International Summer School: Urban Data Science

Students will explore rainfall models and simulations, focusing on urban hydrology and extreme events. The curriculum covers hydrological time series characterization, rainfall phenomena observations, and the application of various models at different time scales, including linear parametric models and disaggregation multiplicative cascade methods. Emphasis is placed on the definition of extreme events, utilizing common instrumentation and innovative techniques for rainfall observations. The course introduces statistical notions and provides hands-on experience with R code for monthly scale rainfall simulation using linear parametric models. Students will also understand the importance of sub-hourly rainfall simulation, highlighting the necessity for a robust and user-friendly approach, incorporating COSMOS and the multifractal disaggregation method. Overall, the module equips students with a comprehensive understanding of urban hydrology and practical skills in rainfall modeling.

Sustainable urban metabolism refers to the efficient and balanced management of resources, energy, and waste, within urban environments to promote long-term ecological viability, social well-being, and economic prosperity. It involves understanding, analyzing, and optimizing the flows of materials and energy in cities to minimize negative environmental impacts and enhance resilience.

This workshop will enable students to understand the intricate dynamics of urban metabolism, providing valuable insights into resource flows pathways through the economy, its environmental impacts, and sustainable development strategies essential for fostering resilient and thriving cities.

Starting from the application of urban metabolism concepts to nations, followed by a narrowing down to cities, two types of spatial resolutions will be considered (aggregate versus neighborhood scale) to evaluate alternative strategies towards climate neutral cities.

Urban areas exhibit higher temperatures than the surrounding areas because of their positive thermal balance. Global climate change is synergistically affecting urban temperatures, increasing the magnitude of overheating. About 13,000 cities are exhibiting overheating problems, measured as up to 10.0 °C, and more than 1.7 billion people are living under severe overheating conditions.

Projections about future urban climatic conditions have shown that minimum and maximum temperatures in urban areas may increase substantially due to changes in urban form, urban functions and urban fabric on the one hand and climate change impacts on the other (for instance, heat waves are expected to increase in frequency, duration and intensity).

Urban overheating increases air pollution (ground ozone concentration) as well as the cooling energy consumption of buildings and the peak electricity demand, resulting also in higher emissions of greenhouse gasses; on the other hand it decreases human productivity and leads to surges in heat-related mortality and morbidity while also intensifying mental health problems. 

This module will enable students to understand the main mechanisms that control the urban thermal environment, to understand how urban form, urban functions and urban fabric influence urban heat, to  realize the synergistic impact of climate change to the state of the urban thermal environment, to assess urban heat at varying temporal and spatial scales, to produce composite urban heat related indicators (for instance the urban heat risk) and to learn and explore urban heat mitigation measures. Ground and satellite data will be used, whereas machine learning will be exploited in view of big data analysis.