electricsheepasia/asia-population-emdat-country-profiles-malaysia

EM-DAT - Country Profiles, Malaysia

Publisher: Centre for Research on the Epidemiology of Disasters · Source: HDX · License: hdx-other · Updated: 2026-05-02

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Abstract

Aggregated figures for natural hazard related events in EM-DAT: Malaysia

Documentation on the Country Profiles available here

How to cite the EM-DAT Project here

Main dataset on HDX: EM-DAT - Country Profiles

More on the EM-DAT database : website / data portal

Each line corresponds to a given combination of year, country, disaster subtype and reports figures for :

• number of disasters
• total number of people affected
• total number of deaths
• economic losses (original value and adjusted)

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Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-05-02. Geographic scope: MYS.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Country-level aggregates
Rows (total)	44
Columns	15 (7 numeric, 8 categorical, 0 datetime)
Train split	35 rows
Test split	8 rows
Geographic scope	MYS
Publisher	Centre for Research on the Epidemiology of Disasters
HDX last updated	2026-05-02

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Variables

Geographic — year (range 2000.0–2025.0), country (Malaysia, #country +name), iso (MYS, #country +code), disaster_type (Flood, Storm, Mass movement (wet)), disaster_subtype (Riverine flood, Flood (General), Flash flood).

Demographic — total_damage_usd_original (range 2000.0–1460000000.0), total_damage_usd_adjusted (range 2647.0–1690173645.0).

Outcome / Measurement — total_events (range 1.0–5.0), total_affected (range 6.0–2200000.0), total_deaths (range 1.0–80.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-06).

Other — disaster_group (Natural, #cause +group), disaster_subroup (Hydrological, Meteorological, Geophysical), cpi (range 54.8952–97.135).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-population-emdat-country-profiles-malaysia")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
year	float64	2.3%	2000.0 – 2025.0 (mean 2011.4651)
country	object	0.0%	Malaysia, #country +name
iso	object	0.0%	MYS, #country +code
disaster_group	object	0.0%	Natural, #cause +group
disaster_subroup	object	0.0%	Hydrological, Meteorological, Geophysical
disaster_type	object	0.0%	Flood, Storm, Mass movement (wet)
disaster_subtype	object	0.0%	Riverine flood, Flood (General), Flash flood
total_events	float64	2.3%	1.0 – 5.0 (mean 1.8837)
total_affected	float64	6.8%	6.0 – 2200000.0 (mean 85260.1707)
total_deaths	float64	34.1%	1.0 – 80.0 (mean 15.1034)
total_damage_usd_original	float64	72.7%	2000.0 – 1460000000.0 (mean 282170166.6667)
total_damage_usd_adjusted	float64	72.7%	2647.0 – 1690173645.0 (mean 383144853.8333)
cpi	float64	6.8%	54.8952 – 97.135 (mean 70.9832)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-06

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Numeric Summary

Column	Min	Max	Mean	Median
year	2000.0	2025.0	2011.4651	2011.0
total_events	1.0	5.0	1.8837	1.0
total_affected	6.0	2200000.0	85260.1707	12610.0
total_deaths	1.0	80.0	15.1034	10.0
total_damage_usd_original	2000.0	1460000000.0	282170166.6667	16000000.0
total_damage_usd_adjusted	2647.0	1690173645.0	383144853.8333	25420445.5
cpi	54.8952	97.135	70.9832	71.7077

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 5 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Centre for Research on the Epidemiology of Disasters and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: total_deaths, total_damage_usd_original, total_damage_usd_adjusted.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_population_emdat_country_profiles_malaysia,
  title     = {EM-DAT - Country Profiles, Malaysia},
  author    = {Centre for Research on the Epidemiology of Disasters},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/emdat-country-profiles-mys},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.