electricsheepafrica/africa-drc-displacement-forecasts

Future Displacement Forecasts

Publisher: Danish Refugee Council · Source: HDX · License: cc-by · Updated: 2026-02-22

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Abstract

Forecasts of forced displacement (IDPs, asylum seekers and refugees) one to three years into the future based on machine learning model.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-02-22. Geographic scope: AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 18 others.

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

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Dataset Characteristics

Domain	Forced displacement and migration
Unit of observation	Country-level aggregates
Rows (total)	26
Columns	13 (9 numeric, 4 categorical, 0 datetime)
Train split	20 rows
Test split	5 rows
Geographic scope	AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 18 others
Publisher	Danish Refugee Council
HDX last updated	2026-02-22

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Variables

Geographic — country_name (Afghanistan, Myanmar, Burundi), country_code (AFG, MMR, BDI).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-07).

Other — 2015 (range -0.9146–0.5809), 2016 (range -0.8845–0.7106), 2017 (range -57500000000.0–947000000000.0), 2018 (range -0.7326–0.8803), 2019 (range -0.8209–0.2595) and 4 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-drc-displacement-forecasts")
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
country_name	object	0.0%	Afghanistan, Myanmar, Burundi
country_code	object	0.0%	AFG, MMR, BDI
2015	float64	23.1%	-0.9146 – 0.5809 (mean -0.1645)
2016	float64	0.0%	-0.8845 – 0.7106 (mean -0.0236)
2017	float64	0.0%	-57500000000.0 – 947000000000.0 (mean 34211538461.4929)
2018	float64	0.0%	-0.7326 – 0.8803 (mean -0.0908)
2019	float64	0.0%	-0.8209 – 0.2595 (mean -0.116)
2020	float64	3.8%	-0.5663 – 0.2766 (mean -0.0508)
2021	float64	3.8%	-0.3136 – 0.4539 (mean 0.0041)
2022	float64	0.0%	-0.9192 – 0.4765 (mean -0.0141)
2023	float64	0.0%	-0.59 – 1.1355 (mean 0.0684)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-07

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Numeric Summary

Column	Min	Max	Mean	Median
2015	-0.9146	0.5809	-0.1645	-0.0711
2016	-0.8845	0.7106	-0.0236	-0.0053
2017	-57500000000.0	947000000000.0	34211538461.4929	-0.0107
2018	-0.7326	0.8803	-0.0908	-0.0225
2019	-0.8209	0.2595	-0.116	-0.0775
2020	-0.5663	0.2766	-0.0508	-0.007
2021	-0.3136	0.4539	0.0041	-0.0439
2022	-0.9192	0.4765	-0.0141	0.037
2023	-0.59	1.1355	0.0684	0.0066

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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. 5 column(s) with >80% missing values were removed: 2010, 2011, 2012, 2013, 2014. 1 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.

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Limitations

• Data originates from Danish Refugee Council 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: 2015.
• This dataset spans 26 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_drc_displacement_forecasts,
  title     = {Future Displacement Forecasts},
  author    = {Danish Refugee Council},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/drc-displacement-forecasts},
  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.