This session explores the use of non-traditional data sources and advanced data science inspired methods to maximise positive outcomes on the environment, public health and the economy. The increasing amount of data collected on the environment, human mobility, health and how we conduct our daily lives as a society has the potential to dramatically inspire and transform policy design and implementation, particularly in the field of air quality monitoring and its impact on health on the general population. With relatively easy access to utilising near real-time data, including satellite imagery, low-cost air quality monitoring sensors, traffic cameras, human mobility data, we have a tremendous opportunity to predict quantified impacts of proposed policy interventions and thus determine interventions with the greatest outcomes on public health, the environment and the economy. This session will bring together some of the leading experts across the public sector and academic organisations in the UK to share the latest developments and case studies covering a range of policy critical issues on this topic. The session will be of interest to a broad range of audience, including statisticians, data scientists, policy makers and academics.

Air pollution is a destructive global crisis and it remains one of the biggest and most immediate environmental threats to human health, causing significant premature deaths and adverse health outcomes, including cardiovascular and respiratory disease, cancer, neurological effects, and birth outcomes. Human mobility is expected to play a key role in influencing the air quality which in turn is expected to have an impact on the health of the general population. In this session, we aim to bring together case studies from the UK public sector organisations and beyond who have taken innovative approaches to explore and better understand the climate change—human mobility—health nexus. The session will be a series of talks involving non-traditional big data sources and advanced machine learning modelling techniques to understand the complex interplay between climate change, health and mobility and showcase tools to aid policy makers through evidence-based decision making.

The session will be of interest to a broad range of audience as the case studies will tackle some of the major technical and methodological hurdles requiring a consolidation of the advanced statistical and machine learning techniques. Causal inferences can be a real challenge in a data landscape with highly interrelated influences such as mobility, health and air pollution. The session will present ways to tackle this challenge with real world examples.  Uncovering the complex interplay amongst the three chosen influences will be the common theme of the session and since these influences have both spatial and temporal dimensions, the sessions will present methods and analysis developed to unify the spatial-temporal dimensions of the chosen influences. The session will also include recent research on the Health outcomes on the general population after controlling for a range of socio-economic, demographic indicators, mobility patterns and land use patterns to develop a comprehensive understanding of the societal and health impacts of air pollution. Session will also contribute towards developing our understanding into human mobility and urban planning using state of the art machine learning methods. 

This session will be of interest to the policy makers to make informed decisions as the society makes a transition into the post COVID world. The techniques and analysis presented in this session have a potential to be useful to other National Statistical Institutes and academic community and we believe the session can be a useful addition to the scientific programme of the conference particularly in relation to the well-being and welfare of the people.

Organiser: Chaitanya Joshi 

Chair: Myer Glickman

Speaker: Myer Glickman 

Speaker: Chaitanya Joshi 

Speaker: Ying Jin