Four flagships projects are central to the project’s transdisciplinary aims. They all refer geographically to the Canton of Zurich. The flagship projects 1-3 will each be a PhD.
Improving indicators to capture emerging forms of precarious work
This project deals with emerging forms of precarious work (e.g. platform labour, live-in labour). The goal is to identify and qualitatively assess these forms of precarious work and to propose solutions to capture them in existing indicators and taxonomies of work.
An index of multiple deprivation in the Canton of Zurich
This project has the objective to co-create a spatially explicit index of multiple deprivation. Potential indicator domains include income, health, employment, education, lived environment. The project includes calculating indicator weights as well as appropriate spatial geographies.
Developping multimodal indicators of wellbeing in green spaces
The goal of this project is to develop spatially explicit indicators of green spaces with emphasis on societal and environmental well-being. Deep learning will be used to analyse street-view images, aerial photographs and satellite images combined with existing map data. Possible indicator areas are biomass, vegetation density and plant species.
Artistic negotiation of data (processes) and indicators
There are gaps in our knowledge of society based on data and indicators. The project develops explorative art and design practices that open up new perspectives on indicators and their underlying data (processes), appropriating and publicly negotiating their gaps. In conjunction with teaching at the ZHdK and UZH, artist residencies will take place at the Cantonal Statistical Office as well as interventions and exhibitions in social living spaces.
Additionally, there are associated projects to the PDL. They include ongoing PhDs addressing relevant themes including the use of text to measure wellbeing in landscapes, vegetation structure mapping from satellite imagery with deep learning, exploring underemployment and on-demand labour ethnographically or tracing neighbourhood densification with qualitative interviews and visual methodologies.