Team ByteBridgers presents Bullets Over Broadway
built on ☕ for Youth Data Hack.
Analyze a public dataset to gain insights into a social, environmental, or economic issue and develop a data driven solution.
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.
The EDA includes the use of Python libraries such as Pandas, Matplotlib, Seaborn and Folium for visualization and geospatial analysis.
-
Shootings by Borough:
- Bar chart illustrating the number of shootings in each borough.
-
Shootings Over Time:
- Line chart depicting the trend of shootings over time on a monthly basis.
-
Perpetrator Age Group Distribution:
- Bar chart showing the distribution of shootings based on perpetrator age groups.
-
Perpetrator Gender and Race Distribution:
- Stacked bar charts illustrating the distribution of shootings based on perpetrator gender and race.
-
Victim Age, Gender and Race Distributions:
- Stacked bar charts showing the distribution of shootings based on victim age, gender and race.
-
Shootings Resulting in Murder:
- Pie chart indicating the proportion of shootings resulting in murder.
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Geospatial Analysis:
- Folium map with a heatmap indicating the locations of shooting incidents.
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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.
-
Additional Analysis:
- Various charts analyzing shootings by time of day, day of the week and top precincts.
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.
- Next.js
- JavaScript
- Leaflet
- Jupyter Notebook
- Pandas
- Matplotlib
- Seaborn
- Folium
- Scikit-learn (sklearn)
- NumPy
Bullets Over Broadway
is available under the MIT license. See the LICENSE
file for more info.
This is a Next.js project bootstrapped with create-next-app
.
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.
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.