Land use type allocation informed by urban energy performance: a use case for a semantic-web approach to master planning

Cities are growing fast and facing unprecedented challenges as urban populations grow and resources are becoming scarce. A city's master planning involves a series of decision-making processes and requires knowledge from various domains. Urban planners are seeking computational support. We present a use case of land use type or building function allocations informed by urban energy performance as a pilot demonstrator for a semantic-web approach to these challenges. The software used for energy performance assessment was the City Energy Analyst. Using a quarter in downtown Singapore as an example, the results indicated 70% to 80% residential supplemented by other land use types favours efficient use of district cooling systems and photovoltaic panels. Urban planners may use the results to narrow down the search space of land use type ratios for the selected mixed-use area in Singapore. The use case serves as a pilot demonstrator for a broader research scope, the project Cities Knowledge Graph. To support master planning, the project aims to build an extendable plat-form to integrate more datasets and evaluation software for various urban qualities and domains.