srinivaspavan9 / cis6930sp24-assignment3

CIS 6930, Spring 2024 Assignment 3 - Incident Analysis Dashboard

Author

Name: [Srinivas Pavan Singh Runval]
UFID: [93324706]

Introduction

Assignment 3 expands on the data processing of police incident records previously enriched in Assignment 2. The primary focus is to visualize the augmented data using a Streamlit dashboard, allowing interactive exploration of incident metrics such as the day of the week, time of day, weather conditions, location ranks, and nature of incidents. This dashboard serves as a practical application to demonstrate real-time data analysis capabilities in an interactive environment.

Running Instructions

To execute the Streamlit dashboard, follow the instructions below. Ensure Python 3 and required libraries (streamlit, pandas, matplotlib, seaborn, etc.) are installed.

1. Setup Environment:
   Ensure Python 3.6 or newer is installed along with pipenv for handling virtual environments and dependencies.

   pip install pipenv

2. Install Dependencies:
   Navigate to the project directory and install dependencies using:

   pipenv install

3. Activate Virtual Environment:
   Activate the virtual environment with:

   pipenv shell

   Alternatively, use pipenv run before commands to run them directly within the virtual environment.

4. Execute the Dashboard:
   Use the following command to run the Streamlit dashboard:

   pipenv run streamlit run your_dashboard_script.py

   Replace your_dashboard_script.py with the actual path to your Streamlit script.

Detailed Function Descriptions

The main functionalities included in the dashboard script are:

• process_data(urls_filename, progress_bar): Processes each URL from the given CSV file, downloads PDFs, extracts incidents, and augments data, updating a progress bar in the dashboard.
• display_data(df): Displays data and various interactive visualizations in the Streamlit dashboard.
• Visualization Components: Include pie charts, line charts, and bar charts to explore different aspects of the incident data such as day of the week, time of day, and weather conditions.

Bugs & Assumptions

• Bugs: No major bugs are known. Ensure all dependencies are correctly installed and API keys are set before execution.
• Assumptions: Assumes a stable internet connection for downloading PDFs and that the incident PDFs follow a uniform format for correct data extraction and visualization.

Resources

• Streamlit Documentation: [Link to Streamlit documentation]
• Pandas Documentation: [Link to Pandas documentation]
• Matplotlib and Seaborn for Visualization: [Link to Matplotlib], [Link to Seaborn]