Resources

A plain-english guide to the Infrastructure Contributions Plan (ICP) evidence challenges for growth area councils in Victoria, Australia.

Forecaz CEO Bradley Rasmussen presented original research at the BNMA 2025 Conference hosted by the Bayesian Network Modelling Association and the University of Melbourne, showing how combining Bayesian Network models with Machine Learning produces more accurate urban growth forecasts than either method alone.

Forecaz presented at the PIA QLD Conference with Toowoomba Regional Council, showcasing their AI‑Powered Urban Growth Model. The session explored how AI codifies planner knowledge, aligns growth with infrastructure, and supports evidence‑based decisions for sustainable urban development. A great exchange on how AI is reshaping the future of planning in Australia.

Forecaz founder joins Nicole Bennetts on the Hustle & Bustle podcast to explore the future of cities, discussing how AI and data-driven modelling are reshaping urban planning. Discover how Forecaz enables planners to test zoning scenarios, improve accuracy, and connect technology with the realities of policy and funding.

The City of Gold Coast partnered with Forecaz and Griffith University to develop an evidence-based Planning and Urban Growth (PUG) model, aligning with statutory guidelines and official QGSO projections. The model combines Bayesian Networks and Machine Learning to predict parcel-level development, supporting land use analysis, scenario testing, and major infrastructure planning such as the Light Rail extension.

Urban Utilities partners with Forecaz to deliver data-driven growth planning. With SE Qld’s rapid expansion placing increasing pressure on critical water and sewerage infrastructure, UU needed a smarter, spatially detailed way to plan for growth. Using the Forecaz platform, UU now models urban development at property level, bringing data accuracy and operational efficiency to long-term infrastructure forecasting.