electricsheepasia/asia-displacement-philippines-affected-persons-locations

Philippines - Affected Persons Locations

Publisher: OCHA Philippines · Source: HDX · License: hdx-other · Updated: 2023-05-02

Abstract

Priority municipalities: Within Yolanda'S 50Km Radius Haiyan (Yoland) corridor: Data on Displaced Families And Persons Located Within 50Km Radius of Yolandas Data identifies the 171 priority municipalities identified by the government. Data was received 3 Dec 2013 as PDF from DSWD and converted to excel. Pcodes (PSGC from government) were added to be able to link the data to spatial files for mapping

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2023-05-02. Geographic scope: PHL.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — region (Region  VI, REGION  VIII, REGION  VII), province (LEYTE, ILOILO, CAPIZ), municipality (San  Remegio, Pilar, Busuanga), population (range 6228.0–552936.0), numberoffamily (range 1325.0–58823.0) and 4 others.

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

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-displacement-philippines-affected-persons-locations")
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 OCHA Philippines 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_displacement_philippines_affected_persons_locations,
  title     = {Philippines - Affected Persons Locations},
  author    = {OCHA Philippines},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/philippines-affected-persons-locations},
  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.