This is the Materials repository for the 2019 Centre for Advanced Spatial Analysis' (CASA) Doctoral Summer School for Advanced Spatial Modelling.
The Summer School ran from the 21st to the 23rd of August 2019 with 30 PhD students taking part. Programme for the summer school consisted of 12 30-minute tutorials and a hackathon where participants worked in teams to apply the methods covered in the tutorials to research questions relating to the UK's 2017 Industrial Strategy.
This repository contains teaching and administration materials for the summer school as well as the teams' projects, submitted as R markdown notebooks.The repository is organised as follows:
The R markdown notebook and accompanying html for the three netoworks tutorials delivered by Obi Thompson Sargoni, Dr Elsa Arcaute (CASA) and Dr Neave O'Cleary (Oxford Mathematical Institute)
The R markdown notebook and accompanying html for the three remote sensing tutorials delivered by Matthew Ng, Dr Maxim Chernetskiy (Institute of Geosciences, Mineralogy, FSU Jena), and Dr Andy MacLachlan (CASA)
The R markdown notebook and accompanying html for the three statistics tutorials delivered by Zara Shabrina, Dr Georg Hanh (Biostatistics Department of the T.H. Chan School of Public Health, Harvard University), and Dr Thomas Oléron Evans (CASA)
The R markdown notebook and accompanying html for the three spatial statistics tutorials delivered by Bonnie Buyuklieva, Dr Robin Lovelace (Institute for Transport Studies, University of Leeds), and Dr David Murrell (Research Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research, UCL)
The R markdown and html presentations submitted by the Hackathon teams. The summer school attendees worked in teams to apply the methods covered in the summer school and that attendees use in their research to address research questions related to the Grand Challenges laid out in the UK 2017 Industrial Strategy.
Within this folder are separate folders containing the submissions from each team. In addition to the R markdown and html files that were rpesented by the teams are code, data, and project files used and produced by the teams. An extract of the teams' projects is given below:
Let’s try “to ensure that people can enjoy at least five extra healthy, independent years of life by 2035, whilst narrowing the gap between the experience of the richest and poorest”.
To do this, we could start by finding out what is the relationship between the difference between healthspan and lifespan and socio-spatial conditions. What variables associate with a life that is both healthy and long, or at the very least healthy for its duration? With a better understanding of these, we can allign policies to suit the needs of an ageing society.
Using Child Obesity data (10-11 years) as representation of chronic disease, can we model which deprivation factors are prevalent per area in order to inform the government of areas of efficient investment, as well as the public on how to facilitate healthy lifestyle choices?
As part of the industrial Strategy - Clean Growth Grand Challenge there has been a call for the construction industry to reduce whole life cost for assets by 33%, greenhouse gas emission by 50% to drive the UK construction industry as a global leader in clean growth.
Mission: “Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030”
We aim to address this by…
- Investigating the spatial distribution of obesity in England
- Evaluating the importance of green spaces and sports facilities
- Assessing the significance of other potential explanatory variables
This exercise uses data on the Index of Deprivation to identify the areas of potential need for transport infrastructure networks expansion. This dataset, is compared to existing transport nodes, and car ownerwhip rates and origin-destination datasets. It shows potential hotspots where investment in public transport should be prioritised. Before talking of technological advancement in the automotive industry, it is important to ensure mobility is accessible and affordable for all.
During summer or winter, the more extreme temperatures cause people to use additional energy to either cool down or warm up their properties.
Low energy efficiency during such times would mean a lot of wasted energy to achieve that.
Identify regions more affected by the extreme temperatures that also have low energy efficiency/high energy consumption.
For such regions:
- What is the make-up of the properties?
- Are they mostly old dwellings, are they recently refurbished/insulated etc.?
- How much room is there to achieve the potential energy efficiency (does it correlate with dwelling age)?
Once these regions are identified and have enough potential for improvements:
- What could be the average costs of improving the energy efficiency of the dwellings within these regions ?
- Whether they could reasonably be covered by homeowners and public funding needs to be provided?