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

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

Dataset Characteristics

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.

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()

Schema

Numeric Summary

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.

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.

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)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.