Geodemographic Analytics

The characteristics of people living in our cities are changing rapidly. Increasingly diverse and dynamic populations are the result of higher migration rates, urban consolidation and gentrification, different patterns of working, and children who stay at home into their thirties and beyond. This demographic shift of where and how people live and work impacts the demand for goods, services and infrastructure. Planning to meet that social, commercial and community demand in an efficient and effective manner requires accurate forecasts detailing how demographic composition in small areas will change over time.
Small area classifications
Small area profiles and classifications provide an ability to aggregate features within and across small areas. Tile metrics enable areas to be understood in terms of different features that can be overlaid to provide a rich understanding of the nature of individual small areas, or tiles, which, by their nature, facilitate aggregation into larger functionally important geographic areas. A data model based on tile metrics allows for consideration of relationships that exist between geodemographic features, while simplifying visual representations and cartographic analysis in parcels that have jurisdictional or commercial meaning for organisations and government.

Sensing Value has pioneered new, robust techniques for projecting sample-based data back to small areas (from Statistical Area 1 level up to national level). Sensing Value’s Small Areas Modelled Statistics (SAMS) methodology enables Sensing Value to take validated, highly trusted data sources provided at higher geographic resolution - from sources such as the Australian Bureau of Statistics - and project this data back to small geographic areas. Our library of SAMS tiles are building blocks that can be configured and layered to suit a variety of population, economic, social, investment and service analyses. SAMS tiles are also the building blocks used by our machine learning, modelling and scenario testing use cases. Our SAMS data sets are continually updated using trusted data sources.
Population forecast
Sensing Value uses detailed time series data sets to map the demographic composition of small areas across different Census data (1981 to 2016) as a foundation for forecasting how the composition of these small areas is likely to continue to change in the future. Accurately forecasting populations requires more than extrapolation of demographic trends. Sensing Value has developed population forecast models based on models of underlying structural relationships that exist between land use, population and employment. Census data allows the formulation of a range of ratios and estimates to be developed that can predict population density as a function of changes in land use. It is possible to estimate density of people by role, and by building given occupancy, land use and built form patterns, which can then be aggregated up to larger geographic areas.
Modelling incidence of social issues - domestic violence, drink driving, reduced life expectancy, health risk behaviours
Sensing Value has developed advanced geo-demographic modelling techniques that allow estimation of the incidence of social issues at a small geographic level, areas of around 700 households called the Statistical Area 1 (SA1) by the Australian Bureau of Statistics.

These techniques have been successfully applied to create an expected value or count for a range of social issues and behaviours including:
- incidence of undetected drink driving, road trauma risk
- smoking rates, alcohol consumption patterns
- use of prescription drugs and patterns of compliance in taking prescribed medications
- participation in higher education by school leavers
- crime rates, including hooliganism, feeling of insecurity, domestic violence

The modelled, or expected rates that apply at the small area can then be compared to the official statistics generated for larger geographic areas. Where there is a gap between actual and expected rates lever analysis can be applied to determine the factors that correlate with the gaps. For example, in modelling life expectancy at the SA1 level shorter life expectancy was associated with less access to primary health care, as well as proximity to active mine sites.
Housing Demand
Sensing Value’s joint research with Monash University’s Centre for Population and Urban Research has led to the development of housing demand statistics within small areas. This highly specialised set of analytic tiles has received wide-spread recognition for both the sophistication of the approach taken and the specificity of the metrics that were derived in terms of occupier/investor dynamics, occupier demand by housing type and location, and housing stress indicators.
Small area time series demography
Sensing Value has built detailed time series data sets mapping the demographic composition of small areas across different Census data sets spanning from 1981 through to the most recent 2016 Census.
We don’t just say we collaborate to innovate, it’s inherent in our business model. Our engagement with leading academic and industry partners is the basis for continuous development of products and services that consider how people move, use spaces, and engage in activities over time. Add that to varied and extensive domain knowledge and you have a company that delivers dynamic intelligence and smart solutions. That defines Sensing Value.

Sensing Value Pty Limited
ABN 40600933211

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