Shifting Patterns of Theft in Victoria (2016–2025)

Data Visualisation & Communication — Assignment 3 (Web-hosted)

Ganesh Kumar Reddy Avula (s4187768)

27 October 2025

1) Objective & Constraints

  • The goal of this assignment is to tell an insightful and engaging story using open and publicly available data, transforming statistical information into meaningful visual insights.

  • The selected topic focuses on Theft offences (Subdivision B40) recorded across Victoria’s Local Government Areas (LGAs) from 2016 to 2025, as reported by the Crime Statistics Agency (CSA) Victoria.

  • The project demonstrates how data visualisation and storytelling can be combined to uncover regional and temporal trends in theft, allowing viewers to better understand the social and geographical dynamics of crime in Victoria.

  • The final output is a web-hosted, R-based slideshow created with reveal.js, adhering to the assignment constraints of using no static reports or videos.

  • The story is presented in no more than 10 slides, ensuring the focus remains on data-driven visuals and concise interpretation.

  • The published slideshow will be made publicly accessible via RPubs, ensuring transparency, reproducibility, and compliance with the principles of open data.

2) Data Overview

  • Primary Source: Crime Statistics Agency (CSA) Victoria — Recorded Offences, Year Ending June 2025 (Table 02: LGA rates). This dataset provides official records of criminal offences by Local Government Area (LGA), including offence subdivisions such as Theft (B40). It includes both counts and rates per 100,000 population, enabling fair comparison between LGAs of different sizes. crimestatistics.vic.gov.au
  • Secondary Source: Australian Bureau of Statistics (ABS) — Regional Population Estimates, 2025. Used to contextualise population differences and ensure rate-based comparisons remain accurate and representative. abs.gov.au
  • Both datasets are open access, reputable, and publicly verifiable. The use of rates per 100,000 people mitigates population-size bias, ensuring an accurate regional analysis of theft across Victoria.
## $year_range
## [1] 2016 2025
## 
## $n_lgas
## [1] 79

4) Geographic Distribution of Theft (2025)

  • Displays theft rates per 100,000 people across Victorian LGAs (2025).
  • Darker shading indicates higher theft intensity, concentrated in inner-urban and growth areas.
  • Reveals clear spatial disparities in crime distribution.

5) Biggest Shifts in Theft Rates (2019–2025)

  • Compares theft-rate changes from 2019 to 2025.
  • Left chart: LGAs with the biggest increases; right chart: those with the largest declines.
  • Shows how theft patterns diverged sharply post-pandemic.

6) Top 20 LGAs — Theft Rate Comparison

  • Tracks theft trends for the top 20 LGAs with the highest rates in 2025.
  • Urban LGAs stay persistently high, while others show distinct rises or declines.
  • Illustrates long-term variation across local contexts.

7) Local Focus — Theft Trend by LGA

  • Focused trend for a single LGA (e.g., Melbourne).
  • Displays yearly change in theft rates and local volatility over time.
  • Shows how specific communities differ from the statewide pattern.

8) Takeaways

  • Uneven theft pressure: Inner-urban commercial hubs (e.g., Melbourne, Yarra) and fast-growing suburban corridors consistently record theft rates above the state median, while regional LGAs show mixed and variable patterns.

  • Post-2019 divergence: The period following the COVID-19 pandemic introduced strong spatial divergence, as mobility, retail activity, and vehicle use rebounded unevenly across the state, creating different local theft trajectories.

  • Policy relevance: These findings suggest a need for targeted local prevention strategies — for example, enhanced security in retail zones, vehicle-theft deterrence in car parks, and place-based policing tailored to high-risk LGAs.

  • Broader insight: Theft in Victoria is not uniformly distributed but shaped by urban density, economic opportunity, and social mobility, emphasising the value of data-driven crime prevention.

9) References