Recent advances in computation and urban dataset availability (big data, machine learning, BIM) are emerging means for advancing sustainable design. However, computational sustainability is short in frameworks and application methods for urban landscapes. This research aims to identify areas where computation could advance landscape design by developing a framework for sustainability evaluation. The study combined mixed methods to identify socio-ecological key performance indicators (KPIs) and landscape-design intervention approaches. These methods include a systematic review, survey among industry professionals, and statistic analysis of project evaluation datasets. The findings supported developing a KPI-based evaluation method for early design phases and testing it on neighborhood-development case studies in Israel. The results demonstrate how spatial KPIs enrich the parametric vocabulary of urban landscapes and advance sustainability benchmarking of designs.

Thesis supervisors: Prof. Yasha J. Grobman, Prof. Pnina Plaut.