electricsheepafrica/africa-displacement-benin

Benin Displacement - [IDPs] - Mobility Tracking [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2025-08-08

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Abstract

Due to Sahel Crisis, IOM DTM conducted mobility tracking in Alibori and Atacora departments in Benin. The datasets show the total number of IDP individuals identified and the origin of the majority of IDPs.

Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the heure_de_début_de_l_évaluation, heure_de_fin_de_l_évaluation column(s). Geographic scope: BEN.

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

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Dataset Characteristics

Domain	Forced displacement and migration
Unit of observation	Geolocated point observations
Rows (total)	255
Columns	48 (12 numeric, 29 categorical, 7 datetime)
Train split	204 rows
Test split	51 rows
Geographic scope	BEN
Publisher	International Organization for Migration (IOM)
HDX last updated	2025-08-08

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Variables

Geographic — position_gps_de_début_latitude (range 10.0221–12.072), position_gps_de_début_longitude (range 0.8746–3.5758), pdode_admin_1 (BJ02, BJ01, #adm1+code), pdode_admin_2 (BJ0205, BJ0105, BJ0101), y_a_t_il_des_ménages_personnes_pdi_au_niveau_de_votre_localité and 13 others.

Temporal — m2682_meta_date_auto, date_de_l_évaluation, submission_time.

Demographic — personne_interrogée_rôle_profession, nombre_total_de_ménages (range 1.0–247.0).

Outcome / Measurement — nombre_total_de_pdis (range 1.0–1357.0).

Identifier / Metadata — identifiant_de_l_appareil (collect:oWFICfj709rtiWpW, collect:y3rvfq0yjMNbGIM1, collect:ceRr8pC5ESuhjyME), identifiant_arrondissement (BJ02053, BJ02051, BJ02055), identifiant_de_la_localité2 (BJ0103_ANG_Ang_0001, BJ0105_TOU_San_0001, BJ0203_TAP_Pen_0001), id (range 20260441.0–20404036.0), uuid and 2 others.

Other — heure_de_début_de_l_évaluation, heure_de_fin_de_l_évaluation, position_gps_de_début (10.8316516 1.0207709 0.0 3400.0, 11.3811455 2.4335497 305.1 3.62, 10.6486525 1.2567644 0.0 4199.999), position_gps_de_début_altitude (range 0.0–610.7), position_gps_de_début_precision (range 1.036–4199.999) and 12 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-displacement-benin")
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
heure_de_début_de_l_évaluation	datetime64[ns]	0.4%
heure_de_fin_de_l_évaluation	datetime64[ns]	0.4%
m2682_meta_date_auto	datetime64[ns]	0.4%
identifiant_de_l_appareil	object	0.4%	collect:oWFICfj709rtiWpW, collect:y3rvfq0yjMNbGIM1, collect:ceRr8pC5ESuhjyME
position_gps_de_début	object	54.5%	10.8316516 1.0207709 0.0 3400.0, 11.3811455 2.4335497 305.1 3.62, 10.6486525 1.2567644 0.0 4199.999
position_gps_de_début_latitude	float64	54.5%	10.0221 – 12.072 (mean 10.8809)
position_gps_de_début_longitude	float64	54.5%	0.8746 – 3.5758 (mean 1.7912)
position_gps_de_début_altitude	float64	54.5%	0.0 – 610.7 (mean 271.5858)
position_gps_de_début_precision	float64	54.5%	1.036 – 4199.999 (mean 277.4288)
si_j_ai_votre_permission_pouvons_nous_continuer	object	0.4%	Oui
date_de_l_évaluation	datetime64[ns]	0.4%
pdode_admin_1	object	0.0%	BJ02, BJ01, #adm1+code
département	object	0.0%	ATAKORA, ALIBORI, #adm1+name
pdode_admin_2	object	0.0%	BJ0205, BJ0105, BJ0101
commune	object	0.0%	MATERI, MALANVILLE, BANIKOARA
identifiant_arrondissement	object	0.0%	BJ02053, BJ02051, BJ02055
arrondissement	object	0.0%	MATERI, DASSIRI, TANTEGA
identifiant_de_la_localité2	object	0.4%	BJ0103_ANG_Ang_0001, BJ0105_TOU_San_0001, BJ0203_TAP_Pen_0001
localités_évaluées	object	0.4%
personne_interrogée_rôle_profession	object	0.4%
autre_rôle_profession	object	59.6%
le_répondant_consent_il_à_cette_interview	object	0.4%
y_a_t_il_des_ménages_personnes_pdi_au_niveau_de_votre_localité	object	0.4%
nombre_total_de_pdis	float64	0.4%	1.0 – 1357.0 (mean 107.4567)
nombre_total_de_ménages	float64	0.4%	1.0 – 247.0 (mean 22.7205)
raison_du_déplacement_et_arrivée_des_personnes_déplacées_à_partir_du_lieu_d_origine_sub	object	0.4%
lieu_d_origine_admin_1_du_plus_grand_groupe_de_population_présent_dans_la_localité	object	0.4%
lieu_d_origine_du_plus_grand_groupe_de_pdi_dans_votre_localité	object	0.4%
origine_de_la_majorité_des_pdi_présents_dans_la_localité	object	0.4%
pour_le_groupe_le_plus_important_de_personnes_déplacées_qui_reviennent_d_où_reviennent_elles_admin_1	object	0.4%
pour_le_groupe_le_plus_important_de_personnes_déplacées_qui_reviennent_d_où_reviennent_elles_admin_2	object	0.4%
date_du_déplacement_initial	datetime64[ns]	0.4%
date_d_arrivée_du_plus_grand_groupe_de_population_dans_la_localité	datetime64[ns]	0.4%
période_d_arrivée_des_pdis	object	0.4%
coordonnées	object	0.4%
coordonnées_latitude	float64	0.4%	10.0219 – 12.3887 (mean 10.9793)
coordonnées_longitude	float64	0.4%	0.7883 – 3.5746 (mean 1.8669)
coordonnées_altitude	float64	0.4%	0.0 – 613.7 (mean 278.6505)
coordonnées_precision	float64	0.4%	0.753 – 4800.0 (mean 45.2553)
id	float64	0.4%	20260441.0 – 20404036.0 (mean 20319989.9646)
uuid	object	0.4%
submission_time	datetime64[ns]	0.4%
status	object	0.4%
submitted_by	object	0.4%
version	object	0.4%
index	float64	0.4%	1.0 – 266.0 (mean 132.1417)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
position_gps_de_début_latitude	10.0221	12.072	10.8809	10.7764
position_gps_de_début_longitude	0.8746	3.5758	1.7912	1.4116
position_gps_de_début_altitude	0.0	610.7	271.5858	268.2585
position_gps_de_début_precision	1.036	4199.999	277.4288	3.0855
nombre_total_de_pdis	1.0	1357.0	107.4567	50.0
nombre_total_de_ménages	1.0	247.0	22.7205	11.0
coordonnées_latitude	10.0219	12.3887	10.9793	10.7987
coordonnées_longitude	0.7883	3.5746	1.8669	1.4232
coordonnées_altitude	0.0	613.7	278.6505	259.656
coordonnées_precision	0.753	4800.0	45.2553	3.9
id	20260441.0	20404036.0	20319989.9646	20313580.5
index	1.0	266.0	132.1417	131.5

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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. 14 column(s) with >80% missing values were removed: introduction_bonjour_je_m_appelle_nom_de_l_enquêteur_et_je_travaille_pour_l_organisation_internationale_pour_les_migrations_oim_nous_menons_des_entretiens_afin_de_comprendre_la_dynamique_des_déplacements_dans_votre_département_cet_entretien_durera, autre_localité, raison_climatique_additionnelle, raison_climatique_additionnelle_inondation, raison_climatique_additionnelle_cyclône, raison_climatique_additionnelle_incendie_feu_de_brousse.... 2 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: position_gps_de_début, position_gps_de_début_latitude, position_gps_de_début_longitude, position_gps_de_début_altitude, position_gps_de_début_precision, autre_rôle_profession.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_displacement_benin,
  title     = {Benin Displacement - [IDPs] - Mobility Tracking [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/benin-displacement-idps-mobility-tracking-iom-dtm},
  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.