electricsheepasia/asia-refugees-informal-settlements-refugees-living-in

Informal Settlements (Refugees living in informal settlements)

Publisher: OCHA Lebanon · Source: HDX · License: cc-by · Updated: 2025-05-05

Abstract

In Lebanon Syrian refugees are scattered all over the country, mostly living in the cities and villages with host communities renting or occupying abandoned or unfinished building, however over 30% of the refugees are living in tents in Informal Settlements. Informal Settlements are collections of tent (not more than 20 tents).

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-05-05. Geographic scope: LBN.

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

Dataset Characteristics

Variables

Geographic — inter_agency_mapping_platform (IAMP 34 (December) - Notes and Guidance, 1, 2).

Identifier / Metadata — unnamed_1 (Latest update date is 21th December 2016, GPS points are of differing accuracy because of the different satellite coverage achieved in the field, Coordinate system is WGS 1984), esa_source (HDX), esa_processed (2026-05-05).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-refugees-informal-settlements-refugees-living-in")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

No numeric columns.

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 exact duplicate rows were removed. 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 Lebanon 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_refugees_informal_settlements_refugees_living_in,
  title     = {Informal Settlements (Refugees living in informal settlements)},
  author    = {OCHA Lebanon},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/informal-settlements-refugees-living-in-informal-settlements},
  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.