electricsheepasia/asia-migration-mongolia-displacement-idps-rapid-assessm

Mongolia Displacement Data - Rapid Assessment [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2025-07-22

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Abstract

The datasets provide an overview of key thematic areas covered by the Ulaanbaatar city-wide rapid assessment on population, migration and needs from June to August 2022. The datasets explore eight primary themes across Ulaanbaatar, namely, population baseline figures, registration, internal arrivals and departures (migration), employment, financial situation, mobility-related challenges, and services and infrastructure. Data are collected through key informant interviews with kheseg leaders , who are well-informed about their communities, resources and local context.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the submission_time column(s). Geographic scope: MNG.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Subnational administrative unit observations
Rows (total)	1,879
Columns	85 (48 numeric, 36 categorical, 1 datetime)
Train split	1,503 rows
Test split	375 rows
Geographic scope	MNG
Publisher	International Organization for Migration (IOM)
HDX last updated	2025-07-22

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Variables

Geographic — district (Bayanzurkh district, Songinokhairkhan district, Bayangol district), yr_0_5 (range 4.0–189649.0), yr_6_17 (range 10.0–383047.0), elderly_60 (range 2.0–151581.0), sex_population_total (range 0.0–1640758.0) and 39 others.

Temporal — submission_time.

Demographic — female (range 26.0–866271.0), male (range 20.0–774487.0), vuln_single_parent_household (range 0.0–21151.0), perc_individuals_registered (Most people (61-89%), Nearly everyone (90 – 99%), Around half of the people (40-60%)), individualsnotregistered_1 (Individual see themselves as temporary and therefore don’t want to register, Schooling/educational reasons;, Don’t see any use in registering, i.e. not relevant to their lives, because public services will be provided regardless;) and 2 others.

Outcome / Measurement — children_total (range 14.0–572696.0), adults_total (range 26.0–1068062.0), arrivals_number, departures_number.

Identifier / Metadata — id, uuid, esa_source, esa_processed.

Other — khoroo (range 1.0–43.0), kheseg (range 1.0–19.0), adults_18_59 (range 11.0–916481.0), vuln_pregnant_women (range 0.0–12027.0), vuln_disabilities (range 0.0–27666.0) and 20 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-migration-mongolia-displacement-idps-rapid-assessm")
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
district	object	0.2%	Bayanzurkh district, Songinokhairkhan district, Bayangol district
khoroo	float64	0.2%	1.0 – 43.0 (mean 17.2527)
kheseg	float64	0.2%	1.0 – 19.0 (mean 5.5975)
yr_0_5	float64	0.1%	4.0 – 189649.0 (mean 202.0767)
yr_6_17	float64	0.1%	10.0 – 383047.0 (mean 408.1481)
children_total	float64	0.1%	14.0 – 572696.0 (mean 610.2248)
adults_18_59	float64	0.1%	11.0 – 916481.0 (mean 976.5381)
elderly_60	float64	0.1%	2.0 – 151581.0 (mean 161.5141)
adults_total	float64	0.1%	26.0 – 1068062.0 (mean 1138.0522)
female	float64	0.1%	26.0 – 866271.0 (mean 923.0378)
male	float64	0.1%	20.0 – 774487.0 (mean 825.2392)
sex_population_total	int64	0.0%	0.0 – 1640758.0 (mean 2619.6243)
check_sumsex_j_k_sumage_d_e_g_h	float64	0.2%	0.0 – 0.0 (mean 0.0)
households_population_total	float64	0.1%	20.0 – 474198.0 (mean 505.2722)
vuln_single_parent_household	float64	0.1%	0.0 – 21151.0 (mean 22.537)
vuln_elderly_headed_household	float64	0.1%	0.0 – 55004.0 (mean 58.6084)
vuln_pregnant_women	float64	0.2%	0.0 – 12027.0 (mean 12.822)
vuln_disabilities	float64	0.1%	0.0 – 27666.0 (mean 29.479)
vuln_elderly_wo_caregiver	float64	0.1%	0.0 – 13138.0 (mean 13.9989)
vuln_child_sep_motherandfather	float64	0.1%	0.0 – 1701.0 (mean 1.8125)
vuln_child_sep_motherorfather	float64	0.1%	0.0 – 13418.0 (mean 14.2973)
vuln_population_total	float64	0.1%
perc_individuals_registered	object	0.2%	Most people (61-89%), Nearly everyone (90 – 99%), Around half of the people (40-60%)
individualsnotregistered_1	object	4.6%	Individual see themselves as temporary and therefore don’t want to register, Schooling/educational reasons;, Don’t see any use in registering, i.e. not relevant to their lives, because public services will be provided regardless;
individualsnotregistered_2	object	4.6%	Individual see themselves as temporary and therefore don’t want to register, Don’t know how to fill in the forms;, Don’t see any use in registering, i.e. not relevant to their lives, because public services will be provided regardless;
individualsnotregistered_3	object	4.6%	Individual see themselves as temporary and therefore don’t want to register, Don’t know how to fill in the forms;, Registration requires too much additional information or supporting documents;
arrivals	object	0.2%	yes, no
arrivals_number	float64	18.9%
arrivals_district_type	object	18.9%	UB city different district, Rural, This district
urban_arrivals_type	object	31.5%	Apartment kheseg, Ger kheseg, Mixed kheseg
arrivals_otor	object	18.9%	no, yes
arrivals_mining	object	18.9%	no, yes
arrivals_hard_labour	float64	18.9%
arrivals_still_remain	float64	18.9%
arrivals_reasons_1	object	18.9%
employment_type_1	object	77.2%
arrivals_reasons_2	object	18.9%
arrivals_reasons_3	object	22.7%
mobility_challenges	object	18.9%
mobility_challenges_air_pollution	float64	18.9%
mobility_challenges_desertification_land_degradation	float64	18.9%
mobility_challenges_environmental_pollution	float64	18.9%
mobility_challenges_crime_increases	float64	18.9%
mobility_challenges_strain_on_health_services	float64	18.9%
mobility_challenges_strain_on_educational_services	float64	18.9%
mobility_challenges_social_tensions	float64	18.9%
mobility_challenges_traffic_problems	float64	18.9%
mobility_challenges_other	float64	18.9%
mobility_challenges_no_challenges	float64	18.9%
mobility_challenges_action	object	27.1%
mobility_challenges_action_preferred	object	27.1%
mobility_challenges_action_preferred_create_a_favorable_legal_environment_for_the_registration_of_short_term_movements	float64	27.1%
mobility_challenges_action_preferred_improving_control_of_traffic_entering_and_exiting_the_centralized_area_creating_a_guardhouse	float64	27.1%
mobility_challenges_action_preferred_register_people_who_are_traveling_between_destinations_rural	float64	27.1%
mobility_challenges_action_preferred_other	float64	27.1%
mobility_challenges_action_preferred_i_do_not_know	float64	27.1%
departures	object	0.2%
departures_number	float64	32.3%
departures_district_type	object	32.3%
urban_departures_type	object	34.6%
departures_reasons_1	object	32.3%
departures_reasons_2	object	32.3%
departures_reasons_3	object	34.4%
primary_empl_sect	object	0.2%
primary_income_source	object	0.2%
households_reduc_expen	object	0.2%
goods_and_services_1	object	0.2%
goods_and_services_2	object	0.2%
goods_and_services_3	object	2.9%
disability	object	0.2%
disability_specialised_transportation_services	float64	0.2%
disability_infrastructure_to_safely_access_streets_parks_and_buildings	float64	0.2%
disability_specialised_medical_care	float64	0.2%
disability_specialised_education_facilities_or_services	float64	0.2%
disability_other_specify	float64	0.2%
disability_none_is_requred	float64	0.2%
additionalcomments	object	43.0%
id	float64	0.2%
uuid	object	0.2%
submission_time	datetime64[ns]	0.2%
status	object	0.2%
version	object	0.3%
index	float64	0.4%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
khoroo	1.0	43.0	17.2527	16.0
kheseg	1.0	19.0	5.5975	5.0
yr_0_5	4.0	189649.0	202.0767	94.0
yr_6_17	10.0	383047.0	408.1481	190.0
children_total	14.0	572696.0	610.2248	287.0
adults_18_59	11.0	916481.0	976.5381	467.0
elderly_60	2.0	151581.0	161.5141	70.0
adults_total	26.0	1068062.0	1138.0522	545.0
female	26.0	866271.0	923.0378	438.0
male	20.0	774487.0	825.2392	402.0
sex_population_total	0.0	1640758.0	2619.6243	845.0
check_sumsex_j_k_sumage_d_e_g_h	0.0	0.0	0.0	0.0
households_population_total	20.0	474198.0	505.2722	238.0
vuln_single_parent_household	0.0	21151.0	22.537	6.0
vuln_elderly_headed_household	0.0	55004.0	58.6084	22.0

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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. 41 column(s) with >80% missing values were removed: total_population_by_age, individualsnotregistered_1_other, individualsnotregistered_2_other, individualsnotregistered_3_other, rural_arrivals_type, arrivals_otor_number.... 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: urban_arrivals_type, employment_type_1, arrivals_reasons_3, mobility_challenges_action, mobility_challenges_action_preferred, mobility_challenges_action_preferred_create_a_favorable_legal_environment_for_the_registration_of_short_term_movements, mobility_challenges_action_preferred_improving_control_of_traffic_entering_and_exiting_the_centralized_area_creating_a_guardhouse, mobility_challenges_action_preferred_register_people_who_are_traveling_between_destinations_rural....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_migration_mongolia_displacement_idps_rapid_assessm,
  title     = {Mongolia Displacement Data - Rapid Assessment [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mongolia-displacement-idps-rapid-assessment-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.