77) and Pakistani (1.51; 95% CI 1.48 to 1.54) compared with White British patients. For chronic ACSC, high risk was found among Other (2.02; 95% CI 1.97 to 2.08), Pakistani (2.07; 95% CI 2.02 to 2.12) and Bangladeshi (1.36; 95% CI 1.30 to 1.42). For vaccine-preventable diseases, other (2.42; 95% CI 2.31 to 2.54), Pakistani (1.94; 95% CI 1.85 to 2.04), Bangladeshi (1.48; 95% CI 1.36 to 1.62), Black African (1.45; 95% CI 1.36 to 1.54) and white other (1.38; 95% CI 1.33 to 1.43) groups. Elevated risk was only partly explained in analyses also adjusting for area deprivation.ConclusionsACSC admission was especially high among individuals of Bangladeshi, Pakistani, Black African, white other or other background with up to twofold differences compared with the white British group. This suggests that these ethnic groups are not receiving optimal primary care.

The barriers to social integration posed by ethnic residential segregation are currently receiving renewed attention in Great Britain. A common characteristic of past studies of ethnic segregation ...

The accurate measurement of human activity with high spatial and temporal granularity is crucial for understanding the structure and function of the built environment. With increasing mobile ownership, the Wi-Fi ‘probe requests’ generated by mobile devices can act as a cheap, scalable and real-time source of data for establishing such measures. The two major challenges we face in using these probe requests for estimating human activity are: filtering the noise generated by the uncertain field of measurement and clustering anonymised probe requests generated by the same devices together without compromising the privacy of the users. In this paper, we demonstrate that we can overcome these challenges by using class intervals and a novel graph-based technique for filtering and clustering the probe requests which in turn, enables us to reliably measure real-time pedestrian footfall at retail high streets.

BackgroundPopulation and social structure act as important confounders on pathways linking genotypes to health outcomes. This study examines whether the historical, geographical origins of British surnames – as markers of population structure - are associated with differential health outcomes today. MethodsWe coded the hospital admissions of more than 30 million patients in England between 1999 and 2013 to their surname origin and divided their diagnoses into 125 major disease categories. A base population dataset was constructed with patients’ first admission of any kind. Age- and sex-standardised odds ratios were calculated with logistic regression using patients with ubiquitous English surnames such as “Smith” as reference. Using a data mining approach, we scanned the results for “signals”, where a branch of related surname origins all had significantly higher or lower risk than the reference group. We subsequently studied the age- and sex-standardised incidence for each signal across the density of the surname origin (quintiles) as well as quintiles of area deprivation. ResultsWe identified a signal with Scottish surnames (alcohol-related disorders) and three with different branches of English surnames (disorders of teeth and jaw, fractures, upper gastrointestinal disorders). For the three English surname groups, the risk was only different to patients with other surnames in the quintile with the highest density of that group. Differential risk remained when studied across quintiles of area deprivation. ConclusionsThe study shows that surname origins are associated with diverse health outcomes and may thus act as combined markers of population structure over and above area deprivation.

Like many other cosmopolitan neighbourhoods around the world, several neighbourhoods in East London have experienced rapid social and demographic change through gentrification. This chapter harnesses linked consumer and administrative data collected over a 20-year period to measure the geodemographic changes that have occurred in three neighbourhoods in London’s city fringe: Hoxton East and Shoreditch, Spitalfields and Banglatown, and Whitechapel. Using an address-level linked database, representative of the vast majority of the adult population in the United Kingdom, we produce highly granular estimations of geodemographic characteristics such as ethnicity and we characterise residential moves by their origins and destinations.

Background Accurate recording of ethnicity in electronic healthcare records is important for the monitoring of health inequalities. Yet until the late 1990s, ethnicity information was absent from more than half of records of patients who received inpatient care in England. In this study, we report on the usefulness of a names-based ethnicity classification, Ethnicity Estimator (EE), for addressing this gap in the hospital records. Materials and methods Data on inpatient hospital admissions were obtained from Hospital Episode Statistics (HES) between April 1999 and March 2014. The data were enhanced with ethnicity coding of participants’ surnames using the EE software. Only data on the first episode for each patient each year were included. Results A total of 111,231,653 patient-years were recorded between April 1999 and March 2014. The completeness of ethnicity records improved from 59.5 % in 1999 to 90.5 % in 2013 (financial year). Biggest improvement was seen in the White British group, which increased from 55.4 % in 1999 to 73.9 % in 2013. The correct prediction of NHS-reported ethnicity varied by ethnic group (2013 figures): White British (89.8 %), Pakistani (81.7 %), Indian (74.6 %), Chinese (72.9 %), Bangladeshi (63.4 %), Black African (57.3 %), White Other (50.5 %), White Irish (45.0 %). For other ethnic groups the prediction success was low to none. Prediction success was above 70 % in most areas outside London but fell below 40 % in parts of London. Conclusion Studies of ethnic inequalities in hospital inpatient care in England are limited by incomplete data on patient ethnicity collected in the 1990s and 2000s. The prediction success of a names-based ethnicity classification tool has been quantified in HES for the first time and the results can be used to inform decisions around the optimal analysis of ethnic groups using this data source.

The analysis of spatial distributions is possible using a broad spectrum of new and existing digital data sources. Challenges can arise with respect to use of areal units that are both appropriate and compatible. In addition, regional statistics are prone to scale and aggregation effects that manifest the modifiable areal unit problem (MAUP). This paper introduces a web mapping system that allows users to experiment with standard and bespoke zonal schemes in the geodemographic analysis of regional patterns. We describe the architecture and design of the platform and its associated data processing techniques before demonstrating its value through user case scenarios. Using the segregation index as an example, we demonstrate how the use of interactive maps can assist in revealing the scale-dependent nature of the index. Our web mapping system can be employed to help geography students, policymakers and researchers better understand the underlying geodemographic structure of functional regions.

The nineteenth century saw rapid urbanization and dramatic social change in Great Britain, some of which can now be viewed at national scales for the first time through linkage of georeferenced digital historical data to contemporary and historical framework data. Here, we attempt to georeference every individual address record from the 1881, 1891, and 1901 censuses for Great Britain and to define the fast-growing historical street networks and residential geographies of every urban settlement. We next devise a scale-free historical geodemographic classification using variables common to these three censuses and assign cluster group characteristics to every urban street segment. We also link the evolution of the urban street morphology with changes in residential differentiation and the geodemographic assignments over the twenty-year study period. The results of this intensive data processing make it possible to chart the development of urban residential areas across Great Britain and bring focus to the changing social structures of the cities. We examine these changes with examples drawn from the entire British urban settlement system. Our conclusions discuss the implications of this extensive analysis for improved understanding of the evolution of Great Britain’s urban system.

Background Ethnic inequalities in health are well-known and partly explained by social determinants such as poorer living and working conditions, health behaviours, discrimination, social exclusion, and healthcare accessibility factors. Inequalities are known both for self-reported health and for diseases such as diabetes, cardiovascular diseases, respiratory diseases, and non-specific chest pains. Most studies however concern individual diseases or self-reported health and do not provide an overview that can detect gaps in existing knowledge. The aim of this study is thus to identify ethnic inequalities in inpatient hospital admission for all major disease categories in England. Methods Observational study of the inpatient hospital admission database in England enhanced with ethnicity coding of participants’ surnames. The primary diagnosis was coded to Level 1 of the Global Burden of Disease groups. For each year, only the first admission for each condition for each participant was included. If a participant was readmitted within two days only the first admission was counted. Admission risk for all major disease groups for each ethnic group relative to the White British group were calculated using logistic regression adjusting for age and area deprivation. Results 40,928,105 admissions were identified between April 2009 and March 2014. Ethnic inequalities were found in cardiovascular diseases, respiratory diseases, chest pain, and diabetes in line with previous studies. Additional inequalities were found in nutritional deficiencies, endocrine disorders, and sense organ diseases. Conclusions The results of this study were consistent with known inequalities, but also found previously unreported disparities in nutritional deficiencies, endocrine disorders, and sense organ diseases. Further studies would be required to map out the relevant care pathways for ethnic minorities and establish whether preventive measures can be strengthened.

There is growing evidence that ethnic minorities in Europe are disproportionately affected by Covid-19. Using a name-based ethnicity classifier, we found that hospitalised Black, Asian and minority ethnic cases were younger and more likely to be admitted to intensive care (ICU). Pakistani, Bangladeshi and White - other than British or Irish, ethnic groups were most at risk. In this study, older age and male gender, but not ethnicity, were associated with death in hospitalised patients.

Big data analytics and artificial intelligence, paired with blockchain technology, the Internet of Things, and other emerging technologies, are poised to revolutionise urban management. With massive amounts of data collected from citizens, devices, and traditional sources such as routine and well-established censuses, urban areas across the world have – for the first time in history – the opportunity to monitor and manage their urban infrastructure in real-time. This simultaneously provides previously unimaginable opportunities to shape the future of cities, but also gives rise to new ethical challenges. This paper provides a transdisciplinary synthesis of the developments, opportunities, and challenges for urban management and planning under this ongoing ‘digital revolution’ to provide a reference point for the largely fragmented research efforts and policy practice in this area. We consider both top-down systems engineering approaches and the bottom-up emergent approaches to coordination of different systems and functions, their implications for the existing physical and institutional constraints on the built environment and various planning practices, as well as the social and ethical considerations associated with this transformation from non-digital urban management to data-driven urban management.

Do administrative boundaries correspond to the observable ways in which people interact in urban space? As cities grow in complexity, and people interact over long distances with greater ease, so partitioning of cities needs to depart from conventional gravity models. The current state-of-the-art for uncovering interactional regions, i.e. regions reflective of observable human mobility and interaction patterns, is to apply community detection to networks constructed from vast amounts of human interactions, such as phone calls or flights. This approach is well suited for origin–destination activities, but not for activities involving multiple locations, such as police patrols, and is blind to spatial anomalies. As a result of the latter, community detection generates geographically coherent regions, which may appear plausible but give no insights into forces other than gravity that shape our interaction patterns.This paper proposes novel approaches to regional delineation that address the aforement...

The deployment of loyalty card and other consumer data in geographic research brings opportunities to explore and understand patterns of purchasing behaviour in unprecedented detail. However, valid generalisation requires thorough evaluation of their potential bias. We argue that, in competitive markets where consumers can choose to shop across competing retailers, loyalty card data from just one of these may not represent a ‘complete’ view of all purchases, and that this ‘completeness’ must be controlled for when assessing bias. To this end, we undertake a UK wide analysis of loyalty card data assembled by a major UK grocery retailer and provide guidelines for their effective deployment in the domains of urban and retail analysis. We assess, for the first time, the ‘completeness’ of circa 500 million customer transactions recorded by a major customer loyalty programme in representing the overall purchasing patterns of circa 16 million consumers across the entire UK, and develop a method by which to do this. Moreover, no operator has complete national store coverage, and so we suggest ways of accommodating this when conducting analysis using loyalty card data. We illustrate the importance of these issues before providing recommendations for the wider use of consumer loyalty card data.

Geocoding historical addresses is a primary yet nontrivial application of spatial analysis in historical geographic information systems (GIS) and spatial humanities. We demonstrate our endeavours of geo-referencing and visualising historical census addresses in England and Wales, by matching the residential addresses to a historical gazetteer and a contemporary address database of Britain. The results indicate that it is feasible to standardise and geocode a large share of unique addresses from the historical database. The historical gazetteer and the modern address registers are two complementary data assets that can be used to geo-reference both well-formatted addresses in urban areas and non-standard addresses such as place names or building names in rural areas. The geo-referenced historical census data open up new opportunities for a broad spectrum of geo-demographic research on historical population characteristics at the micro level in England and Wales.

Economic, physical, built, cultural, learning, social and service environments have a profound effect on lifelong health. However, policy thinking about health research is dominated by the ‘biomedical model’ which promotes medicalisation and an emphasis on diagnosis and treatment at the expense of prevention. Prevention research has tended to focus on ‘downstream’ interventions that rely on individual behaviour change, frequently increasing inequalities. Preventive strategies often focus on isolated leverage points and are scattered across different settings. This paper describes a major new prevention research programme that aims to create City Collaboratory testbeds to support the identification, implementation and evaluation of upstream interventions within a whole system city setting. Prevention of physical and mental ill-health will come from the cumulative effect of multiple system-wide interventions. Rather than scatter these interventions across many settings and evaluate single outcomes, we will test their collective impact across multiple outcomes with the goal of achieving a tipping point for better health. Our focus is on early life (ActEarly) in recognition of childhood and adolescence being such critical periods for influencing lifelong health and wellbeing.

Identifying socio-spatial pa erns through geodemographic classification has provenutility over a range of disciplines. While most of these spatial classification systems include a plethora of socioeconomic attributes, there is arguably little to no input regarding attributes of the built environment or physical space, and their relationship to socioeconomic profiles within this context has not been evaluated in any systematic way. This research explores the generation of neighbourhood characteristics and other attributes using a geographic data science approach, taking advantage of the increasing availability of such spatial data from open data sources. We adopt a SOM (Self-Organizing Maps) methodology to create a classification of Multidimensional Open Data Urban Morphology (MODUM) and test the extent to which this output systematically follows conventional socioeconomic profiles. Such an analysis can also provide a simplified structure of the physical properties of geographic space that can be further used as input to more complex socioeconomic models.

This paper presents the methodology that has been used to create the 2011 Area Classification for Output Areas (2011 OAC). This extends a lineage of widely used public domain census only geodemographic classifications in the UK. It provides an update to the successful 2001 OAC methodology, and summarizes the social and physical structure of neighbourhoods using data from the 2011 UK Census. We also present the results of a user engagement exercise that underpinned the creation of an updated methodology for the 2011 OAC. The 2011 OAC comprises 8 Supergroups, 26 Groups and 76 Subgroups. Finally, we present an example of the results of the classification in Southampton.

A geodemographic classification provides a set of categorical summaries of the built and socio-economic characteristics of small geographic areas. Many classifications, including that developed in this paper, are created entirely from data extracted from a single decennial census of population. Such classifications are often criticised as becoming less useful over time because of the changing composition of small geographic areas. This paper presents a methodology for exploring the veracity of this assertion, by examining changes in UK census-based geodemographic indicators over time, as well as a substantive interpretation of the overall results. We present an innovative methodology that classifies both 2001 and 2011 census data inputs utilising a unified geography and set of attributes to create a classification that spans both census periods. Through this classification, we examine the temporal stability of the clusters and whether other secondary data sources and internal measures might usefully indicate local uncertainties in such a classification during an intercensal period.

This chapter describes some of the rapid developments in data collection and analysis through the processing of data collected and archived in real time that are capable of generating new insights into urban processes that in time, might lead to new theories of how cities function. It will focus on London both for its strategic importance as a global city, but also because its citizens are highly connected, and thus capable of generating a large number of datasets. These are individual-based and largely built from the bottom up. We believe that many of these aspects of London’s new data system will soon be replicated in other European cities, and it is clear that similar developments are already taking place in other world cities such as New York City and Singapore; it thus provides a useful basis on which to demonstrate another role of CyberGIS and its related technologies through the analysis of ‘big’ data, albeit in the urban domain.

This paper examines the association between given and family names and self-ascribed ethnicity as classified by the 2011 Census of Population for England and Wales. Using Census data in an innovative way under the new Office for National Statistics (ONS) Secure Research Service (SRS; previously the ONS Virtual Microdata Laboratory, VML), we investigate how bearers of a full range of given and family names assigned themselves to 2011 Census categories, using a names classification tool previously described in this journal. Based on these results, we develop a follow-up ethnicity estimation tool and describe how the tool may be used to observe changing relations between naming practices and ethnic identities as a facet of social integration and cosmopolitanism in an increasingly diverse society.

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.

This paper seeks and uses highly disaggregate social media sources to characterize Greater London in terms of flows of people with modelled individual characteristics, as well as conventional measures of land use morphology and night-time residence. We conduct three analyses. First, we use the Shannon Entropy measure to characterize the geography of information creation across the city. Second, we create a geo-temporal demographic classification of Twitter users in London. Third, we begin to use Twitter data to characterize the links between different locations across the city. We see all three elements as data rich, highly disaggregate geo-temporal analysis of urban form and function, albeit one that pertains to no clearly defined population. Our conclusions reflect upon this severe shortcoming in analysis using social media data, and its implications for progressing our understanding of socio-spatial distributions within cities.

This paper presents a preliminary empirical evaluation of the strategic importance of infusing Twitter social media data into classifications of small areas, as a way of moving beyond the nighttime residential geographies of conventional geodemographic classifications. We attempt an empirically based critique of the merits and drawbacks of the use of social media data, in which the value of high spatial and temporal granularity of revealed activity patterns is contrasted with the paucity of individual attribute information. We apply new and novel methods to enrich the profiles of Twitter users in order to generalize about activity patterns in London, our case-study city. More insidious problems in the use of social media data arise from the as-yet-unknown sources and operation of bias in their user bases. Our contribution is to begin to identify and assess the biases inherent in social media usage in social research, and use these to evaluate their deployment in research applications. Keywords: geodemographics, GIS, social media

The ability to infer routes taken by vehicles from sparse and noisy GPS data is of crucial importance in many traffic applications. The task, known as map-matching, can be accurately approached by a popular technique known as ST-Matching. The algorithm is computationally efficient and has been shown to outperform more traditional map-matching approaches, especially on low-frequency GPS data. The major drawback of the algorithm is a lack of confidence scores associated with its outputs, which are particularly useful when GPS data quality is low. In this paper, we propose a probabilistic adaptation of ST-Matching that equips it with the ability to express map-matching certainty using probabilities. The adaptation, called probabilistic ST-Matching (PST-Matching) is inspired by similarities between ST-Matching and probabilistic approaches to map-matching based on a Hidden Markov Model. We validate the proposed algorithm on GPS trajectories of varied quality and show that it is similar to ST-Matching in terms of accuracy and computational efficiency, yet with the added benefit of having a measure of confidence associated with its outputs.