Explore local area socio-economic disadvantage and its drivers (2021)

This web mapping application provides information about the geographic distribution of relative socio-economic disadvantage and the contributing indicator variables derived from 2021 Census data, thereby providing a deeper understanding of local area disadvantage and its drivers.

The data used in this web mapping application is the Australian Bureau of Statistics Socio-Economic Index for Areas Index (SEIFA), Index Relative Socio-Economic Disadvantage (IRSD) 2021 by Statistical Area 1 (SA1) geography (Australian Statistical Geography Standard (ASGS) Edition 3) and includes mapping of the standardised variable proportions for each of the contributing IRSD indicator variables associated with socio-economic disadvantage. Please note, in calculating the SEIFA IRSD index the indicator variables are weighted, the standardised variable proportions mapped in this application are unweighted.

The SEIFA Indexes are calculated from area level data and therefore indicate the collective socio-economic characteristics of the people living in an area. While an area may be identified as relatively disadvantaged this does not mean that all individuals within that area are disadvantaged, only that relative to other areas, this area has a high proportion of relatively disadvantaged people. For detailed information on how to use the SEIFA data, please refer to the SEIFA 2021 Technical Paper. This application is designed primarily for desktop view.

This Application is made possible by the Digital Atlas of Australia

The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS)

If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide, view the ABS privacy policy.Data and geography referencesSource data publication: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), AustraliaGeographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3Further information: Data downloads (Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, Data downloadsSource: Australian Bureau of Statistics (ABS)

The Socio-Economic Indexes for Areas (SEIFA) rank areas according to their relative socio-economic advantage and disadvantage using 2021 Census data. This layer presents data by Statistical Area Level 2 (SA2), 2021. SEIFA 2021 consists of four indexes: The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) The Index of Relative Socio-economic Disadvantage (IRSD) The Index of Education and Occupation (IEO) The Index of Economic Resources (IER) Each index summarises different subsets of 2021 Census variables and focuses on a different aspect of socio-economic advantage and disadvantage.For detailed information on how to use the SEIFA data, please refer to the SEIFA 2021 Technical Paper.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, Data downloads Source: Australian Bureau of Statistics (ABS)

SEIFA Disadvantage Deciles were joined to the 2016 Census SA1s based on the SA1 seven digit number. Of 831 SA1s in the Sunshine Coast Region, 35 were excluded based on exclusion rules. This left 796 SA1s with an index score, as symbolised on the map.

This dataset presents the footprint of the proportion of underinsurance across Australia. The data is aggregated to Statistical Area Level 1 (SA1) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS). House and contents underinsurance is understood as homeowners having no house insurance and renters having no contents insurance to cover adverse events.

To create this dataset, researchers developed a method to extrapolate the patterns of underinsurance evident in the 2015 Australian Survey of Social Attitudes (AuSSA), an omnibus postal survey of Australian adults (Blunsdon, 2016). To do this, they combined the results of the full model of underinsurance with the 2016 Socio-Economic Indexes for Areas (SEIFA) (Australian Bureau of Statistics, 2019). For this spatial mapping, regression coefficients were converted to probabilities by taking the exponent of each coefficient to generate the odds ratio and then using the formula: probability = odds/(1+odds). For each SA1 unit (containing approximately 150 households), the proportion of residents or households was determined for each predictor variable from raw census data. The level of underinsurance (proportion of people predicted not to have insurance) was then predicted separately for renters and owner-occupiers for every SA1 and a single map generated by weighting the predictions by the proportion of renters and owner-occupiers per SA1.

For further information about this dataset and its creation, please refer to the publication: Booth, K., & Kendal, D. (2019). Underinsurance as adaptation: Household agency in places of marketisation and financialisation. Environment and Planning A: Economy and Space.

Please note:

• The researchers acknowledge some limitations with the data, including the lack of data on rental properties. They do not know whether these properties are insured by landlord-investors and how this may be associated with sociodemographic variables and contribute to the mapping.

• This research was in part supported by the Australian Government through the Australian Research Council Discovery Program (DP170100096).

Thermal patterns in the urban landscape can be viewed as heat islands (areas at least 125 m x 125 m) and localised hot spots (areas at least 2 m x 2 m). Heat islands reveal where heat has built up and what features of the urban setting are most severely affected. Hot spots display intricate patterns of heat and allow for exploration of how different surfaces contribute to heat build-up.Underlying each heat island is a menagerie of mixed landscapes, land-uses, and land-covers resulting in different characteristics of each heat island. Investigating both day and night time thermal effects, and specifically where persistent heat islands retain heat into the evening reveals additional information about how land-use affects liveability. Analysing social vulnerability within heat islands reveals who lives within these areas, identifies social groups that are disproportionately affected by heat, and helps prioritise which areas of heat are most in need of remediation. How to cool heat islands depends on what lies within heat islands.The tools for mitigating urban heat (proximity to water, green infrastructure, white roofing) generally come at additional costs, which tends to result in heat islands having a more pronounced effect upon residents of lesser means. To assess whether heat disproportionately affects any particular groups social vulnerability data was acquired from the 2011 census. Key social vulnerability indicators were identified as: elderly population (>75 years old)people who need of assistance due to disabilitiespeople who speak English as a second language not well or not at allmedian rent paid by residents; and Socio-Economic Indexes for Areas of Disadvantage (SEIFA Score). Data were acquired from the 2011 Census at the Statistical Area Level 1 (SA1). These data were used to create a simple Social Vulnerability Index (SVI), normalizing each dataset from 0-1 and summing the results to give an index value of 0-5 representing low to high vulnerability. The SVI was calculated for each urban heat island informing where heat and vulnerability co-exist.