electricsheepasia/asia-impact-data-casualties-and-damage-typhoon-haiyan-yolanda

Impact data - casualties and damage - Typhoon Haiyan (Yolanda)

Publisher: Netherlands Red Cross - 510 · Source: HDX · License: cc-by · Updated: 2021-09-23

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Abstract

Counts of damage and casualties from official data sets

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2021-09-23. Geographic scope: PHL.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	170
Columns	18 (8 numeric, 10 categorical, 0 datetime)
Train split	136 rows
Test split	34 rows
Geographic scope	PHL
Publisher	Netherlands Red Cross - 510
HDX last updated	2021-09-23

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Variables

Geographic — province (LEYTE , ILOILO , CAPIZ), municipality (SAN REMEGIO , STA. FE , PILAR), displaced_families (range 0.0–55908.0), displaced_people (range 0.0–276509.0), partially_damaged (range 0.0–46553.0) and 1 others.

Demographic — total_houses_damaged (range 72.0–58823.0).

Identifier / Metadata — unnamed_0 (range 0.0–169.0), unnamed_2 (range 1325.0–58823.0), unnamed_3 (range 6228.0–552936.0), l1_name (LEYTE , ILOILO , CAPIZ), l2_name (SAN REMEGIO , STA. FE , PILAR) and 6 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-impact-data-casualties-and-damage-typhoon-haiyan-yolanda")
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
unnamed_0	int64	0.0%	0.0 – 169.0 (mean 84.5)
province	object	0.0%	LEYTE , ILOILO , CAPIZ
municipality	object	0.0%	SAN REMEGIO , STA. FE , PILAR
unnamed_2	int64	0.0%	1325.0 – 58823.0 (mean 8627.4647)
unnamed_3	int64	0.0%	6228.0 – 552936.0 (mean 41674.9235)
displaced_families	int64	0.0%	0.0 – 55908.0 (mean 5401.5353)
displaced_people	int64	0.0%	0.0 – 276509.0 (mean 25733.2294)
total_houses_damaged	int64	0.0%	72.0 – 58823.0 (mean 5957.5882)
partially_damaged	int64	0.0%	0.0 – 46553.0 (mean 2905.3647)
totally_damaged	int64	0.0%	20.0 – 14132.0 (mean 3052.2235)
l1_name	object	0.0%	LEYTE , ILOILO , CAPIZ
l2_name	object	0.0%	SAN REMEGIO , STA. FE , PILAR
l1_best_match_name	object	6.5%	LEYTE, ILOILO, AKLAN
l2_best_match_name	object	6.5%	SAN REMIGIO, SANTA FE, CORON
l1_code	object	0.0%	PH083700000, PH063000000, PH061900000
l2_code	object	0.0%	PH175302000, PH175307000, PH175309000
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-04

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_0	0.0	169.0	84.5	84.5
unnamed_2	1325.0	58823.0	8627.4647	6975.5
unnamed_3	6228.0	552936.0	41674.9235	32087.5
displaced_families	0.0	55908.0	5401.5353	4249.5
displaced_people	0.0	276509.0	25733.2294	20720.0
total_houses_damaged	72.0	58823.0	5957.5882	4325.0
partially_damaged	0.0	46553.0	2905.3647	1977.5
totally_damaged	20.0	14132.0	3052.2235	2303.0

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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. 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 Netherlands Red Cross - 510 and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_impact_data_casualties_and_damage_typhoon_haiyan_yolanda,
  title     = {Impact data - casualties and damage - Typhoon Haiyan (Yolanda)},
  author    = {Netherlands Red Cross - 510},
  year      = {2021},
  url       = {https://data.humdata.org/dataset/impact-data-casualties-and-damage-typhoon-haiyan-yolanda},
  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.