Urban Redevelopment Authority (URA) is responsible for identifying suitable land plots for siting of new amenities. This process is known as Site Search. The current process of site search relies a lot on the planner’s familiarity. Local planners can spend a dedicated 1 to 2 weeks of work to identify suitable potential sites, using many different applications and data.

Finding new sites for facilities is a multi-faceted problem that involves the matching of time, space and various planning considerations. Planners have to understand and analyse the:

    ⦿ Demographics of an area for the particular timeframe

    ⦿ The capacity of a facility

    ⦿ Catchment of the facility

    ⦿ Added considerations specific to a site

The challenge increases exponentially in complexity when planners look for sites with a combination of requirements. Trade-offs then have to be weighed against each other, adding to the challenge.

Hence, there is value to exploring an evidence-based decision making support system, to help planners suggest options for site search.

Problem Statement

<sub>The aim of our project is to design a data-driven recommendation engine for site search.

This engine should optimise the quantitative aspects of the search, recommending optimal potential sites to the planners for their qualitative analysis.</sub>

Optimisation Models