Theft from Motor Vehicle vs Socio-Economic Advantage (Victoria)

Exploring patterns in crime using CSA and ABS data

Nuttanicha Kaewsayta (s4067518)

26 October 2025

Framing the Story

  • Focus: Theft from Motor Vehicle (TFMV) in Victoria
  • Compare crime rates with socio-economic advantage using ABS SEIFA IRSAD 2021
  • Questions:
    • Are TFMV rates higher in more disadvantaged LGAs?
    • Has this pattern changed over time?

Data Sources

Average TFMV Rate by SEIFA Group

  • This plot compares theft-from-motor-vehicle (TFMV) incident rates between socio-economic groups.
  • It shows how disadvantaged, middle, and advantaged LGAs have changed over the past decade.
  • The goal is to identify whether the socio-economic gap in TFMV rates has persisted over time.

Rate Gap Over Time

  • This chart shows the difference in TFMV rates between highly disadvantaged and highly advantaged LGAs.
  • A positive gap means disadvantaged areas experience more incidents per 100,000 residents.
  • It highlights whether inequality in property crime has narrowed or remained stable over time.

Distribution by SEIFA Quintile

  • This boxplot displays the spread of TFMV rates across SEIFA quintiles for the most recent year.
  • Each box represents a range of LGAs within a socio-economic group.
  • It helps to see how crime levels vary across the entire distribution, not just averages.

Top 10 LGAs — Highest TFMV Rates

  • Urban and densely populated LGAs tend to dominate the top ranks.
  • These areas may face more vehicle-related opportunities for theft due to higher density and traffic.

Bottom 10 LGAs — Lowest TFMV Rates

  • Regional and less populated LGAs often have lower theft rates.
  • These areas may benefit from stronger local community ties and lower vehicle density.

Relationship Between SEIFA and TFMV

  • This scatter plot examines the relationship between socio-economic decile and TFMV rate.
  • The downward trend line suggests that higher socio-economic advantage is linked to fewer TFMV incidents.
  • It visually supports the idea that disadvantage is a strong predictor of property crime.

Summary of Findings

  • Theft from motor vehicles remains consistently higher in socio-economically disadvantaged LGAs.
  • The rate gap between disadvantaged and advantaged areas has persisted across the last decade, showing limited signs of improvement.
  • Urban and inner-city LGAs, which often have higher population density and vehicle concentration, record the highest incident rates.
  • Advantaged LGAs generally experience fewer incidents, suggesting a link between community affluence and lower property crime exposure.
  • Overall, the findings highlight a strong and persistent relationship between socio-economic disadvantage and theft-related offences in Victoria.

References

Australian Bureau of Statistics. (2021). Socio-Economic Indexes for Areas (SEIFA), Australia, 2021. Australian Bureau of Statistics. Retrieved October 22, 2025, from https://www.abs.gov.au/statistics/people/people-and-communities/socio-economic-indexes-areas-seifa-australia/2021

Australian Bureau of Statistics. (2024). Regional population, 2023–24. Australian Bureau of Statistics. Retrieved October 22, 2025, from https://www.abs.gov.au/statistics/people/population/regional-population/latest-release

Crime Statistics Agency. (2025). Crime by location: Data tables – Criminal incidents, year ending June 2025. Crime Statistics Agency, Victoria State Government. Retrieved October 22, 2025, from https://www.crimestatistics.vic.gov.au/crime-statistics/latest-crime-data-by-area

R Core Team. (2025). R: A language and environment for statistical computing (Version 4.3.2) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Wickham, H., & RStudio. (2023). Tidyverse: Easily install and load the tidyverse (Version 2.0.0) [R package]. https://CRAN.R-project.org/package=tidyverse

Xie, Y., Allaire, J. J., & Grolemund, G. (2023). R Markdown: The definitive guide (2nd ed.). Chapman & Hall/CRC.