electricsheepafrica/africa-displacement-somalia

Somalia Displacement - [IDPs, Returnees] - Baseline Assessment [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2025-05-05

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Abstract

The dataset contains IDPs, Returnees and host community at sub-national level.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the reported_date, m_0296_what_is_the_date_of_the_assessment column(s). Geographic scope: SOM.

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

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Dataset Characteristics

Domain	Conflict and security
Unit of observation	Subnational administrative unit observations
Rows (total)	12,091
Columns	86 (28 numeric, 54 categorical, 4 datetime)
Train split	9,672 rows
Test split	2,418 rows
Geographic scope	SOM
Publisher	International Organization for Migration (IOM)
HDX last updated	2025-05-05

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Variables

Geographic — country (Somalia, #adm0+name), country_code (SOM, #adm0+code), m_1725_what_is_the_type_of_interview (Face-to-face interview carried out in the location on a mobile device, Phone call, Face to face interview but not at settlement location), why_is_the_interview_being_conducted_by_phone (Location is inaccessible or partially inaccessible, Would take a long time to access, Other), m_0671_why_is_the_location_inaccessible_restriction_of_movement_within_the_affected_area (No, Yes) and 53 others.

Temporal — reported_date, m_0296_what_is_the_date_of_the_assessment.

Demographic — m_0327_estimated_of_resident_households (range 0.0–33000.0), m_0328_estimated_of_resident_individuals (range 0.0–198069.0), m_1813_total_of_male_resident_individuals (range 0.0–87850.0), m_1814_total_of_female_resident_individuals (range 0.0–110219.0), m_0309_estimated_of_idp_households (range 0.0–6319.0) and 9 others.

Outcome / Measurement — m_0489_round_number (range 3.0–3.0).

Identifier / Metadata — settlement_dtm_id, m_0445_settlement_name, s_0476_when_did_people_first_arrive_in_this_settlement, s_0477_when_did_people_last_arrive_at_this_settlement, ocha_cccm_id and 2 others.

Other — m_0342_what_is_the_settlement_classification, catchment_area, is_the_area_accessible_to_humanitarian_partners, origin_destination.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-displacement-somalia")
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
m_0489_round_number	float64	0.0%	3.0 – 3.0 (mean 3.0)
country	object	0.0%	Somalia, #adm0+name
country_code	object	0.0%	SOM, #adm0+code
reported_date	datetime64[ns]	0.0%
m_0296_what_is_the_date_of_the_assessment	datetime64[ns]	0.0%
m_1725_what_is_the_type_of_interview	object	0.0%	Face-to-face interview carried out in the location on a mobile device, Phone call, Face to face interview but not at settlement location
why_is_the_interview_being_conducted_by_phone	object	78.1%	Location is inaccessible or partially inaccessible, Would take a long time to access, Other
m_0671_why_is_the_location_inaccessible_restriction_of_movement_within_the_affected_area	object	77.1%	No, Yes
m_0671_why_is_the_location_inaccessible_military_operations_and_on_going_hostilities_impeding_humanitarian_operations	object	77.1%	No, Yes
m_0671_why_is_the_location_inaccessible_interference_in_the_implementation_of_humanitarian_activities	object	77.1%	No, Yes
m_0671_why_is_the_location_inaccessible_presence_of_mines_and_unexploded_ordinance	object	77.1%	No, Yes
m_0671_why_is_the_location_inaccessible_physical_environment_damage_on_roads_bridges	object	77.1%	No, Yes
m_0671_why_is_the_location_inaccessible_denial_of_the_existence_of_humanitarian_needs_or_of_entitlements_to_services	object	77.1%	No, Yes
m_0671_why_is_the_location_inaccessible_restrictions_on_or_obstruction_of_conflict_affected_people_s_access_to_services_and_assistance	object	77.1%
m_0671_why_is_the_location_inaccessible_presence_of_as	object	77.1%
m_0671_why_is_the_location_inaccessible_clan_conflict	object	77.1%
m_0671_why_is_the_location_inaccessible_other	object	77.1%
number_of_key_informant	float64	0.0%	1.0 – 4.0 (mean 1.0087)
m_0337_what_is_the_location_type	object	0.0%
m_0342_what_is_the_settlement_classification	object	0.0%
please_indicate_the_population_groups_that_are_present_in_the_location_idps	object	0.0%
please_indicate_the_population_groups_that_are_present_in_the_location_resident_host_community	object	0.0%
please_indicate_the_population_groups_that_are_present_in_the_location_returnees	object	0.0%
m_0303_region_name	object	0.0%
region_pcode	object	0.0%
m_0304_district_name	object	0.0%
district_pcode	object	0.0%
ocha_region_pcode	object	0.0%
ocha_district_pcode	object	0.0%
m_0305_operational_zone	object	0.0%
city_town_name	object	52.8%
city_town_code	object	52.8%
catchment_area	object	71.4%
s_0454_if_a_neighbourhood_or_sub_site_within_the_location_please_specify_name_of_the_neighbourhood_or_sub_site	object	33.4%
settlement_dtm_id	object	0.0%
m_0445_settlement_name	object	0.0%
m_0327_estimated_of_resident_households	float64	0.0%	0.0 – 33000.0 (mean 130.4754)
m_0328_estimated_of_resident_individuals	float64	0.0%	0.0 – 198069.0 (mean 782.1263)
m_1813_total_of_male_resident_individuals	float64	0.0%	0.0 – 87850.0 (mean 348.6581)
m_1814_total_of_female_resident_individuals	float64	0.0%	0.0 – 110219.0 (mean 433.4682)
m_1477_of_male_resident_infants_0_5_years	float64	0.0%	0.0 – 10852.0 (mean 74.6418)
m_1478_of_male_resident_children_6_17_years	float64	0.0%	0.0 – 32470.0 (mean 109.1835)
m_1482_of_male_resident_adults_18_59_years	float64	0.0%	0.0 – 49820.0 (mean 148.6989)
m_1483_of_male_resident_elderly_60	float64	0.0%	0.0 – 2538.0 (mean 16.134)
m_1484_of_female_resident_infant_0_5_years	float64	0.0%	0.0 – 14174.0 (mean 92.539)
m_1485_of_female_resident_children_6_17_years	float64	0.0%	0.0 – 37911.0 (mean 134.065)
m_1489_of_female_resident_adults_18_59_years	float64	0.0%	0.0 – 65080.0 (mean 186.7489)
s_2033_of_female_resident_elderly_60	float64	0.0%	0.0 – 3592.0 (mean 20.1152)
m_0309_estimated_of_idp_households	float64	0.0%	0.0 – 6319.0 (mean 44.9827)
m_0310_estimated_of_idp_individuals	float64	0.0%	0.0 – 37914.0 (mean 269.8164)
m_1813_total_of_male_idp_individuals	float64	0.0%	0.0 – 17342.0 (mean 119.9607)
m_1814_total_of_female_idp_individuals	float64	0.0%	0.0 – 20572.0 (mean 149.8557)
m_1477_of_male_idp_infant_0_5_years	float64	0.0%	0.0 – 4245.0 (mean 24.1044)
m_1478_of_male_idp_children_6_17_years	float64	0.0%	0.0 – 6300.0 (mean 42.2503)
m_1482_of_male_idp_adults_18_59_years	float64	0.0%
m_1483_of_male_idp_elderly_60	float64	0.0%
m_1484_of_female_idp_infant_0_5_years	float64	0.0%
m_1485_of_female_idp_children_6_17_years	float64	0.0%
m_1489_of_female_idp_adults_18_59_years	float64	0.0%
s_2033_of_female_idp_elderly_60	float64	0.0%
s_0476_when_did_people_first_arrive_in_this_settlement	datetime64[ns]	58.3%
s_0477_when_did_people_last_arrive_at_this_settlement	datetime64[ns]	58.3%
m_0413_region_of_origin_largest_idp_group	object	58.3%
m_0414_district_of_origin_largest_idp_group	object	58.3%
s_0418_region_of_origin_second_largest_idp_group	object	58.3%
s_0419_district_of_origin_second_largest_idp_group	object	59.7%
s_0423_region_of_origin_third_largest_idp_group	object	59.7%
s_0424_district_of_origin_third_largest_idp_group	object	68.5%
m_0326_primary_reason_for_displacement_of_idps	object	58.3%
secondary_reason_for_displacement_of_idps	object	58.3%
other_reason_for_displacement_of_idps	object	59.4%
are_there_previous_residents_from_this_settlement_departees_who_have_left_this_settlement_and_not_yet_returned	object	0.0%
estimated_of_returnee_households	float64	0.0%
estimated_of_returnee_individuals	float64	0.0%
m_1544_overall_reliability_accuracy_of_the_information_according_to_the_enumerator	object	0.0%
in_this_location_are_there_groups_belonging_to_minority_or_marginalized_clans	object	0.0%
is_the_area_accessible_to_humanitarian_partners	object	0.0%
if_area_is_inaccessible_or_partially_accessible_to_humanitarian_partners_why	object	69.3%
ocha_cccm_id	object	37.6%
pop1	object	0.0%
pop2	object	0.0%
pop3	object	0.0%
popcat	object	0.0%
origin_destination	object	58.3%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
m_0489_round_number	3.0	3.0	3.0	3.0
number_of_key_informant	1.0	4.0	1.0087	1.0
m_0327_estimated_of_resident_households	0.0	33000.0	130.4754	40.0
m_0328_estimated_of_resident_individuals	0.0	198069.0	782.1263	240.0
m_1813_total_of_male_resident_individuals	0.0	87850.0	348.6581	105.0
m_1814_total_of_female_resident_individuals	0.0	110219.0	433.4682	133.5
m_1477_of_male_resident_infants_0_5_years	0.0	10852.0	74.6418	21.0
m_1478_of_male_resident_children_6_17_years	0.0	32470.0	109.1835	31.0
m_1482_of_male_resident_adults_18_59_years	0.0	49820.0	148.6989	44.0
m_1483_of_male_resident_elderly_60	0.0	2538.0	16.134	5.0
m_1484_of_female_resident_infant_0_5_years	0.0	14174.0	92.539	28.0
m_1485_of_female_resident_children_6_17_years	0.0	37911.0	134.065	39.0
m_1489_of_female_resident_adults_18_59_years	0.0	65080.0	186.7489	53.0
s_2033_of_female_resident_elderly_60	0.0	3592.0	20.1152	6.0
m_0309_estimated_of_idp_households	0.0	6319.0	44.9827	0.0

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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. 6 column(s) with >80% missing values were removed: why_was_the_interview_conducted_using_a_paper_form, why_is_the_interview_being_conducted_outside_of_the_location, settlement_alternate_name, m_0326_if_primary_cause_is_natural_disaster_human_enhanced_please_specify, if_secondary_cause_is_natural_disaster_human_enhanced_please_specify, if_other_reason_for_displacement_is_natural_disaster_human_enhanced_please_specify. 7 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: why_is_the_interview_being_conducted_by_phone, m_0671_why_is_the_location_inaccessible_restriction_of_movement_within_the_affected_area, m_0671_why_is_the_location_inaccessible_military_operations_and_on_going_hostilities_impeding_humanitarian_operations, m_0671_why_is_the_location_inaccessible_interference_in_the_implementation_of_humanitarian_activities, m_0671_why_is_the_location_inaccessible_presence_of_mines_and_unexploded_ordinance, m_0671_why_is_the_location_inaccessible_physical_environment_damage_on_roads_bridges, m_0671_why_is_the_location_inaccessible_denial_of_the_existence_of_humanitarian_needs_or_of_entitlements_to_services, m_0671_why_is_the_location_inaccessible_restrictions_on_or_obstruction_of_conflict_affected_people_s_access_to_services_and_assistance....
• 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_somalia,
  title     = {Somalia Displacement - [IDPs, Returnees] - Baseline Assessment [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/somalia-displacement-idps-returnees-baseline-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.