Event Details

DSNE seminar with Simon Willcock (Bangor University)

There is widening awareness that global challenges (such as climate change, biodiversity loss and widening inequality) urgently need to be addressed or we may be faced with a dramatic collapse of some social-ecological systems. For example, 14 of the 18 categories of nature’s contributions to people (ecosystem services; ES) show a decline, so we face the challenge of how to reduce this environmental demand whilst, at the same time, increasing the wellbeing of the world’s poorest people. However, the scale of global challenges and the solutions required to address them (e.g. the SDGs) presents fundamental difficulties that need to be overcome. One of the most substantial challenges hindering our understanding of the interactions between people and nature is that data on social systems are not collected in a comparable manner to natural systems data. Within natural science, the development of sensor technologies (ranging from site-specific moisture and flow sensors up to remote satellite-based sensors) brought forth unprecedented levels of data availability, providing standardised hourly/daily/weekly data at high spatial resolution (e.g. metres, kilometres)and across vast spatial extents (often globally). However, social science did not experience this step change and so now lags behind in its ability to capture data at high spatial-temporal resolution and at global scales. For example, while social data collection is often at regular time intervals (e.g.,annual) and (at best) geographically representative, the expense and logistic challenge of these efforts precludes data collection at the frequency necessary to capture the socioeconomic drivers or responses to environmental disturbances at scales relevant to current global challenges. This fundamentally limits our ability to achieve key SDGs such as food security and reducing the impacts of climate change. In this presentation, I will discuss smartphone methods and analyses needed collect social data at high frequencies and over large spatial scales.

Link to join via Zoom: https://us02web.zoom.us/j/85434124002?pwd=SS8xcEd3bXBPT1kwYTFaV0lBYUxvUT09