Academic · Geospatial Analytics · Sustainability

Flood Risk Vulnerability Mapping: Trincomalee & Galle

NUS MSc Data Science for Sustainability · Geospatial Analytics for Disaster Risk Reduction · Group project · AY 2025/26

A comparative geospatial risk analysis of two Sri Lankan coastal districts affected by the 2004 Indian Ocean tsunami.

At a glance

Question: How coastal flood-risk and infrastructure-exposure patterns differ between Trincomalee and Galle.
Classification: Academic · Geospatial Analytics · Sustainability
Framework: Weighted index followed by rule-based classification.
Team context: Group project; individual contribution not publicly specified.

This was a team-based academic project. I contributed to the geospatial workflow, comparative district analysis, map-based evidence generation, and interpretation of findings.

Research question

How do coastal flood-risk and infrastructure-exposure patterns differ between Trincomalee and Galle, two Sri Lankan districts affected by the 2004 Indian Ocean tsunami?

Comparison frame

Same analytical frame
Both districts were assessed within 10 km coastal buffers using standardized raster analysis.
Different coastal contexts
The comparison keeps the method consistent while examining how hazard, vulnerability and development patterns vary by district.

Why Trincomalee vs Galle

The project treats the districts as a matched comparison: the same 10 km coastal-buffer scope, analytical resolution and indicator framework make differences in risk-zone and exposure patterns easier to examine without reducing either district to a single danger ranking.

Visual context

Context

Study area map showing Trincomalee and Galle in Sri Lanka — **Study Area – Trincomalee & Galle**Shows the two district study areas used for the comparative analysis.

Data & geospatial workflow

EPSG:32644 · 30 m analytical resolution · 10 km coastal buffer

All raster analysis was standardized around a common projected coordinate system and analytical resolution. The workflow moved from data ingestion through reprojection and resampling to hazard and vulnerability indicators, weighted risk, classification, exposure analysis, spatial autocorrelation and interpretation.

ESA WorldCover 2021 is used as a proxy for built-up exposure; it is not actual 2025 infrastructure data.

Processing

Google Earth Engine
Python
Jupyter

GIS / Cartography

QGIS

Data

Landsat 5
SRTM DEM
HydroSHEDS
JRC Global Surface Water
ESA WorldCover 2021
WorldPop 2020
OpenStreetMap
LSIB coastline/boundaries

Two-stage risk framework

Stage 1 · Weighted index

A weighted index combined hazard and vulnerability indicators.

Hazard: elevation, coast distance, historical inundation proxy, TWI and slope.

Vulnerability: population density, settlement type, road access and hospital access.

Stage 2 · Rule-based classification

Low, Medium and High classes were assigned using rule-based physical thresholds informed by coastal-risk literature.

Visual evidence

Flood-risk patterns

Flood risk zones in Trincomalee District — **Flood Risk · Trincomalee**Low, Medium and High risk zones across the Trincomalee coastal study area.

Flood risk zones in Galle District — **Flood Risk · Galle**Low, Medium and High risk zones across the Galle coastal study area.

Interactive district comparison

Select a district to compare verified risk, built-up exposure, vulnerability and spatial-clustering values. This is a lightweight comparison module, not a live GIS engine.

Infrastructure exposure

Using the ESA WorldCover 2021 built-up class as a proxy, Galle has substantially more of its built-up proxy in the combined Medium/High classes. Trincomalee has the larger share specifically in the High-risk class. These patterns describe the proxy distribution, not actual 2025 infrastructure and not a simple ranking of which district is more dangerous.

Flood risk and coastal infrastructure in Trincomalee — **Infrastructure exposure · Trincomalee**Built-up areas are shown as a proxy over the district risk zones.

Flood risk and coastal infrastructure in Galle — **Infrastructure exposure · Galle**Built-up areas are shown as a proxy over the district risk zones.

Visual evidence

Population exposure

Flood risk and coastal population density in Trincomalee — **Population exposure · Trincomalee**Population-density symbols are overlaid on the district risk zones.

Flood risk and coastal population density in Galle — **Population exposure · Galle**Population-density symbols are overlaid on the district risk zones.

Spatial clustering

The project used Global Moran's I for overall risk clustering, Bivariate Moran's I for co-location between risk and built-up exposure, LISA hotspot analysis and factor decomposition. The bivariate results indicate statistically significant spatial co-location in both districts.

Methods

Global Moran's I
Measures spatial clustering of the risk index.
Bivariate Moran's I
Tests co-location between risk and the built-up exposure proxy.
LISA & decomposition
Locates hotspots and examines indicator contributions.

Focused evidence

Focused case evidence

Flood risk and coastal railway corridor in Galle — **Railway corridor · Galle**Shows the coastal railway corridor and Peraliya disaster site in relation to the mapped risk zones.

Key findings

Galle has the larger Medium-risk share and the larger combined Medium/High built-up proxy share.
Trincomalee has the larger share specifically in the High-risk class.
Galle's mean Vulnerability Index is higher, with population-density contribution particularly associated with that difference.
Both districts show clustered risk and statistically significant spatial co-location between risk and the built-up exposure proxy.

Limitations

The Landsat-derived historical inundation proxy substantially under-detected the actual 2004 tsunami extent and therefore contributed near-zero to the weighted score.
ESA WorldCover 2021 is only a proxy for later built-up exposure and may undercount informal coastal structures.
Shared indicator weights across both districts may miss region-specific risk drivers.

What I would explore next

Test region-specific indicator weights against the shared-weight baseline.
Improve historical inundation reconstruction with a better-validated event proxy.
Evaluate higher-resolution built-up exposure data, including informal coastal structures where available.

Artifacts

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