If you're a UK/EU student interested in cities, Big Data and
geocomputation, please consider applying for the following two
studentships, created under the umbrella of the DREAM NERC CDT
(http://www.dream-cdt.ac.uk). Please note the deadline is tight so, in
case you're interested in applying, do get in touch quickly. Best
regards,
]d[
Geo-hazards, climate risk and the spatial economics of cities:
quantifying the economic vulnerability and resilience of cities in
developing countries
Geo-hazards, climate risk and the spatial economics of cities:
quantifying the economic vulnerability and resilience of cities in
developing countries
Many of the great challenges of the 21st Century are closely connected
to cities and the risks associated with a changing climate and a range
of geo-hazards from extreme weather such as typhoons and floods to
volcanic eruptions, earthquakes and rising sea levels. As the process of
urbanization leads an even larger percentage of the population
inhabiting urban areas and the continued agglomeration of economic
activity and firms in cities, obtaining an understanding of how cities
work and how they are able to adapt to current and future environmental
and geo-hazard risk is crucial. Insight into what makes cities
successful places for innovation and economic development in a riskier
world is not only relevant for scientific purposes but also has clear
policy implications: good interventions can only be designed based on
detailed knowledge of the underlying economic, geographical and social
mechanisms.
This project asks fundamental questions that underlie the success of
cities and the vulnerability and resilience of firms, households and
countries. What are the main forces behind the benefits of urban
density? Does spatial structure play a role in the economic outcomes of
cities? What are the economic implications of different spatial
configurations of economic activity?
How should cities be designed to withstand changes in climate, pollution
and extreme weather events? How does urbanization effect energy
efficiency? This project will take an empirical approach and make use of
modern tools of quantitative geography and applied economics to obtain
exogenous sources of variation that allow causal interpretations:
quasi-natural experiments, propensity score matching, (spatial)
differences in differences, or advanced spatial statistics and
econometrics.
The candidate will be required to master machine learning and other big
data techniques as part of the project. The analysis will rely on large
geo-referenced (micro-) datasets including climate data, remote sensing,
satellite data and climate change prediction models to test recent
theories related to cities and their spatial dimension. Although the
area of interest is flexible, special relevance will be given to
candidates interested in developing countries such as China and East
Asia.
About you: A successful candidate will have a degree in quantitative
economic geography, economics or environmental science or related field.
A background in quantitative methods, particularly in econometrics and
spatial analysis is welcome. Programming skills (R, Python and STATA),
or (geo-) database experience are not required but the candidate should
have an interest in learning them throughout the project.
For further details: Please contact Dr Daniel Arribas-Bel:
Email: [log in to unmask]
Telephone: +44 (0) 121 414 8306
Supervisory panel: Birmingham Dr Daniel Arribas-Bel; Professor Robert
James Ross Elliot
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Development of high-resolution spatio-temporal maps of air pollution in
West Midlands as a tool to identify sources of risk and impacts
associated with air pollution
Development of high-resolution spatio-temporal maps of air pollution in
West Midlands as a tool to identify sources of risk and impacts
associated with air pollution Exposure to air pollution affects
cardiovascular, respiratory and cognitive performance diseases. These
diseases contribute the greatest public health burden to society.
Therefore, in order to implement appropriate and effective measures to
reduce risks associated with exposure and its burden of disease it is
necessary to have detailed knowledge of how and to what extent exposure
to air pollution affects these health outcomes.
This project aims at developing and validating models that will account
for the spatial and temporal variability of two air pollutants,
particulate matter and black carbon, across Birmingham. Two approaches
will be undertaken, the more established land use regression models and
the recent advancements in machine learning techniques. Concentrations
of pollutants, will be combined with land use, meteorology, traffic
density and emissions data. Two types of models will be constructed, one
static model capturing the spatial variability of the pollutants; and
one dynamic model capturing the spatial-temporal variability of
pollutants.
The static model will be used to assess relationships between air
pollution and cognitive performance. The dynamic model will be used to
assess the relationships between air exposure and ambulance call-out
rates for cardiovascular and respiratory emergencies.
The outcomes of this project will be useful for environmental health
policy makers to define policies aimed at reducing exposure. Results
will be also of interest to health emergency services. The dynamic
models would be beneficial to the local council as a first step to
develop a traffic management system. It will benefit environmental
health researchers providing further knowledge on the chronic and acute
health effects of air pollution on cognitive performance and
cardio-respiratory incidents, respectively.
The successful candidate will benefit by the structure of the DREAM
Centre for Doctoral Training with the possibility to choose from a wide
range of relevant modules offered by the four participating
universities. Opportunities of training in high performance computing
are ‘Programming for Big Data’ and ‘Machine Learning’. In regards to
informatics, modules on ‘Applied Environmental Informatics’, ‘Spatial
Data Management’ and ‘Time Series Analysis’ will be beneficial. Training
on numerical analysis can be supported by modules such as ‘Statistics
for Big Data’ and ‘Causal Inference in Quantitative Social Research’.
The risk analysis element can be covered by modules such as
‘Environmental Policy & Risk Governance’ and ‘Environmental Risks –
Hazard Assessment & Management’. Finally, training on environmental
analysis will be provided by modules such as ‘Atmospheric Observations’,
‘Econometrics for Environmental Valuation’ and ‘Practical Epidemiology &
Statistics’.
About you: Applicants should hold a minimum of a UK Honours Degree at
2:1 level or equivalent in relevant modules such as mathematics and
physics.
For further details: Please contact Dr Juana Maria Delgado-Saborit:
Email: [log in to unmask]
Telephone: +44 (0)121 41 45427
Supervisory panel: Birmingham Dr Juana Maria Delgado-Saborit; Dr Daniel
Arribas-Bel; Dr Emmanouil Tranos Industrial partners Public Health
England; Birmingham City Council
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Daniel Arribas-Bel, PhD.
Url: darribas.org
Mail: [log in to unmask]
Lecturer in Human Geography
School of Geography, Earth and Environmental Sciences
University of Birmingham (UK)
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