electricsheepasia/asia-settlements-philippines-other-0-0-0-0-0-0-0-0-0-0-0

Philippines - Existing Evacuation Centers and Relocation sites(TS Washi Emergency)

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

Abstract

This dataset is about the Humanitarian Profile in the TS Washi Emergency. It shows the existing Evacuation Centers and Relocation sites in Cagayan de Oro and Iligan City in Mindanao, Philippines due to the Tropical Storm Washi Flashflood

Each row in this dataset represents tabular records. 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

Identifier / Metadata — unnamed_1 (range 1.0–108.0), unnamed_2 (range 0.0–57.0), unnamed_3 (EVACUATION CENTERS, Name, Bonbonon Brgy. Hall), unnamed_4 (range 0.0–10432000201.0), unnamed_5 (Brgy. Bulua, Brgy. Macasandig, Brgy. Kauswagan) and 8 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-settlements-philippines-other-0-0-0-0-0-0-0-0-0-0-0")
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 snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `departmentofsocialwelfareanddevelopment`. 10 exact duplicate rows were removed. 7 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 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.
• —The following columns have >20% missing values and should be treated with caution in modelling: unnamed_4, unnamed_6, unnamed_8.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_settlements_philippines_other_0_0_0_0_0_0_0_0_0_0_0,
  title     = {Philippines - Existing Evacuation Centers and Relocation sites(TS Washi Emergency)},
  author    = {OCHA Philippines},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/philippines-other-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0},
  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.