NYC 311 Service Request Data Analysis

Overview

Comprehensive analysis of NYC 311 service request data to identify complaint patterns, response times, and geographic distributions across New York City boroughs.

Quick Start

# 1. Install dependencies
pip install -r requirements.txt

# 2. Run the full analysis pipeline
python main.py

This generates:

• nyc311_profile.html - Interactive HTML profiling report with all charts embedded
• Report.pdf - Executive summary

Project Structure

GET_305_Data_Analysis/
├── main.py                 # ⭐ MAIN ENTRY POINT
├── requirements.txt        # Python dependencies
├── README.md               # This file
├── NYC311_analysis.ipynb   # Detailed Jupyter notebook with statistics
├── nyc311_sql_tasks.sql    # SQL cleaning queries
├── setup_database.py       # Database setup module
├── generate_dashboard.py   # HTML profiling report generator
├── generate_report.py      # PDF report module
├── nyc311_profile.html     # Generated profiling report
├── Report.pdf              # Generated PDF report
└── .gitignore              # Git ignore rules

Usage

Full Pipeline (Recommended)

python main.py

Individual Steps

python main.py --setup      # Setup database only
python main.py --dashboard  # Generate HTML profiling report only
python main.py --report     # Generate PDF report only
python main.py --help       # Show all options

Jupyter Notebook

For interactive analysis with statistics:

jupyter notebook NYC311_analysis.ipynb

Generated Outputs

File	Description
nyc311.db	SQLite database with raw and cleaned data
nyc311_profile.html	📊 HTML profiling report with embedded charts
Report.pdf	Executive summary PDF

Profiling Report Features

The nyc311_profile.html includes:

• 📈 Time series of complaint volume
• 📋 Top 10 complaint types
• 🗺️ Geographic distribution map
• ⏱️ Response time analysis
• 📊 Borough comparison
• 🕐 Hourly patterns
• 📉 Data quality statistics

All charts are embedded directly in the HTML - no separate image files!

Data Pipeline

Raw CSV → SQLite (raw_311) → SQL Cleaning → 311_cleaned → Profiling + Report

Key Findings

1. Brooklyn has the highest complaint volume (~118,864 requests)
2. HEAT/HOT WATER is the most common complaint type
3. Significant differences in response times across boroughs (p < 0.05)
4. Strong association between complaint types and boroughs (p < 0.001)

Statistical Analysis (in Notebook)

• Hypothesis Test 1: Two-sample t-test (Manhattan vs Brooklyn response times)
• Hypothesis Test 2: Chi-square test of independence (complaint type × borough)
• Correlation Analysis: Pearson and Spearman coefficients
• Regression: OLS model predicting response time

Requirements

• Python 3.9+
• See requirements.txt for dependencies

Author

Muhammad Muntazar Tasiu 20231725