StopOdds is a public-interest, non-profit project that helps understand fare inspection patterns on Melbourne public transport. We are committed to transparency and protecting your privacy through technical and procedural safeguards.
We collect only anonymous trip and demographic data:
How many public transport trips you took in the specified period
How many times you were checked during those trips
Your anonymous data is used to:
We compute inspection rates per 100 trips for different demographic groups
We use Poisson/Negative Binomial regression to identify patterns
You receive an estimate based on your demographic traits
Public results help inform transport equity discussions
Any demographic group with fewer than 50 respondents is automatically suppressed from public results. This prevents identification of individuals in small groups.
We don't publish detailed cross-tabulations that could reveal information about small subgroups, even if individual groups meet the 50-person threshold.
Raw submission data is retained for 12 months for model training and validation. After this period, only aggregated statistics are kept, with no way to trace back to individual submissions.
Data is stored securely with encryption in transit and at rest. Access is strictly limited to automated processing systems. We use industry-standard security practices and regular backups.
This project operates under the Australian Privacy Principles (APP) with specific focus on:
This privacy policy and our methodology are publicly available
All data collection is anonymous with no identifying information
We only collect information necessary for our stated statistical purposes
We implement validation and quality checks on submitted data
Appropriate technical and organisational measures protect your data
Since we collect only anonymous data, traditional data subject rights (like access or deletion requests) don't apply in the usual way. However:
You can choose what information to provide
We don't build individual profiles or track users across sessions
Our statistical methods and code are transparent and auditable
If you have questions about this privacy policy or our data practices: