parking-violations / README.md
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metadata
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

About

Generated by Mindweave Technologies -- 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