Requests by District

What this shows

A bar chart of the total number of support requests received from each district.

Key insights

Identifies which districts reported the highest number of help requests.
Highlights where overall demand for relief was strongest.
Helps prioritise district-level resource allocation and planning.

Production-ready emergency response system with real-time flood monitoring, relief coordination, and volunteer management for Sri Lanka.

→

District × Urgency

What this shows

A stacked bar chart showing how many low, medium, high, and emergency requests came from each district.

Key insights

Separates “busy” districts from those with more life-threatening cases.
Reveals where emergency and high-urgency situations were concentrated.
Supports decisions on where rescue teams and medical units should be prioritised.

Assistance Totals

What this shows

A bar chart of how many times each assistance type (food, water, medicine, boats, etc.) was requested.

Key insights

Shows which types of support were most in demand during the flood.
Helps design standard relief packages and stockpiling strategies.
Highlights hidden needs like hygiene products or baby items.

Needs by District

What this shows

A grouped bar view of the most common assistance types requested in each district.

Key insights

Reveals how needs are different from district to district.
Shows which districts needed more medical help, which needed more food/water, and which needed rescue.
Supports customised, district-specific response planning.

Urgency vs Needs

What this shows

A heatmap of urgency levels (low/medium/high/emergency) against assistance types.

Key insights

Highlights which needs are most associated with emergency situations (e.g. rescue, boats, evacuation).
Shows which needs appear more in medium or low urgency (e.g. cleaning, clothing, repairs).
Helps design emergency vs recovery response bundles.

Geo Scatter Map

What this shows

A geographic scatter plot of individual requests, using latitude and longitude and coloured by urgency.

Key insights

Makes visible the spatial distribution of people requesting help.
Identifies localised hotspots of distress within and across districts.
Supports planning of rescue routes, shelters, and medical deployments.

Density Heatmap

What this shows

A heatmap layer over the map, where brighter areas indicate higher (and more urgent) concentrations of requests.

Key insights

Turns individual points into clear “pressure zones”.
Helps find the most intense clusters of need for focused response.
Useful for long-term planning of flood defences and shelters in repeat hotspot areas.

District Needs Heatmap

What this shows

A heatmap of districts versus assistance types, showing how often each type of help was requested in each district.

Key insights

Provides a “needs fingerprint” for every district.
Shows which districts are more medical-heavy, food-heavy, or rescue-heavy.
Supports long-term district-wise disaster preparedness and stock strategies.

KMeans Clusters

What this shows

A clustering view where similar requests are grouped based on needs, urgency, and location.

Key insights

Reveals typical “types” of cases (e.g. rescue clusters, medical clusters, basic-needs clusters).
Helps design task-specific response teams for each type of situation.
Can support future automated triage by matching new cases to existing clusters.

About ResQ Hub

ResQ Hub

This dashboard visualises and analyses requests for support made during a recent flood situation in Sri Lanka. Each point and bar in these graphs represents real households or locations asking for help from communities, government agencies, and supporting organisations.

Flood Support Analytics Dashboard

For the best viewing experience and clearer interaction with the analytical charts, please use this dashboard on a laptop or desktop screen.

About me

Developed and Analysed by: Chanupa Athsara
Email: athsara141@gmail.com

This project combines data cleaning, geospatial processing, and visual analytics to understand where needs arose, what types of help were requested, and how severe the situations were across districts.

Data & Methods

Python Pandas Plotly Folium Scikit-learn

Cleaned raw records, standardised city names, and mapped them to districts in Sri Lanka.
Converted assistance types into one-hot encoded columns (0/1) for analysis of needs.
Analysed urgency levels (low, medium, high, emergency) to understand severity.
Used latitude and longitude for geospatial mapping and hotspot detection.
Applied clustering and basic modelling to explore patterns in needs and urgency.

How to Read This Dashboard

Use the tabs above to switch between different analytical views:

Requests by District: Overall volume of requests per district.
District × Urgency: How severe the situation was in each district.
Assistance Totals: What people needed the most overall (food, water, medicine, etc.).
Needs by District: Which types of help were requested in each district.
Urgency vs Needs: Which needs are associated with emergency vs lower urgency.
Geo Scatter & Density Heatmap: Where people were located geographically.
District Needs Heatmap: A “fingerprint” of needs for each district.
KMeans Clusters: Groups of similar requests based on needs, urgency, and location.

Usage & Licence

This dashboard is intended for learning, humanitarian analysis, and planning discussions. It should not be used to identify individuals or share sensitive personal information. Any real deployments should follow proper data protection, privacy, and ethical guidelines.

You can explore more of my work and get in touch through: