Development of metric system to measure an innovation district

• Main Author: Poh, Jin An
• Other Authors: Heng Kok Hui, John Gerard
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/168288
• Institution: Nanyang Technological University

The rise of innovation districts around the world has accelerated rapidly in recent years along with technological advancements. In this study, an innovation district is defined as a geographical area where clusters of world class institutions and companies are well-connected with business accelerators and start-ups to drive growth and increase productivity. These areas are also characterized by having an easily accessible transport system and have mixed-use for work and play. They also serve as a strong foundation for creation of jobs and development of organisations. An innovation district can be seen as an innovation ecosystem that consists of economic assets, physical assets and networking assets where several factors come to play to sustain the ecosystem in the long term. Several studies have been made to analyse the importance of innovation districts in driving economic growth and enhancing the quality of our lives. However, there are currently no methods put in practice to measure the level of success of innovation districts in a quantitative way, which is crucial to identify the specific areas that are lacking and implement new strategies to improve the overall performance of a district. Therefore, a metric system is proposed in this paper to measure the level of success of an innovation district in a quantitative manner by obtaining an overall performance score. There are five main key performance indicators selected to be used in the metric system. Several measurable parameters can be used to quantify each key performance indicator that can be compared across different innovation districts. After which, a few predictive models have been considered to measure the potential growth and help districts identify specific areas to improve on. A hybrid forecasting model has been proposed which can help improve the accuracy of representing data and predict future values. Lastly, the limitations and future work of this project is presented in this paper. This project is industry sponsored, where Smart Cities Network has been chosen as the industry partner to collaborate with NTU. Discussions were made with the Smart Cities Network throughout this project.