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  Fully-funded EPSRC PhD studentship available in Lancaster Data Science & AI Institute: Novel geospatial methods for combining data at multiple spatial and temporal scales in the context of historical disease mapping


   Lancaster Environment Centre

  Dr D Whyatt, Dr Emanuele Giorgi, Dr Patricia Murrieta-Flores  Friday, April 19, 2024  Funded PhD Project (Students Worldwide)

About the Project

With the advent of advanced technology, a vast amount of data has become available, making the integration of information across spatial and temporal scales increasingly crucial for understanding complex systems and making informed decisions. For instance, in environmental sciences, researchers often seek to understand the impact of climate change on ecosystems and biodiversity. To achieve this, data must be integrated from various spatial scales, ranging from local habitats to regional landscapes, and temporal scales, spanning decades or even centuries. Similarly, in epidemiology, the spread of infectious diseases is influenced by factors operating at different spatial and temporal scales. For example, the transmission of vector-borne diseases like malaria or dengue fever depends not only on local environmental conditions but also on global climate patterns and human mobility.

However, existing geospatial methods often fail to effectively account for the misalignment in time and/or space of data from multiple sources, with an increasing need of novel methods that are tailored to the characteristics and complexities of the data being integrated.

This research project aims to develop a novel geospatial modelling framework to address these challenges and improve the integration of data across different spatial and temporal scales in the context of historical disease mapping. This framework will then be applied to mapping, analysing, and interpreting sixteenth-century primary sources on diseases and epidemics, utilizing datasets and methods from the ESRC project Digging into Early Colonial Mexico. Epidemics, like the recent COVID-19, have had profound and enduring consequences throughout history. The application area of this project will be in the context of the introduction of diseases, like smallpox, during the Conquest of America that led to catastrophic declines in populations, with mortality rates reaching 97% in some regions. Traditional research methods struggle with the sheer volume, incompleteness, and uncertainty of historical data. In this project we will leverage advancements in machine learning, corpus linguistics, and geographic information sciences presents opportunities to explore vast historical collections and better address data variability and uncertainty. Specifically, the student will:

1.     Develop geospatial methods to extract and analyse and geographic information from ambiguous and uncertain descriptions of health and disease within textual records.

2.     Develop and apply spatio-methods capable that enable the identification of clusters of disease using uncertain data from historical records.

The successful applicant will be supervised by an interdisciplinary team consisting of experts from history, linguistics, statistics and geography.

Prospective candidates should have a first or upper second-class honours degree, or a combination of qualifications or experience equivalent to that level in a relevant subject.

For informal enquiries about the project please contact Duncan Whyatt (), Patricia Murrieta-Flores () or Emanuele Giorgi ().

To apply, please send a CV and cover letter demonstrating your motivation for the post to . The closing date for applications is 19th April 2024 and we anticipate a start date of October 2024 for the successful candidate. 

Computer Science (8) Environmental Sciences (13) Geography (17) History & Archaeology (19)

Register your interest for this project