Spatial and Temporal Assessment of Vulnerability and Resilience in Semi-Arid Landscapes

Food security, particularly in dryer more climate sensitive areas of the world, is becoming increasingly unstable. Greater understanding of human and biological elements of agricultural and environmental systems, particularly in dryland areas, could help mitigate negative impacts of climate change on food security whilst simultaneously addressing issues of environmental degradation. However, biological systems, agricultural systems and the interplay between the two are complex and non-linear and, at present, available methods are not able to fully and simultaneously capture all dynamic and static elements of these integrated systems (Inwood et al., 2018). The aim of this PhD is to provide the means for landscape scale simultaneous assessment of agricultural and environmental systems, and their interplay, through development of a novel methodology and its application to open-source geo-spatial data. The objectives are three-fold: 1) Assess and adapt methodology for understanding the multivariate spatial-temporal interplay between elements of a complex system; 2) Apply the method to real-world data within two active study-sites and assess its effectiveness; 3) Perform 'what-if?' scenarios to identify leavers of change and 'tipping points' within the system. The geographical focus of this research are two study sites, one in Taita-Taveta hills, south-east Kenya, and the second in Yebelo, southern Ethiopia. Methodological efficacy will be established through mathematical proofs as well as testing in a real world environment via ground-truthing in each of the study sites. Ground-truthing will be informed by site visits and over 1000 RHoMIS household surveys collected and analysed for each study site. 'What-if?' scenarios includes changes in environmental factors such as changes in meteorological activity, increases/decreases in wildlife, changes in forest cover etc, as well as human, such as population change and policy interventions. Simultaneous multivariate spatial-temporal systems analysis is an unsolved problem (Chen et al., 2020). Multiple existing models are available within the latent variable, neural network and Bayesian hierarchical fields. A detailed literature review will be undertaken to identify the most likely path to success.

The COMPASS Centre for Doctoral Training will have the following impact.

Doctoral Students Impact.

I1. Recruit and train over 55 students and provide them with a broad and comprehensive education in contemporary Computational Statistics & Data Science, leading to the award of a PhD. The training environment will be built around a set of multilevel cohorts: a variety of group sizes, within and across year cohort activities, within and across disciplinary boundaries with internal and external partners, where statistics and computation are the common focus, but remaining sensitive to disciplinary needs. Our novel doctoral training environment will powerfully impact on students, opening their eyes to not only a range of modern technical benefits and opportunities, but on the power of team-working with people from a range of backgrounds to solve the most important problems of the day. They will learn to apply their skills to achieve impact by collaborative working with internal and external partners, such as via our Rapid Response Teams, Policy Workshops & Statistical Clinics.

I2. As well as advanced training in computational statistics and data science, our students will be impacted by exposure to, and training in, important cognate topics such as ethics, responsible innovation, equality, diversity and inclusion, policy, effective communication and dissemination, enterprise, impact and consultancy skills. It is vital for our students to understand that their training will enable them to have a powerful impact on the wider world, so, e.g., AI algorithms they develop should not be discriminatory, and statistical methodologies should be reproducible, and statistical results accurately and comprehensibly communicated to the general public and policymakers.

I3. The students will gain experience via collaborations with academic partners within the University in cognate disciplines, and a wide range of external industrial & government partners. The students will be impacted by the structured training programmes of the UK Academy of Postgraduate Training in Statistics, the Bristol Doctoral College, the Jean Golding Institute, the Alan Turing Institute and the Heilbronn Institute for Mathematical Sciences, which will be integrated into our programme.

I4. Having received an excellent training, the students will then impact powerfully on the world in their future fruitful careers, spreading excellence.

Impact on our Partners & ourselves.

I5. Direct impacts will be achieved by students engaging with, and working on projects with, our academic partners, with discipline-specific problems arising in engineering, education, medicine, economics, earth sciences, life sciences and geographical sciences, and our external partners Adarga, the Atomic Weapons Establishment, CheckRisk, EDF, GCHQ, GSK, the Office for National Statistics, Sciex, Shell UK, Trainline and the UK Space Agency. The students will demonstrate a wide range of innovation with these partners, will attract engagement from new partners, and often provide attractive future employment matches for students and partners alike.

Wider Societal Impact

I6. COMPASS will greatly benefit the UK by providing over 55 highly trained PhD graduates in an area that is known to be suffering from extreme, well-known, shortages in the people pipeline nationally. COMPASS CDT graduates will be equipped for jobs in sectors of high economic value and national priority, including data science, analytics, pharmaceuticals, security, energy, communications, government, and indeed all research labs that deal with data. Through their training, they will enable these organisations to make well-informed and statistically principled decisions that will allow them to maximise their international competitiveness and contribution to societal well-being. COMPASS will also impact positively on the wider student community, both now and sustainably into the future.