Cities are complex urban environments. Detailed fine grain data is increasingly needed to provide the evidence base for integrated and locally focused planning and investment decisions.
SGS is a market leader in producing small area land use forecasts to aid decision making across government and the private sector. Most recently, this has involved creating the state government forecasts for Victoria and NSW. This experience, along with SGS’ extensive engagement in policy and development advisory, means that forecasts are informed by a comprehensive and continually updated range of datasets.
Small Area Model (SAM) creates a suite of forecast variables which are:
- Estimated using a combination of ‘top-down’ and ‘bottom-up’ methodologies, ensuring that macroeconomic drivers are integrated with micro spatial data and trends
- Disaggregated to a fine-grain spatial scale, allowing for custom geographies to be defined based on the scope of analysis, such as an activity centre or renewal precinct
- Developed using a dynamic and systematic algorithm which enables robust scenario testing and impact analysis
- Presented via a web-accessible and interactive platform which allows for analysis and visualisation.
A vital aspect of SAM is that it can produce these forecasts using a transparent set of inputs, assumptions, and techniques. These parameters can therefore be altered to create sets of clearly defined scenarios, which are a key focus of policy evaluation.
SAM includes detailed information across three key variable types. These are:
- Dwellings and population, which includes a range of household and demographic attributes
- Employment, which includes labour force status, industry and occupation attributes by place of usual residence and place of work
- Students, which includes primary/secondary/tertiary student attributes by education provider type, place of usual residence and place of institution.
Contact our SAM experts Terry Rawnsley and Julian Szafraniec for more information.