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Sao Tome and Principe/datacard_Urban_Disaster_Risk_Resilience_and_Land.md

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+# Datacard for Sao Tome and Principe Urban_Disaster_Risk_Resilience_And_Land Indicators (1960-2024)
+
+This dataset contains a time-series of key urban_disaster_risk_resilience_and_land indicators for Sao Tome and Principe, spanning from 1960 to 2024. The data has been aggregated from multiple sources, cleaned, and processed into a single, analysis-ready CSV file.
+
+The raw data was sourced from **The World Bank** data portal. The original files were provided in Excel (.xls) format.
+
+- **Temporal Coverage**: 1960-2024
+- **Geographic Coverage**: Sao Tome and Principe
+- **Format**: Comma-Separated Values (CSV)
+
+---
+
+## Data Points (Features)
+
+The dataset includes the following urban_disaster_risk_resilience_and_land indicators, with 'Year' serving as the primary date column:
+
+1. `internally_displaced_persons_new_displacement_associated_with_disasters_number_of_cases_`: Internally displaced persons, new displacement associated with disasters (number of cases)
+2. `urban_land_area_where_elevation_is_below_5_meters_sq_km_`: Urban land area where elevation is below 5 meters (sq. km)
+3. `urban_population_of_total_population_`: Urban population (% of total population)
+
+---
+
+## Data Preparation & Missing Data Handling
+
+The raw data was processed using a Python script to transform it into a clean, structured format. The key steps were:
+
+1.  **Filtering**: The data was filtered to include only records for 'Sao Tome and Principe'.
+2.  **Reshaping**: The original wide-format data (years as columns) was melted into a long format.
+3.  **Merging**: Data from all indicator files was merged into a single DataFrame on 'Year'.
+4.  **Handling Missing Data**: Missing values (`NaN`) were filled using a two-step strategy: linear interpolation followed by a back-fill to handle any remaining gaps at the start of the series.