Data-Enabled Decision Support System for Sustainable Urban Development: A Case of Urban Green Space Management

Cities, considered important centers of socio-economic activity and growth, currently confront adverse impacts from climate change. Efforts have been made to leverage modern technologies to make cities smarter. However, implementing them for the efficient management of the urban environment can help cities simultaneously grow smart and sustainable. Urban green spaces (UGS) offer substantial social, environmental, and economic benefits, making their preservation and enhancement a key priority for city administrators. Nevertheless, with changing climatic conditions and intensifying resource constraint conditions, such as water scarcity, labor shortage, and limited funding, decision-makers need to prioritize the allocation of limited resources. However, decision-makers struggle to find the right balance between conflicting objectives and subsequently make informed decisions. Therefore, to achieve effective UGS management, a multidimensional, evidence-based approach is necessary to balance diverse objectives. Accordingly, this thesis, through four interconnected studies, presents a data-enabled decision support system based on utilitarian principles that aims to maximize UGS benefits at minimum costs. ... mehrThis is achieved by systematically incorporating both the total costs of sustaining UGS and the benefits they provide to city residents.

The first study provides the initial component of the decision support system by estimating the management demands associated with sustaining UGS, also referred to as costs. In this, a novel linear time series model, based on soil water balance principles and the Water Use Classifications of Landscape Species approach, was developed. The model provides estimates for the weekly irrigation demand of urban street trees, considering tree characteristics, current and forecasted weather conditions, and soil properties. The second study further adds to the decision support system by estimating the attainable benefits from UGS. Through a novel GIS-based approach, accessibility and quality benefits are calculated using publicly available data on UGS spatial distribution, space size, noise map, remote sensing data, crime statistics, and population distribution in the city. Building upon the foundation laid by the initial two studies, the third study develops a Goal programming-based decision-making model that integrates multiple objectives, including conserving stored carbon, increasing attainable quality and accessibility, and addressing constraints on available resources such as water and personnel. The developed model allows making prioritization decisions to allocate resources efficiently and maximize benefits gained. The final study addresses a critical aspect of data-enabled system: assessing and enhancing the input data quality. By developing a comprehensive quality assessment framework based on total data quality management principles and implementing data filling techniques, the framework improves the reliability and utility of digital tree inventories, thereby enabling informed decision-making in cities with limited data availability.

The findings demonstrate that the proposed decision support system can improve the attainable benefits from UGS in case of resource-constraint scenarios. It presents three prioritization approaches: based on demands, based on benefits, and then by integrating both the demands and benefits. Moreover, through novel approaches developed to quantify social benefits, such as accessibility and quality, and key management inputs like irrigation demand, it showcases the wide applications of available public datasets for urban resource management applications. Also, the significance of data quality and challenges related to low-quality public datasets are addressed through a systematic quality assessment framework. Therefore, it has the potential to significantly contribute to supporting decision-makers in making informed choices for managing UGS.