Family names as indicators of Britain’s changing regional geography

In recent years the geography of surnames has become increasingly researched in genetics, epidemiology, linguistics and geography. Surnames provide a useful data source for the analysis of population structure, migrations, genetic relationships and levels of cultural diffusion and interaction between communities. The Worldnames database (www.publicprofiler.org/worldnames) of 300 million people from 26 countries georeferenced in many cases to the equivalent of UK Postcode level provides a rich source of surname data. This work has focused on the UK component of this dataset, that is the 2001 Enhanced Electoral Role, georeferenced to Output Area level. Exploratory analysis of the distribution of surnames across the UK shows that clear regions exist, such as Cornwall, Central Wales and Scotland, in agreement with anecdotal evidence. This study is concerned with applying a wide range of methods to the UK dataset to test their sensitivity and consistency to surname regions. Methods used thus far are hierarchical and non-hierarchical clustering, barrier algorithms, such as the Monmonier Algorithm, and Multidimensional Scaling. These, to varying degrees, have highlighted the regionality of UK surnames and provide strong foundations to future work and refinement in the UK context. Establishing a firm methodology has enabled comparisons to be made with data from the Great British 1881 census, developing insights into population movements from within and outside Great Britain

The Surname Space of the Czech Republic: Examining Population Structure by Network Analysis of Spatial Co-Occurrence of Surnames

In the majority of countries, surnames represent a ubiquitous cultural attribute inherited from an individual's ancestors and predominantly only altered through marriage. This paper utilises an innovative method, taken from economics, to offer unprecedented insights into the “surname space” of the Czech Republic. We construct this space as a network based on the pairwise probabilities of co-occurrence of surnames and find that the network representation has clear parallels with various ethno-cultural boundaries in the country. Our inductive approach therefore formalizes a simple assumption that the more frequently the bearers of two surnames concentrate in the same locations the higher the probability that these two surnames can be related (considering ethno-cultural relatedness, common co-ancestry or genetic relatedness, or some other type of relatedness). Using the Czech Republic as a case study this paper offers a fresh perspective on surnames as a quantitative data source and provides a methodology that can be easily incorporated within wider cultural, ethnic, geographic and population genetics studies already utilizing surnames.</p

Regional surnames and genetic structure in Great Britain

Following the increasing availability of DNA-sequenced data, the genetic structure of populations can now be inferred and studied in unprecedented detail. Across social science, this innovation is shaping new bio-social research agendas, attracting substantial investment in the collection of genetic, biological and social data for large population samples. Yet genetic samples are special because the precise populations that they represent are uncertain and ill-defined. Unlike most social surveys, a genetic sample's representativeness of the population cannot be established by conventional procedures of statistical inference, and the implications for population-wide generalisations about bio-social phenomena are little understood. In this paper, we seek to address these problems by linking surname data to a censored and geographically uneven sample of DNA scans, collected for the People of the British Isles study. Based on a combination of global and local spatial correspondence measures, we identify eight regions in Great Britain that are most likely to represent the geography of genetic structure of Great Britain's long-settled population. We discuss the implications of this regionalisation for bio-social investigations. We conclude that, as the often highly selective collection of DNA and biomarkers becomes a more common practice, geography is crucial to understanding variation in genetic information within diverse populations

Big Data Analysis of Population Flow between TfL Oyster and Bicycle Hire Networks in London

This study seeks to undertake an initial analysis of the likely flow of people between the Tube to bicycle hire network in London. Data for the two networks were extracted for a month (April and June 2012) in order to establish the strength of the relationship between them. The results quantify the extent to which Tube commuters impact the capacity utilization of the bicycle network. We expect this research to have implications in the expansion and maintenance of bicycle hire in London and similar schemes around the world

Retail Resilience and Social Media Opinion Mining

This research focuses on the usefulness of social media opinion mining in the retail sector and what constitutes an attractive high-street retail centre from the viewpoint of a consumer. Geo-located Twitter data allows us to establish when, where and what people say about different retail centres. Comparing this data with retail centres of differing vitality could allow us to draw conclusions about how useful and predictive this source could be. Initial analysis revealed some contrasting text content within top ranked and bottom ranked retail centres in Greater London

Integrating New Measures of Retail Unit Attractiveness into Spatial Interaction Models

This paper proposes the use of urban analytics to predict the attractiveness of a retail unit in dense urban environments. Traditional attractiveness measures (e.g. retail unit size and store frontage) are compared against urban integration measures (e.g. reach and betweenness) to explore their predictive power in estimating the magnitudes of consumer flows. The study concludes that using urban centrality metrics, such as betweenness, as attractiveness measure has a higher positive effect on predicting footfall compared to traditional measures

Behavioural Analysis of Smart Card Data

Smart card data captured by automated fare collection (AFC) systems are a valuable resource for the analysis of human behaviour. The paper presents an approach of processing transit data for clustering analysis to identify user activities with similar characteristics. The effectiveness of the methods was evaluated using performance evaluation metrics. An external evaluation was used to compare the results with the ground truth. The results demonstrate that simple methods can produce good results when the input dataset used in the model is prepared and enriched with the most relevant features set

Assessing the impacts of various street-level characteristics on the burden of urban burglary in Kaduna, Nigeria

Evidence suggests that crimes committed in urban environments are geographically concentrated across a range of scales, and that the variation in rates of crime within an urban space is significantly dependent on the physical environment as well as the situation in which the crime takes place. However, these assertions are typically drawn from environmental criminological studies that have focussed on Euro-American cities and western intellectual perspectives. We seek to move beyond these by focussing on a second-tier city in sub-Saharan Africa (Kaduna, Nigeria), a context for which very little literature exists. This paper therefore examines the association between a range of street characteristics and the risk of residential burglary in Kaduna for the first time. It describes a methodology for conducting a household crime victimisation survey in Nigeria, and then aggregating the information to a street-level to perform a population-based ecological study. It integrates street network analysis and statistical modelling techniques in order to provide novel estimates for factors that may increase the risk of burglary such as street accessibility metrics (e.g. connectivity, betweenness and closeness centrality), segment length, socioeconomic status and business activities. Finally, the article provides a discussion on the plausibility and implication of findings within the sub-Saharan African context

Validation of Cardiovascular Magnetic Resonance-Derived Equation for Predicted Left Ventricular Mass Using the UK Biobank Imaging Cohort: Tool for Donor-Recipient Size Matching.

BACKGROUND: Current guidance from International Society for Heart and Lung Transplantation recommends using body weight for donor-recipient size matching for heart transplantation. However, recent studies have shown that predicted heart mass, using body weight, height, age, and sex, may represent a better method of size matching. We aim to validate a cardiovascular magnetic resonance (CMR)-derived equation for predicted left ventricular mass (LVM) in a cohort of normal individuals in the United Kingdom. METHODS: This observational study was conducted in 5065 middle-aged (44-77 years old) UK Biobank participants who underwent CMR imaging in 2014 to 2015. Individuals with cancer diagnosis in the previous 12 months or history of cardiovascular disease were excluded. Predicted LVM was calculated based on participants' sex, height, and weight recorded at the time of imaging. Correlation analyses were performed between the predicted LVM and the LVM obtained from manual contouring of CMR cine images. The analysis included 3398 participants (age 61.5±7.5 years, 47.8% males). RESULTS: Predicted LVM was considerably higher than CMR-derived LVM (mean±SD of 138.8±28.9 g versus 86.3±20.9 g). However, there was a strong correlation between the 2 measurements (Spearman correlation coefficient 0.802, P<0.0001). CONCLUSIONS: Predicted LVM calculated using a CMR-derived equation that incorporates height, weight, and sex has a strong correlation with CMR LVM in large cohort of normal individuals in the United Kingdom. Our findings suggest that predicted heart mass equations may be a valid tool for donor-recipient size matching for heart transplantation in the United Kingdom

Targeted genetic analysis in a large cohort of familial and sporadic cases of aneurysm or dissection of the thoracic aorta

PURPOSE: Thoracic aortic aneurysm/aortic dissection (TAAD) is a disorder with highly variable age of onset and phenotype. We sought to determine the prevalence of pathogenic variants in TAAD-associated genes in a mixed cohort of sporadic and familial TAAD patients and identify relevant genotype–phenotype relationships. METHODS: We used a targeted polymerase chain reaction and next-generation sequencing–based panel for genetic analysis of 15 TAAD-associated genes in 1,025 unrelated TAAD cases. RESULTS: We identified 49 pathogenic or likely pathogenic (P/LP) variants in 47 cases (4.9% of those successfully sequenced). Almost half of the variants were in nonsyndromic cases with no known family history of aortic disease. Twenty-five variants were within FBN1 and two patients were found to harbor two P/LP variants. Presence of a related syndrome, younger age at presentation, family history of aortic disease, and involvement of the ascending aorta increased the risk of carrying a P/LP variant. CONCLUSION: Given the poor prognosis of TAAD that is undiagnosed prior to acute rupture or dissection, genetic analysis of both familial and sporadic cases of TAAD will lead to new diagnoses, more informed management, and possibly reduced mortality through earlier, preclinical diagnosis in genetically determined cases and their family members