Global biodiversity loss is recognized as a key threat to humanity. Cities, despite having high concentration of anthropogenic influences, are recognized to play a key role to avert biodiversity loss. But how should cities be planned and designed to support biodiversity conservation? While numerous studies have shown that large green spaces are critical in this role, emerging evidence suggests that beyond just the amount of green space, the patterns of green spaces, as well as the build urban fabric, also influence biodiversity abundance and richness. This evidence is fragmented and generalization is still not possible. This study uses a comparative approach across a large number of cities to develop a better understanding of how urban patterns can be optimized to improve urban biodiversity, with a focus on birds as the species of interest.
We followed a four-step approach. Firstly, we gathered and processed biodiversity data from open sources, ensuring its reliability and consistency. Next, we selected and determined the relevant urban pattern metrics using multi-source data. Subsequently, we conducted the statistical analysis and modelling to explore the relationships between avian biodiversity patterns within and among cities. Lastly, based on our findings, we developed generalizations and formulated recommendations to support biodiversity conservation efforts.
Urban greenspaces have been associated with numerous human health benefits through pathways of reducing environmental harms, facilitating physical activities and social cohesion, and recovering psychophysiological stress. Despite these known connections, the impact of greenspace patterns on human health remains relatively unexplored, extending beyond mere quantity measures (e.g., coverage) of greenspaces.
Health outcomes encompass different causes of mortality observed at the individual level within the Swiss National Cohort. We derive a series of indicators to quantify greenspace patterns using high-resolution remote sensing data. We employ Cox proportional hazard regression models to explore the associations between various pattern indicators and different causes of mortality. Our analyses also account for confounding factors, including age, sex, education level, income level, air quality and temperature.
Local economic development helps communities improve the quality of life, create new economic opportunities, and combat poverty. Small businesses are vital elements of the local economy. According to World Bank statistics, they represent about 90% of businesses and account for more than 50% of employment worldwide. Understanding where small businesses can thrive in cities is crucial for formulating effective policy regulations and urban development plans.
We utilize a causal inference model to explicitly model and validate causal assumptions regarding the influence of urban form and location patterns on the density and diversity of small businesses. Our focus encompasses multiple layers of urban form, including streets, plots, buildings, and green spaces, which collectively shape socio-economic urban processes such as traffic flows, pedestrian movements, and space availability.
With the ongoing effects of climate change, cities worldwide are witnessing more intense rainfall and frequent flooding events. Moreover, the growing global population induces a continuous expansion of urban areas, which, in turn, plays a crucial role in altering the permeability of land cover, further enhancing the occurrence of flood hazards in many cities. However, the relationship between urban morphology and pluvial flooding has received limited attention. The research aims to quantitatively assess the influence of urban morphology on the spatial and temporal distribution of pluvial floodwater. The findings from this study can offer valuable guidelines for enhancing flood resilience in urban planning.
Using a cellular automata-based model, we conduct simulations to analyse the distribution of pluvial floods in a large number of urban catchments characterised by a broad range of urban morphologies. These simulations represent the response of urban catchments to different scenarios of rainfall return periods and drainage efficiency. Subsequently, a series of machine learning methods are applied to uncover hidden relationships between urban morphologies and space-time distribution of pluvial floodwater.
Cities experience higher temperatures than their rural surroundings, a phenomenon known as the urban heat island. Such excessive heat in cities can impact human health, ecology, and building energy consumption. An increase in urban vegetation is an attractive urban heat mitigation strategy as vegetation can alter the local outdoor climate by modifying temperature and humidity, shade provision, and wind speed. Such changes in the local outdoor climate can influence building energy consumption for cooling, dehumidification, and heating.
We combine urban climate, ecohydrological and simple building energy modelling to quantify effects of vegetation type and pattern in combination with urban density on the local outdoor and indoor climate and the building energy demand for cooling, heating, and dehumidification across world cities.
Infrastructures and Ecologies / [CEC] Comparative Ecology of Cities
Principal Investigators: Prof. Dr Tan Puay Yok, Prof. Dr Paolo Burlando
Co-Investigators: Prof. Dr Simone Fatichi, Assoc. Prof. Dr Zhang Ye, Assoc. Prof. Hwang Yun Hye, Dr Daniel Richards
Collaborators: Dr Amy Hahs, Dr Brenda Lin, Asst. Prof. Dr Gabriele Manoli, Assoc. Prof Dr Mark McDonnell, Prof. Dr Jun Yang
Researchers: Dr Dengkai Chi, Dr Naika Meili, Dr Yue Zhu, Yeshan Qiu, Jing Wang