New York City shooting incident data visualization

NYC Shooting Incidents Dashboard

This project analyses shooting incidents in New York City, using rich data sourced from the NYC Open Data Platform.

I undertook this project early in my career as a data analyst to gain hands-on experience working with real-world data. Crime data intrigued me because of its complexity and social importance, and New York City’s open data portal provided an excellent, rich dataset to explore. Visualising this data using Tableau helped me understand the patterns of shootings across the city and sharpen my skills with the tool. It remains one of my most formative and rewarding projects.

Project Highlights

  • Interactive data on where and when shootings occur, as well as visualising shooting rates over time
  • Insights into victim and perpetrator demographics
  • Mapping clusters and hotspots of shooting incidents
  • Dynamic dashboard enabling user-driven exploration

Access the Dashboard

Explore the full dataset and interactive visualisations using the official Tableau dashboard linked below.

NYPD Shooting Incident Data visualisation

Sourcing the Data

For this project, I tapped into the NYPD Shooting Incident Historic dataset via New York City’s OpenData API. After a few light transformations in Power Query to shape the data, I connected it directly to Tableau for visual exploration.

The dataset itself is a rich, citywide record of every shooting incident that has occurred in New York City from 2006 through to the end of the most recent calendar year. Each record includes detailed information about the event, such as time, location, and type of incident, along with demographic information about both victims and suspects.

Visualizing crime data

Visualising Crime Data

To better understand the patterns behind shootings in New York, I approached the data by asking four key questions: where, when, who, and why.

Where focused on mapping the locations of individual incidents. By plotting them geographically, it became easier to identify neighborhoods with persistent violence and spot emerging hotspots over time.

When revealed some of the most striking patterns. I broke the data down by time of day, day of the week, and season. Weekends — especially summer weekends — consistently showed spikes in violent incidents. I used Tableau’s reference band feature to highlight the summer months across line charts, clearly showing how violence surges during that time, particularly at night.

Who involved exploring details about both victims and perpetrators. By segmenting the data demographically, I was able to uncover important insights into which groups were most at risk and which areas needed targeted interventions.

Why is the most complex question — but I started exploring it by bringing in additional datasets. I looked at borough-level statistics like school dropout rates and median income to look for socioeconomic correlations. While causality is hard to prove, the overlap between poverty, education gaps, and violence provided compelling angles for further analysis.

Altogether, these visualisations helped turn raw incident reports into meaningful insights about the systemic nature of urban violence.