electricsheepasia/asia-migration-tajikistan-mobility-migrants-baseline-as

Tajikistan - Baseline Assessment [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2026-02-11

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Abstract

DTM REMAP implements the Baseline Mobility Assessment (BMA) in Tajikistan to track mobility, provide information on population estimates, geographic distribution of migrant workers and return migrants, reasons for migration and countries of return. Data is collected at the village level from key informants and direct observations.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the reported_date, assessmentdate column(s). Geographic scope: TJK.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	949
Columns	152 (112 numeric, 38 categorical, 2 datetime)
Train split	759 rows
Test split	189 rows
Geographic scope	TJK
Publisher	International Organization for Migration (IOM)
HDX last updated	2026-02-11

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Variables

Geographic — country (Tajikistan, #country+name), country_code (TJK, #country+code), interviewtype (face_to_face, both, phone_call), numindcommunity (range 66.0–16773.0), youth (range 0.0–5031.0) and 33 others.

Temporal — reported_date, assessmentdate.

Demographic — villagenameenglish, villagenamelocal, infantsmalesper (range 20.0–85.0), infantsfemalesper (range 15.0–80.0), childrenmalesper (range 30.0–80.0) and 7 others.

Outcome / Measurement — roundnumber (range 4.0–4.0), returnmigrantslowincomerank, outmigrantslowincomerank, totalreturnedmigrants, totaloutmigrants and 1 others.

Identifier / Metadata — id (range 10512937.0–11379389.0), adm1code (TJ3509000, TJ3507000, TJ3501000), adm1nameenglish (Districts of Central Subordination (DCS), Khatlon, Dushanbe), adm1namelocal (Нохияхои тобеи марказ, Хатлон, Душанбе), adm2code (TJ3509058, TJ3509065, TJ3507221) and 8 others.

Other — numfamilies (range 11.0–3390.0), numind (range 3.0–15.0), infants (range 0.0–1800.0), children (range 0.0–3654.0), adults (range 20.0–6509.0) and 76 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-migration-tajikistan-mobility-migrants-baseline-as")
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
id	float64	0.1%	10512937.0 – 11379389.0 (mean 10933502.5728)
roundnumber	float64	0.1%	4.0 – 4.0 (mean 4.0)
country	object	0.0%	Tajikistan, #country+name
country_code	object	0.0%	TJK, #country+code
reported_date	datetime64[ns]	0.1%
assessmentdate	datetime64[ns]	0.1%
interviewtype	object	0.1%	face_to_face, both, phone_call
adm1code	object	0.0%	TJ3509000, TJ3507000, TJ3501000
adm1nameenglish	object	0.0%	Districts of Central Subordination (DCS), Khatlon, Dushanbe
adm1namelocal	object	0.1%	Нохияхои тобеи марказ, Хатлон, Душанбе
adm2code	object	0.0%	TJ3509058, TJ3509065, TJ3507221
adm2nameenglish	object	0.0%	Rudaki, Vahdat, Kulob
adm2namelocal	object	0.1%	Рӯдакӣ, Вахдат, Кӯлоб
adm3code	object	0.1%	TJ3501268_J_0001, TJ3507221_J_0001, TJ3501263_J_0001
jamoatnameenglish	object	0.1%
jamoatnamelocal	object	0.1%
adm4code	object	0.1%
villagenameenglish	object	0.1%
villagenamelocal	object	0.1%
numfamilies	float64	0.1%	11.0 – 3390.0 (mean 306.3966)
numindcommunity	float64	0.1%	66.0 – 16773.0 (mean 1935.404)
numind	float64	0.1%	3.0 – 15.0 (mean 6.2511)
infants	float64	0.1%	0.0 – 1800.0 (mean 261.0485)
infantsmalesper	float64	0.6%	20.0 – 85.0 (mean 49.4008)
infantsfemalesper	float64	0.6%	15.0 – 80.0 (mean 50.5992)
children	float64	0.1%	0.0 – 3654.0 (mean 475.2532)
childrenmalesper	float64	0.3%	30.0 – 80.0 (mean 50.0634)
childrenfemalesper	float64	0.3%	20.0 – 70.0 (mean 49.9366)
youth	float64	0.1%	0.0 – 5031.0 (mean 511.4177)
youthmalesper	float64	0.1%	30.0 – 85.0 (mean 47.4525)
youthfemalesper	float64	0.1%	15.0 – 70.0 (mean 52.5475)
adults	float64	0.1%	20.0 – 6509.0 (mean 545.5506)
adultsmalesper	float64	0.1%	30.0 – 80.0 (mean 47.2996)
adultsfemalesper	float64	0.1%	20.0 – 70.0 (mean 52.7004)
elderly	float64	0.1%	0.0 – 1580.0 (mean 142.1382)
elderlymalesper	float64	0.6%	30.0 – 80.0 (mean 46.666)
elderlyfemalesper	float64	0.6%	20.0 – 70.0 (mean 53.334)
labourmigrantspresence	object	0.1%
returnmigrantspresence	object	0.1%
numrecentreturnmigrants	float64	5.4%
numrecentreturnmigrantsmale	float64	5.5%
numrecentreturnmigrantsfemale	float64	5.5%
numrecentreturnmigrantsgirls	float64	5.5%
numrecentreturnmigrantsboys	float64	5.5%
numrecentreturnmigrantswomen	float64	5.5%
numrecentreturnmigrantsmen	float64	5.5%
returnmigrantslabour	float64	5.5%
returnmigrantseducation	float64	5.5%
returnmigrantsunknown	float64	5.5%
returnmigrantstypeother	object	68.6%
returnmigrantstypeotherper	float64	5.8%
returnmigrantslackeconomicsrank	float64	15.0%
returnmigrantslowincomerank	float64	15.0%
returnmigrantsdepreciationcurrencyrank	float64	15.0%
returnmigrantsworkpermitexpirationrank	float64	15.0%
returnmigrantscompletionofworkrank	float64	15.0%
returnmigrantsdeportationrank	float64	15.0%
returnmigrantsfinancesrank	float64	15.0%
returnmigrantsfamilyissuesrank	float64	15.0%
returnmigrantsfindjobrank	float64	15.0%
returnmigrantspartialmobrank	float64	15.0%
returnmigrantssanctionsrank	float64	15.0%
returnmigrantshumanrightsrank	float64	15.0%
returnmigrantsconflictrank	float64	15.0%
returnmigrantsnaturaldisastersrank	float64	15.0%
returnmigrantshealthtb	float64	15.0%
returnmigrantshealthrank	float64	15.0%
returnmigrantsotherrank	float64	49.7%
outmigrantspresence	object	0.1%
numrecentoutmigrants	float64	21.8%
numrecentoutmigrantsmale	float64	22.9%
numrecentoutmigrantsfemale	float64	22.9%
numrecentoutmigrantsgirls	float64	22.9%
numrecentoutmigrantsboys	float64	22.9%
numrecentoutmigrantswomen	float64	22.9%
numrecentoutmigrantsmen	float64	22.9%
outmigrantslabour	float64	22.9%
outmigrantseducation	float64	22.9%
outmigrantsunknown	float64	22.9%
outmigrantstypeother	object	62.4%
outmigrantstypeotherper	float64	40.4%
outmigrantslackeconomicsrank	float64	27.3%
outmigrantslowincomerank	float64	27.3%
outmigrantsdepreciationcurrencyrank	float64	27.3%
outmigrantsconflictrank	float64	27.3%
outmigrantshumanrightsrank	float64	27.3%
outmigrantsnaturaldisastersrank	float64	27.3%
outmigrantsfinancesrank	float64	27.3%
outmigrantsworkcontractrank	float64	27.3%
outmigrantsfindjobrank	float64	27.3%
outmigrantsbetterlivivngrank	float64	27.3%
outmigrantsfamilyissuesrank	float64	27.3%
outmigrantshealthrank	float64	27.3%
outmigrantseducationrank	float64	27.3%
outmigrantssanctionsrank	float64	27.3%
outmigrantsotherrank	float64	47.8%
internaloutpresence	object	0.1%
internalinpresence	object	0.1%
clinic	object	0.1%
clinicdistance	float64	57.4%
clinicboundary	object	57.4%
hospital	object	0.1%
hospitaldistance	float64	7.8%
hospitalboundary	object	7.8%
tb_center	object	0.1%
tb_centerdistance	float64	3.6%
tb_centerboundary	object	3.6%
market	object	0.1%
marketdistance	float64	23.6%
marketboundary	object	23.6%
safedrinkingwater	object	0.1%
school	object	0.1%
schooldistance	float64	78.5%
schoolboundary	object	78.5%
mobileinternet	object	0.1%
bank	object	0.1%
bankdistance	float64	32.1%
bankboundary	object	32.1%
healthrespiratory	float64	0.1%
healthskin	float64	0.1%
healthtb	float64	0.1%
healthmental	float64	0.1%
healthdisability	float64	0.1%
natdisoptions_floods	float64	0.1%
natdisoptions_extremerain	float64	0.1%
natdisoptions_drought	float64	0.1%
natdisoptions_earthquakes	float64	0.1%
natdisoptions_landslides	float64	0.1%
natdisoptions_extremecold	float64	0.1%
natdisoptions_avalanches	float64	0.1%
natdisoptions_other	float64	0.1%
natdisoptions_none	float64	0.1%
totalreturnedmigrants	float64	0.1%
returnmigrants2020	float64	0.1%
returnmigrants2021	float64	0.1%
returnmigrants2022	float64	0.1%
returnmigrants2023	float64	0.1%
returnmigrants2024	float64	0.1%
totaloutmigrants	float64	0.1%
outmigrants2020	float64	0.1%
outmigrants2021	float64	0.1%
outmigrants2022	float64	0.1%
outmigrants2023	float64	0.1%
outmigrants2024	float64	0.1%
totallabourmigrants	float64	0.1%
labourmigrants2020	float64	0.1%
labourmigrants2021	float64	0.1%
labourmigrants2022	float64	0.1%
labourmigrants2023	float64	0.1%
labourmigrants2024	float64	0.1%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	10512937.0	11379389.0	10933502.5728	10914392.5
roundnumber	4.0	4.0	4.0	4.0
numfamilies	11.0	3390.0	306.3966	204.0
numindcommunity	66.0	16773.0	1935.404	1352.5
numind	3.0	15.0	6.2511	6.0
infants	0.0	1800.0	261.0485	190.0
infantsmalesper	20.0	85.0	49.4008	50.0
infantsfemalesper	15.0	80.0	50.5992	50.0
children	0.0	3654.0	475.2532	306.5
childrenmalesper	30.0	80.0	50.0634	50.0
childrenfemalesper	20.0	70.0	49.9366	50.0
youth	0.0	5031.0	511.4177	350.0
youthmalesper	30.0	85.0	47.4525	45.0
youthfemalesper	15.0	70.0	52.5475	55.0
adults	20.0	6509.0	545.5506	371.5

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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. 97 column(s) with >80% missing values were removed: numrecentlabourmigrants, numrecentlabourmigrantsmale, numrecentlabourmigrantsfemale, numrecentlabourmigrantsgirls, numrecentlabourmigrantsboys, numrecentlabourmigrantswomen.... 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: returnmigrantstypeother, returnmigrantsotherrank, numrecentoutmigrants, numrecentoutmigrantsmale, numrecentoutmigrantsfemale, numrecentoutmigrantsgirls, numrecentoutmigrantsboys, numrecentoutmigrantswomen....
• 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_tajikistan_mobility_migrants_baseline_as,
  title     = {Tajikistan - Baseline Assessment [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/tajikistan-mobility-migrants-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.