---
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](https://data.humdata.org/dataset/emdat-country-profiles-khm) · **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](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/emdat-country-profiles-khm) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*