311 Service Request Data Analysis

This project analyzes real-world 311 service request data using Python to identify trends in municipal issue reporting, evaluate response times, and visualize patterns across neighborhoods.

Overview

This project focuses on processing and analyzing public 311 service request datasets from Boston and San Francisco, which include reports on issues such as infrastructure problems, sanitation, and public services. By analyzing these datasets, the project uncovers patterns in how long cases remain open and how service requests vary by location.

Impact

This analysis demonstrates how data can be used to better understand city operations and community needs. By identifying trends in case duration and volume, this project highlights how municipalities can improve resource allocation and response efficiency in different neighborhoods.

Features

• Data processing and validation using structured datasets
• Sorting and ranking of service requests based on urgency and duration
• Statistical analysis of case volume and average resolution time by neighborhood
• Cross-dataset analysis using both Boston and San Francisco data
• Data visualizations including bar charts and geographic scatterplots

Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Pytest (for testing)

Data

The dataset used in this project is not included due to size constraints. The project demonstrates data processing and analysis logic using Python.

The datasets used include:

• Boston 311 Service Requests (data.boston.gov)
• San Francisco 311 Cases (data.sfgov.org)

These datasets contain information such as case category, location, status, and duration of service requests.

Why Visualization Matters

Data visualization was used to make patterns in the data more accessible and interpretable. Bar charts help compare case durations across neighborhoods, while geographic scatterplots simulate a map view of service requests. These visual tools allow trends and disparities to be identified more quickly than through raw data alone.

What I Learned

• How to work with large, real-world datasets using Pandas and NumPy
• Writing modular, reusable code for data analysis
• Comparing datasets across different cities to identify patterns
• Creating meaningful visualizations to communicate insights
• Applying testing and debugging techniques to ensure accuracy

Notes

This project was developed as part of coursework and demonstrates skills in data analysis, problem-solving, and Python programming.