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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - affected-population
  - economics
  - fatalities
  - hxl
  - natural-disasters
  - khm
pretty_name: EM-DAT - Country Profiles, Cambodia
dataset_info:
  splits:
    - name: train
      num_examples: 24
    - name: test
      num_examples: 6

EM-DAT - Country Profiles, Cambodia

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

Abstract

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

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)

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

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

Dataset Characteristics

Domain Demographics and population
Unit of observation Country-level aggregates
Rows (total) 30
Columns 15 (7 numeric, 8 categorical, 0 datetime)
Train split 24 rows
Test split 6 rows
Geographic scope KHM
Publisher Centre for Research on the Epidemiology of Disasters
HDX last updated 2026-05-02

Variables

Geographic — year (range 2000.0–2025.0), country (Cambodia, #country +name), iso (KHM, #country +code), disaster_type (Flood, Storm, Drought), disaster_subtype (Riverine flood, Flood (General), Drought).

Demographic — total_damage_usd_original (range 100000.0–521000000.0), total_damage_usd_adjusted (range 174393.0–726560507.0).

Outcome / Measurement — total_events (range 1.0–3.0), total_affected (range 501.0–3448053.0), total_deaths (range 1.0–347.0).

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

Other — disaster_group (Natural, #cause +group), disaster_subroup (Hydrological, Meteorological, Climatological), cpi (range 54.8952–100.0).

Quick Start 

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
year float64 3.3% 2000.0 – 2025.0 (mean 2012.5172)
country object 0.0% Cambodia, #country +name
iso object 0.0% KHM, #country +code
disaster_group object 0.0% Natural, #cause +group
disaster_subroup object 0.0% Hydrological, Meteorological, Climatological
disaster_type object 0.0% Flood, Storm, Drought
disaster_subtype object 0.0% Riverine flood, Flood (General), Drought
total_events float64 3.3% 1.0 – 3.0 (mean 1.1724)
total_affected float64 20.0% 501.0 – 3448053.0 (mean 671057.1667)
total_deaths float64 43.3% 1.0 – 347.0 (mean 63.2941)
total_damage_usd_original float64 66.7% 100000.0 – 521000000.0 (mean 140710000.0)
total_damage_usd_adjusted float64 66.7% 174393.0 – 726560507.0 (mean 201038578.8)
cpi float64 6.7% 54.8952 – 100.0 (mean 73.5096)
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-05-06

Numeric Summary

Column Min Max Mean Median
year 2000.0 2025.0 2012.5172 2013.0
total_events 1.0 3.0 1.1724 1.0
total_affected 501.0 3448053.0 671057.1667 239045.5
total_deaths 1.0 347.0 63.2941 16.0
total_damage_usd_original 100000.0 521000000.0 140710000.0 54000000.0
total_damage_usd_adjusted 174393.0 726560507.0 201038578.8 83484700.5
cpi 54.8952 100.0 73.5096 73.7277

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.

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.

Citation 

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

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