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license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- city-data
- violations
- municipal
- citations
- synthetic-data
- mindweave
- govtech
- fines
- law-enforcement
- test-data
- civic-data
- parking
- urban-planning
- open-data
- transportation
pretty_name: City Parking Violations & Citations Dataset (Free Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: citations
data_files: data/citations.csv
default: true
- config_name: officers
data_files: data/officers.csv
- config_name: zones
data_files: data/zones.csv
---
# City Parking Violations & Citations Dataset (Free Sample)
> **This is a free sample** with 2,070 rows. The full dataset has **14,825 rows** across 3 tables.
Parking citation records for a simulated mid-size US city (population ~350K)
over 2 years. 25,000 citations across 50 zones, with officer assignments,
violation types, fine amounts, and payment status tracking.
Features location clustering (downtown hotspots), time-of-day patterns
(meter violations peak 9-11 AM), repeat offenders, and two anomalies —
a parking meter system failure causing a citation surge and a holiday
enforcement blitz in December.
Ideal for: civic tech apps, GovTech dashboards, urban planning analytics,
fine collection systems, and open data portal demos.
## Sample tables
| Table | Sample Rows |
|-------|------------|
| citations | 2,000 |
| officers | 20 |
| zones | 50 |
| **Total** | **2,070** |
## Full dataset
The complete dataset includes all tables with full row counts:
| Table | Full Rows |
|-------|----------|
| citations | 14,755 |
| officers | 20 |
| zones | 50 |
| **Total** | **14,825** |
**Formats included:** CSV, Parquet, SQLite
**[Get the full dataset on Gumroad](https://mindweavetech.gumroad.com)**
## About
Generated by [Mindweave Technologies](https://mindweave.tech) -- realistic synthetic datasets for developers, QA teams, and data engineers.
Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)
Browse all datasets: [https://mindweavetech.gumroad.com](https://mindweavetech.gumroad.com)
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