Building intelligent infrastructure: Prof Goh Kim Huat

The research was supported by the National Research Foundation, Singapore, and the Ministry of National Development, Singapore, under its Cities of Tomorrow R&D Programme.

To construct the framework, Prof Goh and his team partnered with organisations and government agencies to collect data in Singapore, spanning societal, health, attitudinal, economic and environmental dimensions. Examples of data collected include spending patterns, the number of hospitalisations due to different ailments, human traffic volume and sentiments in public feedback to the government. The data ranged from static information to semi-dynamic data and highly dynamic data.

The data was anonymised and aggregated before being analysed and combined into a “data cube” that could be used to detect emerging trends and deviations across different zones in the city.

This tool could alert policymakers drawing up plans for the city to potential outliers or red flags in a city, such as traffic congestion and epidemics. For urban planners, the tool helps them understand the character, vibrancy and activities of the various parts of the city, enabling them to design neighbourhoods that cater to the unique and evolving needs of the residents.

For instance, the tool can help create dementia-friendly neighbourhoods. Alzheimer’s disease is a common form of dementia, and individuals with cardiovascular diseases are more likely to develop the condition. By analysing the correlations between both diseases, the tool can identify subzones where residents face a higher risk of Alzheimer’s disease, along with the environmental, social and economic factors that may exacerbate the development of the condition.

The project’s findings were published in a paper that won the Best Short Paper Award at the 2022 International Conference on Information Systems. Singapore’s public housing agency, the Housing and Development Board, is looking to implement the tool in its town planning.

Prof Goh is also developing algorithms to improve how resources are allocated for community health, and studying the acceptance of artificial intelligence in healthcare.