electricsheepafrica/africa-displacement-congo-dem-rep

DRC Displacement Data - Lomami Province - Baseline Assessment [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2025-04-25

---

Abstract

The dataset contains number of people displaced and returnees at village level in Lomami province. The dataset also contains needs of the displaced and returned people, reason and time of displacement.

Each row in this dataset represents individual-level records. Temporal coverage is indicated by the date_enquete_phase_1 column(s). Geographic scope: COD.

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

---

Dataset Characteristics

Domain	Forced displacement and migration
Unit of observation	Individual-level records
Rows (total)	2,848
Columns	82 (31 numeric, 50 categorical, 1 datetime)
Train split	2,278 rows
Test split	569 rows
Geographic scope	COD
Publisher	International Organization for Migration (IOM)
HDX last updated	2025-04-25

---

Variables

Geographic — province (Lomami, LOMAMI, #adm1+name), admin1_code (CD81, #adm1+code), admin2_code (CD8102, CD8104, CD8106), zone_de_santé (KABINDA, LUBAO, KANDA KANDA), code_village_long and 20 others.

Temporal — date_enquete_phase_1, date_enquete_phase_2 (Pas évalué, 2018-08-02 00:00:00, 2018-07-31 00:00:00), g01_besoin_assistance_rapide_dans_village1_updated_r2, g01_besoin_assistance_rapide_dans_village2_r2_updated, g01_besoin_assistance_rapide_dans_village3_updated and 4 others.

Demographic — code_village (#location+code, CF121015, CI168018), nb_village (range 1.0–1.0), village, code_village_1, total_menage_dep_a_jour (range 0.0–553.0) and 3 others.

Outcome / Measurement — evalué_ière_phase (range 0.0–1.0), evalué_encore_iième_phase (range 0.0–1.0), evalué_seulement_iième_phase (range 0.0–1.0), total_pdi_a_jour (range 0.0–3402.0), total_ind_ret_a_jour (range 0.0–8933.0) and 1 others.

Identifier / Metadata — new_id_simple (range 9085.0–11768.0), code_zs (CD8102CA, CD8108CH, CD8104CG), code_as (CD8106CB039, CD8108CH147, CD8106CB036), f7b_nbre_individus_retournes_repartis_2eme_trimestre_2018, esa_source and 1 others.

Other — territoire (KABINDA, LUILU, NGANDAJIKA), nom_aire_de_santé (KANDA KANDA, MBAO LUBIJI, KABAO), chefferie, nombre_informateur_cle (range 1.0–6.0), présence_pdi_retournés and 23 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-displacement-congo-dem-rep")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
code_village	object	0.0%	#location+code, CF121015, CI168018
date_enquete_phase_1	datetime64[ns]	5.8%
date_enquete_phase_2	object	0.0%	Pas évalué, 2018-08-02 00:00:00, 2018-07-31 00:00:00
new_id_simple	float64	5.8%	9085.0 – 11768.0 (mean 10426.5)
nb_village	float64	0.0%	1.0 – 1.0 (mean 1.0)
province	object	0.0%	Lomami, LOMAMI, #adm1+name
admin1_code	object	0.0%	CD81, #adm1+code
territoire	object	0.0%	KABINDA, LUILU, NGANDAJIKA
admin2_code	object	0.0%	CD8102, CD8104, CD8106
zone_de_santé	object	0.0%	KABINDA, LUBAO, KANDA KANDA
code_zs	object	0.0%	CD8102CA, CD8108CH, CD8104CG
nom_aire_de_santé	object	0.0%	KANDA KANDA, MBAO LUBIJI, KABAO
code_as	object	0.0%	CD8106CB039, CD8108CH147, CD8106CB036
village	object	0.0%
evalué_ière_phase	float64	0.0%	0.0 – 1.0 (mean 0.9427)
evalué_encore_iième_phase	float64	0.0%	0.0 – 1.0 (mean 0.3987)
evalué_seulement_iième_phase	float64	0.0%	0.0 – 1.0 (mean 0.0573)
code_village_1	object	0.0%
code_village_long	object	0.0%
chefferie	object	2.4%
nombre_informateur_cle	float64	0.0%	1.0 – 6.0 (mean 3.3288)
total_menage_dep_a_jour	float64	0.0%	0.0 – 553.0 (mean 18.2164)
total_pdi_a_jour	float64	0.0%	0.0 – 3402.0 (mean 97.5325)
total_ind_ret_a_jour	float64	0.0%	0.0 – 8933.0 (mean 98.0502)
présence_pdi_retournés	object	0.0%
difference_r1_r2	object	0.0%
men_dep_2016_a_jour_r2	float64	0.0%	0.0 – 187.0 (mean 2.7211)
ind_dep_2016_a_jour_r2	float64	0.0%	0.0 – 935.0 (mean 14.2076)
province_de_provenance_2016	object	67.2%
territoire_provenance_2016	object	67.9%
zs_provenance_2016	object	70.9%
raison_de_deplacement_2016	object	67.2%
men_dep_2017_a_jour_r2	float64	0.0%	0.0 – 486.0 (mean 12.0541)
ind_dep_2017_a_jour_r2	float64	0.0%	0.0 – 2580.0 (mean 64.1914)
province_de_provenance_2017	object	40.9%
territoire_de_provenance_2017	object	41.6%
zone_de_santé_de_provenance_2017	object	46.1%
raison_de_déplacement_2017	object	40.9%
men_dep_2018_a_jour_r2	float64	0.0%	0.0 – 186.0 (mean 3.4412)
pdi_2018_a_jour_r2	float64	0.0%	0.0 – 1302.0 (mean 19.1335)
men_dep_1er_trim_2018_a_jour	float64	5.8%	0.0 – 186.0 (mean 2.2258)
province_de_premier_trimestre_2018	object	35.7%
territoire_de_provenance_premier_trimestre_2018	object	36.1%
zones_de_santé_provenance_premier_trimestre_2018	object	68.6%
raison_de_déplacement_premier_trimestre_2018	object	65.7%
menages_deplaces_2eme_trimestre_2018_r2	float64	54.4%	0.0 – 185.0 (mean 2.8428)
e7a_nbre_menage_deplacees_partis_2eme_trimestre_2018	float64	60.1%	0.0 – 15.0 (mean 0.2573)
e7b_nbre_individus_deplacees_partis_2eme_trimestre_2018	float64	60.1%	0.0 – 118.0 (mean 1.3674)
total_menage_ret_a_jour_r2	float64	0.0%	0.0 – 1523.0 (mean 17.7604)
total_ind_ret_a_jour_r2	float64	0.0%
différence_retournés_r1_et_r2	object	0.0%
men_ret_2016_a_jour_r2	float64	0.0%
ind_ret_2016_a_jour_r2	float64	0.0%
men_ret_2017_a_jour_r2	float64	0.0%
ind_ret_2017_a_jour_r2	float64	0.0%
province_provenance_retours_2017	object	69.7%
territoire_provenance_retour_2017	object	70.3%
zs_provenance_retours_2017	object	72.1%
raison_retours_2017	object	69.7%
men_ret_2018_a_jour_r2	float64	0.0%
ind_ret_2018_a_jour_r2	float64	0.0%
men_ret_1er_trim_2018_a_jour	float64	5.8%
province_de_provenance_retours_2018	object	74.9%
territoire_provenance_retours_2018	object	75.5%
zones_de_santé_retours_2018	object	77.4%
raison_retours_trim_1_2018	object	74.9%
menages_retournes_2eme_trimestre_2018	float64	54.4%
f7a_nbre_menage_retournes_repartis_2eme_trimestre_2018	float64	60.1%
f7b_nbre_individus_retournes_repartis_2eme_trimestre_2018	float64	60.1%
g01_besoin_assistance_rapide_dans_village1_updated_r2	object	0.5%
g01_besoin_assistance_rapide_dans_village2_r2_updated	object	0.5%
g01_besoin_assistance_rapide_dans_village3_updated	object	0.5%
g01_besoin_assistance_rapide_dans_village4_updated	object	0.5%
g01_besoin_assistance_rapide_dans_village5_updated	object	0.6%
h01_situation_village_updated_r2	object	1.6%
h02_existance_structure_medicale_operationnelle_updated_r2	object	0.3%
h03_existance_ecole_operationnelle_update_r2	object	0.3%
h04_existance_poste_police_operationnelle_updated_r2	object	1.2%
i02_enquete_se_deroule_dans_village_coordonnees_gps_village_r2_updated	object	1.9%
commentaire_r2_updated	object	62.9%
esa_source	object	0.0%
esa_processed	object	0.0%

---

Numeric Summary

Column	Min	Max	Mean	Median
new_id_simple	9085.0	11768.0	10426.5	10426.5
nb_village	1.0	1.0	1.0	1.0
evalué_ière_phase	0.0	1.0	0.9427	1.0
evalué_encore_iième_phase	0.0	1.0	0.3987	0.0
evalué_seulement_iième_phase	0.0	1.0	0.0573	0.0
nombre_informateur_cle	1.0	6.0	3.3288	3.0
total_menage_dep_a_jour	0.0	553.0	18.2164	6.0
total_pdi_a_jour	0.0	3402.0	97.5325	36.0
total_ind_ret_a_jour	0.0	8933.0	98.0502	0.0
men_dep_2016_a_jour_r2	0.0	187.0	2.7211	0.0
ind_dep_2016_a_jour_r2	0.0	935.0	14.2076	0.0
men_dep_2017_a_jour_r2	0.0	486.0	12.0541	2.0
ind_dep_2017_a_jour_r2	0.0	2580.0	64.1914	13.0
men_dep_2018_a_jour_r2	0.0	186.0	3.4412	0.0
pdi_2018_a_jour_r2	0.0	1302.0	19.1335	0.0

---

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. 20 column(s) with >80% missing values were removed: nouveau_village_phase_2, autre_localité, province_provenance2eme_trimestre2018, territoire_provenance2eme_trimestre2018, zonedesante_provenance2eme_trimestre2018, raison_deplacement2eme_trimestre_2018.... 3 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 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: province_de_provenance_2016, territoire_provenance_2016, zs_provenance_2016, raison_de_deplacement_2016, province_de_provenance_2017, territoire_de_provenance_2017, zone_de_santé_de_provenance_2017, raison_de_déplacement_2017....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_displacement_congo_dem_rep,
  title     = {DRC Displacement Data - Lomami Province - Baseline Assessment [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/drc-displacement-data-lomami-province-baseline-assessment-iom-dtm},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.