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Data Collection & Geo Spatial Mapping

Understanding geographic outbreaks through case clustering and hotspot detection. Enter case reports or use dummy data to visualize the spread of public health events.

Focus Region

Spatial Data Entry

Simulate an outbreak or upload incident reports.

ID Type Lat Long State
No data loaded

Clustering Algorithm

1.00 km

Choropleth Designer

Analyze healthcare disparities and population metrics through geographic scaling.

Legend

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Add Spatial Markers

Build custom maps with personalized markers overlayed on base layers.

Add Location

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Bulk Options

Algorithm Insights & Spatial Logic

Core Clustering Logic

Utilizes Proximity-Linked Aggregation (DBSCAN logic) to identify high-density clusters.

  • 📍 Spatial Join: Pairs points within threshold.
  • 📏 Threshold: Clusters only form if points overlap.
  • 🔥 Hotspot: Confirmed when density reaches min. 3 points.

Technical Execution

  1. Distance Matrix: Haversine formula calculation.
  2. Neighbor Search: K-D Tree style radius search.
  3. Density Check: Core point identification.
  4. Expansion: Recursive cluster growth.
EPIDEMIOLOGICAL VALUE

Spatial clustering identifies Shared Exposure Points. By pinpointing hotspots, health teams can deploy targeted interventions effectively.

Adjust Distance Threshold for street vs. city-wide trends.

Choropleth Mapping Guide

Upload an Excel/CSV file with a 'Region' column (State/District/Sub-District name) and a 'Value' column. The map will automatically scale colors based on your data range.

Field Mapping

Select corresponding columns from your CSV.