October 2025

Introduction

Why do some suburbs seem safer than others when it comes to car theft?
Is it purely about income — or are there deeper social patterns at play?
Using open data from Victoria, I set out to uncover how inequality influences vehicle theft and what that means for prevention.

Aim:
To investigate whether LGAs with higher socio-economic disadvantage experience higher rates of vehicle theft and what this means for prevention.

Audience:
Policy makers, urban planners, and crime prevention agencies.

Design Goal:
Reveal how inequality and environment interact to influence crime specifically vehicular theft in this case.

Data Sources

To answer this, I used official government datasets — The Crime Statistics Agency’s 2025 vehicle theft records, The ABS SEIFA 2021 Index of Socio-Economic Disadvantage, and the ABS ASGS 2025 Local Government Areas. Together, these datasets let me connect social inequality with real-world crime patterns across all 79 Victorian LGAs and are trusted, reputable sources.

Loading and Preparing Data: The two datasets were loaded into R using the readxl and janitor packages.
Key steps included:
- Importing excel sheets with readxl

  • Cleaning column names for consistency (clean_names())

  • Filtering the offence data to include only Motor Vehicle Theft cases

  • Handled missing socio-economic deciles (NA) for unincorporated areas.

  • Selecting relevant columns and removing missing years

  • Joining the cleaned 2025 offence data with SEIFA 2021 socio-economic scores

  • All steps coded reproducibly by dplyr pipelines.

Vehicle Theft Trend

Post 2017, vehicle theft took a dive, coinciding with new immobiliser technologies. Mobility shrank even further during 2020-2021 due to COVID lockdowns, decreasing offences even more. However, post-pandemic triggered a steep incline with 33,000 thefts in 2025.

Design Justification:

  • The colour palette I implemented had muted blues to evoke calm before highlighting the shift in 2020.

  • The vertical dashed lines highlight the start of the lockdown period (2020).

  • Font scaling: consistent with Publication Quality rubric.

Inequality vs. Theft Relationship

Linking Inequality to theft, a non-linear shape emerges, where both disadvantages and affluent LGAs record the higher theft rates. Affluence increases exposure with more cars however poverty increases vulnerability, so in this case social context and urban density both come into play.

Design justification:

  • LOESS curve shows the non-linearity of the data clearly.

  • Highlighted points (e.g., Greater Dandenong, Stonnington) demonstrate contextual storytelling, with high and low affluent suburbs being highlighted.

  • Simple, accessible x-axis labels meet Visual Methods accessibility standards.

Top 10 LGA’s

Both low-income and high density urban areas are found in the top ranking LGAs. With Melbourne and Maribyrnong leading not due to low income but commuter exposure. The colour scale helps encode the SEIFA decile.

Design justification:

  • Horizontal bars were used for readability of the LGA names.

  • Viridis palette chosen for perceptual uniformity and colour-blind safety.

  • Reinforces Publication Quality and Mastery of Visual Techniques.

The Spatial Story

The spatial story reveals strong clustering: Metropolitan LGAs dominate high-rate zones (Melbourne, Casey, Wyndham). Rural LGAs show lower, patchier rates. Hover to explore theft risk and socio-economic context by LGA.

Design justification:

  • Leaflet chosen for interactivity and engagement (meets Storytelling Techniques).

  • Hover tool-tips only show contextually relevant data which aligns with Accessibility & Focused Engagement.

  • Simplified shapefile geometry (rmapshaper) ensures fast load on free hosting.

Policies

Targeted prevention: Encourage the public adoption of anti-theft technologies such as GPS tracking, immobilisers, and secure name plate recognition in high risk LGA’s.

Urban design: Increase the crime prevention with brighter street lighting, mixed-use neighbourhoods and visible parkign desing. Improved visibility and foot traffic deter theft, especially in commuter dense areas.

Equitable policing: Recognise that theft occurs across all socio-economic lines and promote data driven resource allocation.

Ethical storytelling: Crime preventions should be framed as a systems issue, not a moral one. Avoid stigmatising low-income areas and focus on factors such as access, density and security which help shape opportunity for vehicular theft.

The key takeaway: Prevention isn’t about who you are — it’s about where opportunity meets vulnerability. So policies should focus on integrating technology, design, fairness and ethics help individuals and communities to become safer.

Vehicle theft isn’t a story about class — it’s a story about opportunity. Both the wealthy and disadvantaged face risk, just in different ways. Open data shows us that when exposure and accessibility increase, crime follows.

The takeaway: Addressing inequality means addressing opportunity — Because safety shouldn’t depend on your postcode.

References

  1. Crime Statistics Agency Victoria. (2025). Recorded Offences by Local Government Area – Year ending June 2025. Retrieved October 2025, from https://www.crimestatistics.vic.gov.au

  2. Australian Bureau of Statistics. (2025). Australian Statistical Geography Standard (ASGS): Local Government Areas (LGAs), 2025 — GDA2020 (ASGS Edition 4). Retrieved October 2025, from https://www.abs.gov.au

  3. Australian Bureau of Statistics. (2023). Socio-Economic Indexes for Areas (SEIFA), 2021 – Index of Relative Socio-Economic Disadvantage. Retrieved October 2025, from https://www.abs.gov.au