Data-Driven Public Health and Urban Sustainability
The Covid-19 pandemic and its consequences have promoted rethinking of questions of public health, urban sustainability, and urban planning. Understanding the relationship among socio-spatial, environmental and cultural factors is essential to guide urban sustainability and planning, including to prevent future pandemics or other health issues. Uncovering unexpected patterns of relationship is comprehensively study. It demands digitalising and processing data on socio-cultural and environmental factors, and requires multi-disciplinary collaboration. Also, data needed for such analysis often comes from multiple heterogenous sources, calling for data integration or data fusion workflows. Additionally, such datasets tend to be very big and multi-dimensional, making manual statistical analysis hard or infeasible. Here, data science and artificial intelligence methods can provide new approaches to process such high-dimensional multi-source data to uncover yet unknown patterns. To inform future actions, results achieved with such methods need to be understandable, verifiable, and explainable.
We launched this collection to promote communication and collaboration among scholars from multiple disciplines using novel methodologies, including data science and AI, to study data from a large variety of socio-cultural, public health and environmental factors in order to inform public health management, urban planning, and urban sustainability management themes.
Guest editors
Dr. Lixia Chu(Wageningen University & Research)
Dr. Christoph Lofi(Delft University of Technology)
Prof. Dr. Carola Hein(Delft University of Technology)