*****Closing Date Soon - 31st July 2015 17:00:00 BST.*****
The Consumer Data Research Centre has an ESRC funded PhD award
available to begin September 2015 based at the University of
Liverpool. Additionally, and if necessary, the successful candidate
may also be required to complete the MSc in Geographic Data Science,
which is also fully funded.
Project description
This project takes a data-intensive approach to expand knowledge about
urban areas and their building blocks: neighbourhoods. Cities are
probably the single most powerful human invention, yet, we have only
limited understanding of how they work and evolve at both micro and
macro time scales.
Although very broad and sometimes abstract, the concept of
neighbourhood has been instrumental in Social Science to assist our
understanding of urban structures and processes. In recent years,
however, the explosion of datasets relating to consumption in cities
presents urban researchers with an unprecedented opportunity to
define, identify and study neighbourhoods and their evolution in new
quantitative ways.
This project is based within the ESRC-funded Consumer Data Research
Centre (CDRC) at the University of Liverpool, providing both an
opportunity to engage with industry on real-world projects, and
collaborate with a large group of researchers at UCL, University of
Oxford and the University of Leeds.
Complementing the use of unique data sources provided by the CDRC,
will be a methodological emphasis on geocomputation approaches. In
particular, techniques from the fields of spatial analysis, spatial
econometrics and machine learning may be combined. It is envisioned
that the project will produce advances in some of these areas to
customize methodologies to the particular domain question and to the
unique nature of the data used.
The project is divided in the following main work packages:
Bias: get a clear sense of the nature and extent of consumer data, in
comparison to official sources such as the Census.
Neighbourhood identification: methodological exploration of how to
delineate spatial partitions using multiple sources of data, with
potentially different characteristics (points, polygons, etc.).
Comparison of multiple delineations of neighbourhood boundaries:
methodologies to explore differences between partitions, both
statistically and visually.
Methods to track neighbourhood changes over time: techniques to
quantify and assess changes and differences between multiple
partitions.
Large-scale application at the national level: what is the geography
of consumer neighbourhoods in the UK? What does it tell us about
cities and their differences?
Policy recommendations: how can these insights be operationalised by
the public and private sector?
Requirements
A successful candidate will have a 1st class / high upper 2nd class
undergraduate degree or an MSc with Merit or Distinction in a
quantitative social science discipline.
Funding
3 year (or 3+1) ESRC North West Doctoral Training Centre (DTC)
Research Studentships; Studentships include an annual tax-free
maintenance stipend at the standard ESRC rate* (for 2015/16, this is
£14,057 for full-time students - tax free). In addition, the
studentship will pay for all tuition fees. Research Students will have
access to central funds to cover research related expenses,fieldwork,
collaborations and overseas institutional visits. Funding is open to
UK / EU students only.
How to Apply
To apply, please submit a CV and supporting letter of application to
Professor Alex Singleton [log in to unmask], demonstrating
how you feel your skills and experience are relevant to the CDRC.
More Info: http://geographicdatascience.com/job/2015/07/13/PhD-Studentship/
--
Alex Singleton
Professor of Geographic Information Science
Department of Geography and Planning
University of Liverpool
Email: [log in to unmask]
Tel: 0151 7942860
Blog (http://www.alex-singleton.com)
Geographic Data Science Lab (http://geographicdatascience.com)
ESRC CDRC (http://cdrc.ac.uk/)
Twitter: alexsingleton
Skype: alexsingleton
You may leave the list at any time by sending the command
SIGNOFF allstat
to [log in to unmask], leaving the subject line blank.
|