electricsheepafrica/africa-cameroon-baseline-assessment-data-iom-dtm

Cameroon Displacement - [IDPs, Returnees, Refugees] - Baseline Assessment [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: cc-by · Updated: 2025-08-12

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Abstract

The dataset contains IDPs, Returnees and Refugees at sub national level with information on IDPs in camps and host communities. IOM set up and rolled out the first round of the DTM in November 2015 with the objective of providing regular, accurate and updated information on displaced populations within the Far North region of Cameroon to better support the response of the Government of Cameroon and the humanitarian community.

Each row in this dataset represents household-level survey responses. Data was last updated on HDX on 2025-08-12. Geographic scope: CMR.

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

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Dataset Characteristics

Domain	Forced displacement and migration
Unit of observation	Household-level survey responses
Rows (total)	1,424
Columns	16 (6 numeric, 10 categorical, 0 datetime)
Train split	1,139 rows
Test split	284 rows
Geographic scope	CMR
Publisher	International Organization for Migration (IOM)
HDX last updated	2025-08-12

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Variables

Geographic — total_inds_deplaces (range 0.0–26447.0), total_hh_deplaces (range 0.0–5916.0).

Demographic — village_code (XXXX, BOG-0001, WIN-0004), village_name (MADINA, NIGUE, ARDEBE), household_idps (range 1.0–5916.0), household_returnees (range 1.0–1798.0).

Identifier / Metadata — adm1_name (Extrême-Nord, #adm1+name), adm1_code (CMR004, #adm1+code), adm2_name (Logone-Et-Chari, Mayo-Tsanaga, Mayo-Danay), adm2_code (CMR004002, CMR004006, CMR004003), adm3_name (Makary, Logone-Birni, Mokolo) and 4 others.

Other — ind_returnees (range 1.0–13283.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cameroon-baseline-assessment-data-iom-dtm")
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
adm1_name	object	0.0%	Extrême-Nord, #adm1+name
adm1_code	object	0.0%	CMR004, #adm1+code
adm2_name	object	0.0%	Logone-Et-Chari, Mayo-Tsanaga, Mayo-Danay
adm2_code	object	0.0%	CMR004002, CMR004006, CMR004003
adm3_name	object	0.0%	Makary, Logone-Birni, Mokolo
adm3_code	object	0.0%	CMR004002009, CMR004002004, CMR004006002
village_code	object	0.1%	XXXX, BOG-0001, WIN-0004
village_name	object	0.1%	MADINA, NIGUE, ARDEBE
household_idps	float64	37.7%	1.0 – 5916.0 (mean 96.4915)
ind_idps	float64	37.7%	3.0 – 16623.0 (mean 575.9357)
household_returnees	float64	67.4%	1.0 – 1798.0 (mean 92.6293)
ind_returnees	float64	67.4%	1.0 – 13283.0 (mean 638.7823)
total_inds_deplaces	float64	0.1%	0.0 – 26447.0 (mean 599.766)
total_hh_deplaces	float64	0.1%	0.0 – 5916.0 (mean 96.123)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-15

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Numeric Summary

Column	Min	Max	Mean	Median
household_idps	1.0	5916.0	96.4915	28.0
ind_idps	3.0	16623.0	575.9357	182.0
household_returnees	1.0	1798.0	92.6293	30.5
ind_returnees	1.0	13283.0	638.7823	191.5
total_inds_deplaces	0.0	26447.0	599.766	160.0
total_hh_deplaces	0.0	5916.0	96.123	26.0

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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. 3 column(s) with >80% missing values were removed: new_village_for_this_round, household_refugees, ind_refugees. 4 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 International Organization for Migration (IOM) 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: household_idps, ind_idps, household_returnees, ind_returnees.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_cameroon_baseline_assessment_data_iom_dtm,
  title     = {Cameroon Displacement - [IDPs, Returnees, Refugees] - Baseline Assessment [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/cameroon-baseline-assessment-data-iom-dtm},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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