Predicting the future of cities

Forecaz is a proven technology platform that allows organisations to reliably perform urban growth modelling. Forecaz allows government planning agencies, developers and utilities to analyse the impacts of current and alternative land use zonings and evaluate their effects on urban development into the future. 

Forecaz utilises first-principles methodology and Artificial Intelligence to forecast residential and non-residential development at a land parcel level. Forecaz employs AI and Bayesian Network probabilistic models to perform predictive development growth sequencing. The platform supports a variety of growth projections being used for the urban growth forecast, such as Population, Gross Floor Area (GFA) or Employment. The modeller uses a bottom-up and top-down data approach to allocate and sequences land parcels to their highest and best development potential aligned with the projection years nominated for an urban growth model.

No data scientists or complex data inputs required

Forecaz doesn’t require mathematicians and data scientists to configure and process the models that most urban planning modelling tools require to produce a functional result. Many of our clients only have employees with urban planning experience and no specialist data science skills. However, they are able to generate robust urban growth models.

Forecaz isn’t reliant on expensive external datasets that are frequently out of date and not widely available such as construction cost indexes. Reliable models can be produced from publicly available data provided by Local Government Authorities.  Organisations can quickly produce an initial model with this data in four to six weeks.  Forecaz provides advanced configuration features that allow organisations to further refine a model as more data becomes available.  Forecaz also has the ability to incorporate your own data into the urban growth models.

Forecaz has the ability to utilise future urban growth demographics and generate network demand models for a variety of infrastructure networks. Alignment between forecast growth and infrastructure demand can be easily obtained for networks such as water, sewerage, transport and electricity. Utilities can load their current baseline demand into these models to improve the model’s accuracy.

Forecaz accounts for the economies of scale that large development sites can achieve. This ensures development is staged appropriately to align with development take-up in an urban growth model. Also, some sites such as major shopping centres will continue to grow incrementally over the time horizon of the urban growth model. In many of these situations, these sites may never reach their full ultimate development potential. Forecaz provides development staging bands that allow residential and non-residential development to be delivered in defined dwellings and GFA amounts over nominated time periods. 

Self-service capability

Forecaz provides a web-based map viewer allowing general users to spatially interact with scenario models that have been generated and extract the information they require. The viewer provides an interactive map user interface giving general users the ability to geospatially aggregate demographic data, demand growth and infrastructure charges by spatial areas contained in GIS layers (i.e. Statistical Areas (SA2), Suburbs, Infrastructure Network Service Areas). The viewer enables users to compare separate demographic projection years from the same or different forecast models and spatially view the variance between the models.

The viewer allows the model’s network demand and charge forecasts to be aggregated by infrastructure networks and service area catchments.  The module provides the facility to view the generated output data in tabular format.  This data can also be exported to spreadsheets for further manipulation and presentation if required.

The viewer provides users with comprehensive search facilities for land parcels using an address, lot/plan or a property identifier.  Users can then view the forecast attributes of those land parcels including demographics, network demand, and infrastructure charges.  The viewer enables users to search for development approvals and view network demand for land parcels subject to development approvals.

Spatially compare forecast growth models
Comparison of two forecast models

Forecaz features include:

  • A user-friendly web interface provides a spatial viewer and reporting tool that allows the aggregation and visualisation of a model’s attributes by user-defined spatial areas, such as Statistical Areas, Network Services Areas, and Suburbs.
  • Provides the ability to search for land parcels utilising address or property identifiers. Users can view the forecast attributes of those land parcels including dwelling, gross floor area, population, and employment demographics; infrastructure network demand; and infrastructure charges.
  • Corporate GIS Layers can be incorporated in the viewer and overlaid on the Forecaz spatial models.
  • Allows users to search for development approvals and compare the development delivered by the approval against the ultimate development forecast by the urban growth model.
  • Forecaz can provide accurate models using existing publicly available datasets and also incorporate an organisation’s corporate data.
  • Allows a baseline year and one or more projection years to be generated. Each projection year forecasts the assumed growth in demographic attributes and uses these attributes to calculate the demand on various infrastructure networks at each projection year.
  • Forecaz models mixed residential and non-residential development occurring on a land parcel. The model accounts for the dependency and interactions in delivering mixed-use residential and non-residential development, ensuring the overall development is aligned with the residential and non-residential projection targets.
  • Straight forward to configure models with the user interfaces providing a wide variety of parameters to give flexibility in defining models. For example, employment can be determined from GFA, parcel area, developable area, dwellings, and population.
  • Forecaz is not reliant on expensive external datasets that are frequently out of date and not widely available or require expensive external consultants to keep refreshing.
  • Provides the facility to define ‘what if’ urban scenario models with independent growth and development density parameters. An unlimited range of growth forecasts and different development scenarios can be configured to produce property-level demographics and network demand forecasts as alternative scenario urban growth models. The tool allows different scenarios to be geospatially compared and analysed against each other.
  • Forecaz employs Bayesian Network (BN) modelling for a more accurate prediction of the sequence of development and growth, based upon a range of criteria that influences the probability of development (i.e. vacant land, development yield, proximity to infrastructure).
  • Allows for economies of scale that large development sites can deliver to be accounted for in the urban growth model. Forecaz allows some sites such as major shopping centres to continue to grow incrementally over the time span of the urban growth model. In many of these situations, these sites may never reach their full ultimate development potential. Forecaz provides development staging to cater for these requirements within the urban model.  Forecaz provides development staging bands that allow residential and non-residential development to be delivered in defined staging amounts over time.  These staging bands are aligned to geospatial areas, allowing the various staging configurations to be applied at wide-scale areas right down to a property level.
  • Provides the ability to define multiple infrastructure networks, such as Water Supply, Sewerage, Roads and Stormwater, for incorporation into forecast models.
  • Network demand generation rates can be set for specific land uses and development areas for all types of infrastructure networks, e.g. water demand, trip generation and electricity demand. Demographic forecasts can be automatically converted into network demand forecasts and facilitate the development of detailed and consistent demand forecasts across the region and across infrastructure networks. This ensures alignment between forecast growth and infrastructure demand.
  • Calculates the forecast infrastructure charges revenue for the assumed demand growth, supporting both a service area-based charging methodology and a land use-based charge methodology for the calculation of infrastructure charges.
  • Property level demographic and network demand forecasts are exported as a spatial layer, allowing the forecasts to be used by network modelling packages and viewed as map layers.

View parcel level demographic assumptions
View parcel level demographic assumptions

Proven low-risk solution

Forecaz is a tool that allows significant improvements in the conversion of planning densities, constraints and assumptions into demographic and demand forecasts. By automating and providing a repeatable and reliable forecasting process, Forecaz significantly improves the detail and quality of development urban growth forecasts and assumptions.

Forecaz has successfully been used by Local Government Authorities (LGA) and water utilities to model large complex local government areas such as the City of Gold Coast (CoGC). The City of Gold Coast is Australia’s second-largest city, with a devise mix of land use development that includes mixed-use multi-level residential and non-residential development, industrial, retail, commercial, tourism and low and high-density residential. Urban growth within this area is both infill development with increasing dwelling densifications and broad-hectare greenfield development.  Forecaz has been implemented by CoGC to produce an evidence-based urban growth model for the city. Forecaz has been utilised to produce parcel level planning assumptions for the city’s latest Local Government Infrastructure Plan (LGIP).

Forecaz can also model small regional local government areas such as Corangamite in Victoria. With a population of approximately 16,000 people, the council area is predominately rural land that has limited growth opportunities. There is a small amount of urban growth occurring in the small regional towns throughout the council area.  The Forecaz model of the region’s future land use allocations has been used to assess the region’s potential for future tourism development.

Please contact us for further information or a demonstration of Forecaz.