disaster-recovery-analysis

Data Operations and Transformations

This document details the data engineering, cleaning, and merging operations performed to construct the computational indicators for disaster resilience, primarily focusing on the Disaster Impact Index (DII) and the Resilience Recovery Score (RRS). These operations prepared the final analytical datasets used in the visualisations.

1. Disaster Impact Index (DII) Operations

The operations for DII were executed in DII/DataCleaningDII.ipynb to standardise natural disaster records and integrate them with macroeconomic and demographic indicators.

Standardisation

Date Formatting: Extracted and standardised Year, Start Month/Day, and End Month/Day into unified startDate and endDate columns.
Geospatial Formatting: Created a spatial coordinates column by combining Latitude and Longitude.
Population Data Normalisation: Unified the population estimation columns from the UN World Population Prospects dataset and filtered out future projection years to keep only historical data up to 2024.

Data Merging & Feature Engineering

Macroeconomic Integration: Merged the cleaned disaster records with “GDP per capita (PPP)” data using an inner join on Country Code and Year.
Demographic Integration: Merged the dataset with the cleaned population data.
Affected Population Metric: Computed a new feature, affected_population, derived by calculating the percentage of Total Deaths relative to the total national population.
Final Output: The resulting dataset (DII_v5.csv) contained fully integrated event, economic, and demographic data.

2. Resilience Recovery Score (RRS) Operations

The operations for RRS were executed in RRS/DataCleaningRRS.ipynb to model the post-disaster GDP rebound dynamics and integrate them with institutional and human development scores.

Standardisation

Governance Metrics: Standardised the Worldwide Governance Indicators (WGI). Shifted the GovIndex by adding 2.5 and scaling by 20. Converted standard errors into percentage values (ErrorPercent).
Human Development Index (HDI): Converted the HDI values to a percentage scale (HDI_percent).
Geodata Enrichment: Mapped standard ISO-3 country codes to full country names using the pycountry library.

Macroeconomic Integration & Recovery Engineering

Initial Merging: Performed sequential inner joins aligning Governance Index, GDP per capita annual growth, and HDI data by ISO code and Year.
Recovery Time Calculation:
- Computed a pre-disaster GDP growth baseline using the average growth of the 3 years preceding each disaster.
- Calculated recovery_years, representing the number of years required for post-disaster GDP growth to return to or exceed the pre-disaster baseline. Non-recovering states were capped at the current year differential.
Pre & Post GDP Averages: Engineered GDP_GrowthPre (3-year mean before disaster) and GDP_GrowthPost (3-year mean after disaster).

RRS Computation

The final Resilience Recovery Score (RSS) was computed using the formal project equation: RSS = ((GDP_GrowthPost - GDP_GrowthPre) / recovery_years) + ((HDI + GovIndex_ErrorPercent) / 2) Missing values across mandatory macroeconomic and institutional features were dropped, resulting in RSS_v9.csv.

3. Final Dataset Integration

To produce the final master dataset for interactive visual analysis, DII_v5.csv and RSS_v9.csv were row-synchronised:

Filtered records to post-1998 events.
Generated intra-year row identifiers (ID) to account for multiple disasters per country per year.
Merged via inner join on Year, Country, and ID, culminating in RSS_v11.csv (7,038 valid disaster-year observations).

Explore the results of these data operations in our interactive visualisations:

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