Geographical determinants of health
A geographer at heart, Mark's research has also sought to understand the ways in which features of the neighbourhoods we live and interact with daily imprint on our health. This has involved: building the largest and most comprehensive small area database of geographical measures of health - the Access to Health Assets and Hazards - which has been used by Public Health England; exploring the communities who were engaged in asymptomatic COVID-19 testing during the "mass testing" pilot in Liverpool; and examining whether the density of fast food outlets has an effect on body weight and obesity. Research funded by the Drivers of Food Choice programme and the MRC (2016-2019) has identified the extent unhealthy and healthy foods sold and advertised across Ghana and Kenya, and has been used to support strategies to regulate and restrict planning to promote health. The ESRC funded 'Local Data Spaces' project saw Mark co-producing data resources for Local Authorities to help generate evidence to support urgent policy decisions during the COVID-19 pandemic.

Big data, machine learning and health
The growth in availability of data in scope and size has yielded new exciting opportunities for understanding health behaviours, particualrly when combined with maturing data mining approaches. Mark's research has investigated the contribution of new forms of (big) data in this field including: reviewing big data approaches in obesity-related research; examining the contribution of loyalty card records for understanding self-medication behaviours; and using Twitter to examine how misinformation trends reacted to the 2021 UK lockdown. Their 'Understanding Society Biomedical Data Fellowship' (2018) utilised deep learning and decision tree algorithms to evaluate how useful different data types (e.g. biomakers, genetics, social measures) for predicting an individual's risk of future ill health.