ResQVision - Emergency Analytics

Emergency response management systems generate vast amounts of data — incident types, dispatch times, resource statuses, environmental conditions — but much of this data remains underutilized due to fragmented systems and limited analytic capabilities. Existing dashboards often lack multi-dimensional analysis, incident-level granularity, and dynamic exploration features. Critical questions — such as how weather impacts response times, or how ambulance availability correlates with delays — remain difficult to answer using static reports. Our challenge was to build an interactive, user-friendly dashboard that would surface these hidden relationships, empowering decision-makers to act proactively.

We designed ResQVision with a user-centered philosophy focused on clarity, usability, and operational relevance. The dashboard is organized into three modules — Incident Trends, Response Efficiency, and Environmental Impact — each targeting a specific layer of emergency response analysis. We prioritized intuitive interaction models, responsive filtering, and high information density without overwhelming the user. Clear legends, dynamic tooltips, and export functionalities were added to support exploratory analysis while maintaining an accessible learning curve for non-technical users.

• Data Engineering:
  

  Preprocessing emergency incident data using Python to derive temporal fields (Month, Day of Week, Hour) and enrich records with environmental and infrastructural variables (Weather Condition, Traffic Congestion, Road Type).

• Visualization Stack:
  

  Built interactive visualizations using D3.js and React.js, ensuring smooth transitions, dynamic scaling, and modular component structures.

• Dashboard Design:
  

  implemented three distinct dashboards:

  1. Incidents Dashboard (Bar charts, Time Series)
  2. Response Analysis Dashboard (Grouped Bar Chart, Line Chart, Heatmap)
  3. Weather Impact Dashboard (Heatmap)

  All dashboards were equipped with global filters (Region, Time Range, Severity, Traffic) to enable comparative and drill-down analysis.

• Deployment:
  

  Hosted the live dashboard on Netlify for easy access and demonstration. User feedback was collected via an embedded survey to iteratively refine user experience.

ResQVision successfully transforms complex emergency system data into a cohesive, exploratory environment where users can:

• Identify trends in emergency type and severity across time and regions,
• Assess the operational impact of ambulance shortages, road types, and injury scale on response time,
• Uncover how environmental factors like bad weather and traffic congestion exacerbate delays.

The project serves as a strong proof-of-concept for real-world EMS decision support systems. User feedback indicated high scores for ease of navigation, clarity of insights, and potential usefulness in operational and policy settings.