Bullets Over Broadway

With Shootings skyrocketing over the streets of NY, we dive deep into the data provided by NYCOpendata to understand patterns in these shootings to understand the problem better.

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Team ByteBridgers presents Bullets Over Broadway

built on ☕ for Youth Data Hack.

💡 Problem Statement

Analyze a public dataset to gain insights into a social, environmental, or economic issue and develop a data driven solution.

🧠 Knowledge Primer

Dataset

The analysis is based on the NYPD Shooting Incident Data (Historic) dataset. The data is loaded into a Pandas DataFrame, revealing insights into various aspects of shooting incidents.

Exploratory Data Analysis (EDA)

The EDA includes the use of Python libraries such as Pandas, Matplotlib, Seaborn and Folium for visualization and geospatial analysis.

Key Visualizations

  1. Shootings by Borough:

    • Bar chart illustrating the number of shootings in each borough.

  2. Shootings Over Time:

    • Line chart depicting the trend of shootings over time on a monthly basis.

  3. Perpetrator Age Group Distribution:

    • Bar chart showing the distribution of shootings based on perpetrator age groups.

  4. Perpetrator Gender and Race Distribution:

    • Stacked bar charts illustrating the distribution of shootings based on perpetrator gender and race.

  5. Victim Age, Gender and Race Distributions:

    • Stacked bar charts showing the distribution of shootings based on victim age, gender and race.

  6. Shootings Resulting in Murder:

    • Pie chart indicating the proportion of shootings resulting in murder.

  7. Geospatial Analysis:

    • Folium map with a heatmap indicating the locations of shooting incidents.

  8. Cross-tabulations:

    • Tables showing relationships between variables such as perpetrator race vs. victim race, perpetrator age group vs. victim age group and perpetrator gender vs. victim gender.

  9. Additional Analysis:

    • Various charts analyzing shootings by time of day, day of the week and top precincts.

Clustering Analysis

The knowledge primer concludes with a clustering analysis using K-Means clustering. The analysis explores patterns based on precinct, location, time of day and day of the week.

📺 Preview

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💻 Tech Stack / Libraries

  • Next.js
  • JavaScript
  • Leaflet
  • Jupyter Notebook
  • Pandas
  • Matplotlib
  • Seaborn
  • Folium
  • Scikit-learn (sklearn)
  • NumPy

📜 License

Bullets Over Broadway is available under the MIT license. See the LICENSE file for more info.

💥 Contributors

Contributors Contributors

🚨 Forking this repo

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

yarn dev
# or
npm run dev
# or
pnpm dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.js. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Inter, a custom Google Font.

Conclusion

The provided Python code and visualizations offer a comprehensive understanding of the NYPD shooting incident data, providing valuable insights for addressing our problem statement for the Youth Data Hack event.

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