1.Turing-HSBC-ONS Data Science Award 2018

[Awarded Value £132,000; in collaboration with I. Bateman, J. Davidson, P. Wngsaart (Cardiff University), Y. Xia (NUS) and C. Fezzi (Trento)]

"The core of this project is to develop the ability to quantitatively explain the complex interplay between scientific, economic, demographic and statistic factors that are crucial determinants of land-use in the UK. Being able to acquire a better understanding of the processes that drive land-use is essential since land-use is an important factor that determines a wide range of values (e.g. food production, energy production and forestry, water quality and quantity, flooding risk, greenhouse gas emission and storage, recreation and related physical, and mental health) and a number of important environmental concerns (e.g. the long-term decline in biodiversity in the UK). Clearly, the process that determines land-use is exceptionally complex. It depends not only on scientific factors (e.g. climate change), but also on economic determinants (e.g. agricultural price change) and demographic variables (e.g. population growth), to name a few. Furthermore, there are other determinants, which can collectively be described as statistical factors, for example spatial and temporal dependence, and co-movements, (e.g. common trend). The proposed research will conduct an empirical analysis of the interplay between land-use and its important determinants, especially climate change and the agricultural price change, and to construct accurate forecasts of land-use in the UK”.

Please see more information at:

• http://www.exeter.ac.uk/leep/newsandevents/news/articles/economicsdepartmentandleep.php

• https://www.turing.ac.uk/news/turing-hsbc-and-ons-collaborate-economic-data-science-awards

2.Seedcorn Research Funding Data Science, Cardiff Business School, Cardiff University, 2020

[Awarded Value £2,000; in collaboration with P. Wongsaart (City University of London)]

3.British Academy/Leverhulme Small Research Grants [SRG2324\240871], 2024

[Awarded Value £7,700; in collaboration with P. Wongsaart (City University of London) and F. Mascone (Brunel University London)]

"Variations in lockdown strategies versus country-specific effects in explaining spread of Covid19 pandemic: Spatial Functional Data Analysis approach for discrete longitudinal data Academic research and the media alike have reported significant variations in the way that governments around the world responded to the Covid19 pandemic. These variations have created a worldwide debate over different lockdown strategies and their effectiveness in slowing down the spread of the pandemic. In this project, we intend to shed further light on this debate. More specifically, we plan to establish a new cuttingedge analytical method that can help to data-scientifically investigate whether countries that follow similar lockdown strategies encounter the same patterns of spread of the Covid19 pandemic; or there are countryspecific factors that play a more crucial role in explaining the data generating process of daily new Covid19 cases. Moreover, we are also interested in studying whether stringency of lockdown should always be associated with lowering number of Covid19 cases; or there are other country-specific factors that could work to invalidate such a hypothesis. "

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