797 research outputs found
Mobility insights through consumer data: a case study of concessionary bus travel in the West Midlands
Current transport facilities are often built around efficiency and meeting the needs of the commuting population. These can therefore struggle to provide services suited to some of the most vulnerable members of society. In order to achieve an inclusive transport system, it is vital that transport authorities have access to detailed insights into the mobility needs and demands of different groups of the population. Increasingly, these transport authorities are making use of smart technologies and the resulting data to gain greater insight into transport users, and in turn inform decision making and policy planning. These smart technologies include automated fare collection (AFC) systems, which produce large volumes of detailed transport and mobility data from smart card transactions. To a lesser extent, retail datasets, such as loyalty card transaction data, have also been utilised. The spatiotemporal components of these data can provide valuable insight into the activity patterns of cardholders that may not be captured in traditional transport data. This thesis presents an exploration of these two forms of consumer data, with a focus on the older population in the West Midlands. Firstly, this thesis demonstrates how smart card data can be processed and analysed to provide detailed insights into the mobility patterns of concessionary bus users and how these relate to long-term changes in bus patronage recorded in the study area. Secondly, the extent to which loyalty card transaction data can be employed to understand retail behaviours and activity patterns is explored, with a focus on how these insights can be used to supplement and enhance the understanding of mobility gained from the smart card data. Finally, these insights are discussed in terms of the capacity of the current transport network to meet the mobility needs of the older population and the potential of consumer data for future transport-related research
Investigating tuberculosis transmission using spatial methods
BACKGROUND: Tuberculosis remains a leading infectious cause of death worldwide. Reducing transmission requires an increased focus on local control measures informed by spatial data. Effective use of spatial methods will improve understanding of tuberculosis transmission and support outbreak investigations.
METHODS: I conducted a systematic literature review to describe spatial methods that have been used in previous outbreak investigations (Chapter 2). I developed and evaluated a novel interactive mapping tool, written using the R programming language (Chapter 3). Using multinomial logistic regression and spatial scan statistics, I investigated molecular and spatial clustering of tuberculosis in London (Chapter 4). I described the evolution of a large outbreak of drug-resistant tuberculosis in London in space and time (Chapter 5). Through three case studies, I assessed the utility of a novel spatial tool, geographic profiling, which aims to identify the locations of sources of infectious disease using locations of linked cases (Chapter 6). I analysed the spatial accessibility of tuberculosis services in London using travel time data (Chapter 7).
KEY FINDINGS: • Spatial methods provide an important complementary tool to epidemiological analyses, but are currently under-used (less than half a percent of published outbreak investigations used spatial methods). • Large numbers of tuberculosis cases in London have resulted from local transmission, with more than one in ten cases part of large clusters. • Social complexity and area-level deprivation are associated with transmission of tuberculosis in large clusters. • Geographic profiling may assist with epidemiological investigations of infectious diseases in some circumstances by prioritising areas for investigation. • Pan-London commissioning could improve tuberculosis services by enhancing spatial accessibility.
CONCLUSIONS: Spatial methods provide many valuable contributions to investigations of tuberculosis. Development of new tools and wider use of existing methods could limit the public health impacts of infectious disease outbreaks
Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
The current COVID-19 pandemic has profoundly impacted people's lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people's travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 -Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale
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
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 applications of loyalty card data for social science
Large-scale consumer datasets have become increasingly abundant in recent years and many have turned their attention to harnessing these for insights within the social sciences. Whilst commercial organisations have been quick to recognise the benefits of these data as a source of competitive advantage, their emergence has been met with contention in research due to the epistemological, methodological and ethical challenges they present. These issues have seldom been addressed, primarily due to these data being hard to obtain outside of the commercial settings in which they are often generated. This thesis presents an exploration of a unique loyalty card dataset obtained from one of the most prominent UK high street retailers, and thus an opportunity to study the dynamics, potentialities and limitations when applying such data in a research context. The predominant aims of this work were to firstly, address issues of uncertainty surrounding novel consumer datasets by quantifying their inherent representation and data quality issues and secondly, to explore the extent to which we may enrich our current knowledge of spatiotemporal population processes through the analysis of consumer activity patterns. Our current understanding of such dynamics has been limited by the data-scarce era, yet loyalty card data provide individual level, georeferenced population data that are high in velocity. This provided a framework for understanding more detailed interactions between people and places, and what these might indicate for both consumption behaviours and wider societal phenomena. This work endeavoured to provide a substantive contribution to the integration of consumer datasets in social science research, by outlining pragmatic steps to ensure novel data sources can be fit for purpose, and to population geography research, by exploring the extent to which we may utilise spatiotemporal consumption activities to make broad inferences about the general population
A geographic knowledge discovery approach to property valuation
This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of
property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely
data driven and does not require previous knowledge of the area applied.
To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been
however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm.
For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography
domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial
entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the
investigation of relationships between spatial entities beyond first degree connectivity.
A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality
An attention model and its application in man-made scene interpretation
The ultimate aim of research into computer vision is designing a system which interprets
its surrounding environment in a similar way the human can do effortlessly. However, the
state of technology is far from achieving such a goal. In this thesis different components of
a computer vision system that are designed for the task of interpreting man-made scenes,
in particular images of buildings, are described. The flow of information in the proposed
system is bottom-up i.e., the image is first segmented into its meaningful components and
subsequently the regions are labelled using a contextual classifier.
Starting from simple observations concerning the human vision system and the gestalt laws
of human perception, like the law of “good (simple) shape” and “perceptual grouping”, a
blob detector is developed, that identifies components in a 2D image. These components
are convex regions of interest, with interest being defined as significant gradient magnitude
content. An eye tracking experiment is conducted, which shows that the regions identified
by the blob detector, correlate significantly with the regions which drive the attention of
viewers.
Having identified these blobs, it is postulated that a blob represents an object, linguistically
identified with its own semantic name. In other words, a blob may contain a window a
door or a chimney in a building. These regions are used to identify and segment higher
order structures in a building, like facade, window array and also environmental regions
like sky and ground.
Because of inconsistency in the unary features of buildings, a contextual learning algorithm
is used to classify the segmented regions. A model which learns spatial and topological
relationships between different objects from a set of hand-labelled data, is used. This
model utilises this information in a MRF to achieve consistent labellings of new scenes
Spatial access to healthcare: exploring the provision of local services
This thesis creates a context for exploring the provision of local healthcare services
quantitatively, with particular focus on the application of spatial analysis and the use of
geographic information systems (GIS). It focuses theoretically on the intersections between:
health and medical geography; GIScience and spatially integrated social science; and social
justice and spatial equity, elucidating the value of space and place in understanding patient
registration with, and usage of, healthcare services.
The practical elements of the thesis are based on patient registration data provided by
Southwark primary care trust (PCT), and Hospital Episode Statistics from the NHS
Information Centre. Focussing initially on primary care, registration with GP surgeries in
Southwark is considered firstly from a normative perspective, and subsequently by
employing a service area delineation approach. Profiling GP surgeries in this way enables an
insight into patient registration behaviours, and sheds light on the challenges of
implementing an agenda of patient choice as advocated by recent NHS white papers. The
perspective of inpatient and outpatient care is also considered, given the increasing import
of joined up provision in primary and secondary care. The thesis considers the linkage
between the two service hierarchies, investigating utilisation of secondary care by patients.
The value of this thesis derives from its relevance to the reform agenda that looks likely to
radically reshape the NHS, the exploitation of patient registration data at individual level,
novel use of classification, and the systematic application of spatial analysis across a range of
scales
Social marketing and public health
The public health field exists to safeguard the general public from health risks by controlling risk factors, classically through immunization programmes that prevent or control epidemics, or through actions such as monitoring the quality of drinking water. In our post-industrialised society, risk factors other than the environment, such as diet, exercise, tobacco and alcohol use, have grown in importance. The policy response to the growing demand upon healthcare services arising from chronic diseases caused by changing lifestyle factors has taking different forms, and these include targeting vulnerable groups using health promoting campaigns.
This thesis addresses some of the challenges and opportunities in public health campaigns and healthcare planning that arise from the growing repositories of data that can be made available for targeting at the individual and small area level in a public health setting.
The first part sets the scene by describing the concepts of health, public health and social marketing. The intention is to pave the way for broader discussions – in the progress of the thesis – about healthcare planning, population health, and social processes in the light of targeted public health interventions.
Part two addresses the problems and possible solutions to a number issues in healthcare planning, starting with studies at the individual, then moving to organisations and ending with area classifications. The thesis draws on a number of case studies for targeting in a public health context including frequent accident and emergency users, teenage users of abortion services, women’s breast screening uptake, GP registration, and the neighbourhood characteristics of chronic disease patients. Finally, part three provides a synopsis of both context (part one), results (part two) and future perspectives on how routinely collected healthcare data can be used to create evidence for the planning of new cost-effective interventions
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