[Geo4All] Closes tomorrow - Fully funded PhD opportunity - Advancing emotional geographies using artificial intelligence

[De Sabbata, Stefano (Dr.)]

Dear all,

Apologies for cross-posting.

Please see the fully funded PhD opportunity in deep learning approaches in human geography below. Applications close tomorrow Friday March 11th.

Apply here: https://le.ac.uk/study/research-degrees/funded-opportunities/future-100-phd-cse

About the Project

Project Highlights:

  1.  To develop a ground-breaking, interdisciplinary approach to exploring the emotional relationship between people and place
  2.  To harness the capabilities of recent advances in deep learning to conduct multimodal textual and visual analysis including street-view imagery
  3.  To develop a predictive model capable of estimating how places are experienced.

Project Overview

The project aims to better understand people's emotional connections with places by implementing and testing geospatial, multimodal text and image processing models, expanding previous work developed for social media analysis to analyse virtual walking interview data we generated through the Mapping Multiculture project.

In a previous work (Liu and De Sabbata, 2021), we demonstrated how multimodal autoncoders and graph-convolutional neural networks can effectively spatially analyse text and images from social media. We illustrated how to generate models that combine qualitative content analysis with large-scale quantitative analysis to predict specialised and project-specific topics in social media content. In the Mapping Multiculture project (Bennett et al., forthcoming), we have illustrated the effectiveness of street-view imagery in an immersive virtual reality environment as a stimulus in the qualitative interview process and generated a rich dataset of personal experiences of Leicester. That opened up opportunities to explore the emotional connections between people and place, identifying distinct emotional 'atmospheres' associated with different parts of the city. The key idea behind the project here proposed is to apply approaches developed in the first project to the data and context of the second project.

Successful completion of this project will lead to significant advances in geospatial artificial intelligence, thus generating a better understanding of where tensions (and easiness) are experienced, especially in places with particularly dynamic, diverse populations such as Leicester. Exploring people's emotional reactions to places is crucial to further our understanding of their experiences of heritage and culture in a digital age (WP1). However, we need geospatial, multimodal text and image processing tools to effectively link interview transcripts and street-view photography from interviews, along with relevant big data, to identify people's emotional reactions to places (WP2). Finally, we need to explore the generalisability and predictive capabilities of the developed models to critically assess the practical and ethical implications of their use (WP3).

The results from these studies will help us further our understanding of the relationship between place and emotion and the role that geospatial artificial intelligence can take in conducting digital geographical research on these topics at scale.

Methodology

We will conduct three Work Packages (WP) in parallel, which will feedback one another.


  *   WP1: Content Analysis. Qualitatively analyse data generated during the Mapping Multiculture project through virtual walking interviews using Google Earth VR to assess emotional connection with places. Collect street-view imagery. Identify gaps, data imbalances and opportunities for further interviews to support WP2 and WP3.
  *   WP2: Modelling. Explore supervised and semi-supervised machine learning approaches such as multimodal autoncoders and graph-convolutional neural networks to develop geospatial, multimodal text and image processing tools that effectively link interview transcripts and street-view photography from interviews (WP1) to identify people's emotional reaction to places.
  *   WP3: Evaluation and critical assessment. Apply the developed models (WP2) in new scenarios to test their generalisability and predictive capabilities. Explore the use of big data from user-generated content platforms (e.g., social media, Wikipedia) as supplementary data for generalisation. Critically assess the practical and ethical implications of their use.

Entry Requirements:

Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject or overseas equivalent.  The University of Leicester English language<https://www.findaphd.com/common/clickCount.aspx?theid=141763&type=184&DID=4567&url=https%3a%2f%2fle.ac.uk%2fstudy%2fresearch-degrees%2fentry-reqs%2feng-lang-reqs> requirements may apply

How To Apply

Please refer to our How to Apply information at
https://le.ac.uk/study/research-degrees/funded-opportunities/future-100-phd-cse<https://www.findaphd.com/common/clickCount.aspx?theid=141763&type=184&DID=4567&url=https%3a%2f%2fle.ac.uk%2fstudy%2fresearch-degrees%2ffunded-opportunities%2ffuture-100-phd-cse>
With your application, please include:

  *   CV
  *   Personal statement explaining, briefly, your interest in the project and your experience ( If you apply for two projects include a statement for each project on the same document)
  *   Degree Certificates and Transcripts of study already completed and if possible transcript to date of study currently being undertaken
  *   Evidence of English language proficiency, if applicable
  *   In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available.
You can apply for a maximum of 2 projects.
For each project you want to be considered for:

  *   In the Supervisor Section: Enter the Project Reference for each project you want to be considered for (the Project Reference is on the project listing above and on the project description document)
  *   In the Project Title Section: Enter the Project Title for each project in order of priority (e.g. Project 1, Project 2)
In the Funding Section: Enter Future 100 Scholarship or select Future 100 Scholarship from the drop down menu.

Funding Notes
Future 100 Scholarships provide funding for 3.5 years to include:
• Tuition fees at UK rate
• Stipend at UKRI rates (currently £15,609. 2022 rates to be confirmed)
• Access to a Research Training Support Grant of up to £1,500 pa for 3 years.
• Bench fees of £5,000 per annum for three years for laboratory-based studies
International students will need to be able to fund the difference between UK and International fees for the duration of study.

References
Ballatore, A., & De Sabbata, S. (2020). Los Angeles as a digital place: The geographies of user‐generated content. Transactions in GIS, 24(4), 880-902.
Bennett K, Gardner Z and De Sabbata (forthcoming) Digital geographies of everyday multiculturalism: ‘Let’s go Nando’s!’ Social and Cultural Geography
De Sabbata, S., & Liu, P. (2019). Deep learning geodemographics with autoencoders and geographic convolution. In Proceedings of the 22nd AGILE conference on Geographic Information Science, Limassol, Greece.
Liu, P., & De Sabbata, S. (2021). A graph-based semi-supervised approach to classification learning in digital geographies. Computers, Environment and Urban Systems, 86, 101583.
Neal S, Bennett K, Cochrane A and Mohan G (2018) The Lived Experiences of Multiculture: the new spatial and social relations of diversity, Routledge.

All the best,
Stef.


“It’s all going to be alright in the end, and if it's not alright, it's not the end”
(by Ol Parker, via Wittertainment)

Dr Stefano De Sabbata (they/them)
Lecturer in Quantitative Geography
School of Geography, Geology and the Environment,
University of Leicester, University Road, Leicester, LE1 7RH, UK
Research Associate at the Oxford Internet Institute, University of Oxford
t: +44 (0)116 252 3812
e: s.desabbata at le.ac.uk<mailto:s.desabbata at le.ac.uk>
w: sdesabbata.github.io<https://sdesabbata.github.io/>
w: le.ac.uk/departments/geography/people/stefano-de-sabbata<http://www2.le.ac.uk/departments/geography/people/stefano-de-sabbata>
w: oii.ox.ac.uk/people/stefano-de-sabbata<https://www.oii.ox.ac.uk/people/stefano-de-sabbata/>
twitter: @maps4thought<https://twitter.com/maps4thought>

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