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Sri Lanka Displacement - [IDPs] - Baseline Assessment - [IOM DTM] Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2026-04-27 Abstract The dataset contains IDPs Each row in this dataset represents tabular records. Temporal coverage is indicated by the unnamed_1 column(s). Geographic scope: LKA. Curated into ML-ready Parquet format by Electric Sheep Africa. Dataset Characteristics… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-displacement-sri-lanka-displacement-idps-baseline-ass.

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Dataset Card

Sri Lanka Displacement - [IDPs] - Baseline Assessment - [IOM DTM]

Publisher: International Organization for Migration (IOM) · Source: HDX · License: hdx-other · Updated: 2026-04-27

Abstract

The dataset contains IDPs

Each row in this dataset represents tabular records. Temporal coverage is indicated by the unnamed_1 column(s). Geographic scope: LKA.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

DomainFood security and nutrition
Unit of observationTabular records
Rows (total)32
Columns93 (28 numeric, 64 categorical, 1 datetime)
Train split25 rows
Test split6 rows
Geographic scopeLKA
PublisherInternational Organization for Migration (IOM)
HDX last updated2026-04-27

Variables

Geographic — survey_information (REF., AHL-01, AHL-02), site_location (SSID, AHL-01, AHL-02), idps_demography (range 4.0–201.0), displacement_condition.

Demographic — site_management (Available, Unavailable, SITE MANAGEMENT COMMITTEE (SMC) AVAILABILITY).

Identifier / Metadata — unnamed_1, unnamed_2 (range 2.0–2.0), unnamed_4 (Ayagama, Kahawaththa, Deiyangala vidyalaya), unnamed_5 (Rathnapura, DISTRICT, #adm1+name), unnamed_6 (Nivithigala, Elapatha, Eheliyagoda) and 73 others.

Other — vulnerable_group (range 0.0–4.0), shelter_and_nfi, wash, food, health and 5 others.

Quick Start

python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-displacement-sri-lanka-displacement-idps-baseline-ass")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

ColumnTypeNull %Range / Sample Values
survey_informationobject3.1%REF., AHL-01, AHL-02
unnamed_1datetime64[ns]6.2%
unnamed_2float646.2%2.0 – 2.0 (mean 2.0)
site_locationobject3.1%SSID, AHL-01, AHL-02
unnamed_4object3.1%Ayagama, Kahawaththa, Deiyangala vidyalaya
unnamed_5object0.0%Rathnapura, DISTRICT, #adm1+name
unnamed_6object0.0%Nivithigala, Elapatha, Eheliyagoda
unnamed_7object0.0%All GN Division, All GN Division , Palawela
unnamed_8float646.2%6.4037 – 6.8901 (mean 6.6697)
unnamed_9float646.2%80.1812 – 80.5934 (mean 80.3816)
unnamed_10object3.1%Unknown, EXPECTED CLOSING, 0 - 2 Week
site_managementobject3.1%Available, Unavailable, SITE MANAGEMENT COMMITTEE (SMC) AVAILABILITY
unnamed_12object3.1%Available, Unavailable, SITE MANAGEMENT AGENCY (SMA) AVAILABILITY
unnamed_13object3.1%Government, No, Not Mentioned
idps_originobject3.1%
unnamed_15object3.1%
unnamed_16object3.1%
idps_demographyfloat646.2%4.0 – 201.0 (mean 83.6667)
unnamed_18float646.2%0.0 – 8.0 (mean 3.2333)
unnamed_19float646.2%0.0 – 2.0 (mean 0.8333)
unnamed_20float646.2%0.0 – 34.0 (mean 13.8)
unnamed_21float646.2%0.0 – 31.0 (mean 12.1)
unnamed_22float646.2%0.0 – 65.0 (mean 26.1)
unnamed_23float646.2%0.0 – 61.0 (mean 24.4)
unnamed_24float646.2%1.0 – 240.0 (mean 94.9333)
unnamed_25float646.2%0.0 – 272.0 (mean 107.7667)
unnamed_26float646.2%0.0 – 72.0 (mean 28.4)
unnamed_27float646.2%1.0 – 56.0 (mean 22.0667)
unnamed_28float646.2%8.0 – 841.0 (mean 333.6333)
vulnerable_groupfloat646.2%0.0 – 4.0 (mean 0.4333)
unnamed_30float646.2%0.0 – 125.0 (mean 13.8)
unnamed_31float646.2%0.0 – 9.0 (mean 0.7667)
unnamed_32float646.2%0.0 – 92.0 (mean 3.2667)
unnamed_33float646.2%0.0 – 1.0 (mean 0.2)
unnamed_34float646.2%
unnamed_35float646.2%
unnamed_36float646.2%
unnamed_37float646.2%
displacement_conditionobject3.1%
unnamed_39object3.1%
unnamed_40object3.1%
unnamed_41object3.1%
unnamed_42object3.1%
shelter_and_nfiobject3.1%
unnamed_44object3.1%
unnamed_45object3.1%
unnamed_46object3.1%
unnamed_47object3.1%
unnamed_48object3.1%
unnamed_49object3.1%
unnamed_50object21.9%
unnamed_51object31.2%
washobject3.1%
unnamed_53object3.1%
unnamed_54object3.1%
unnamed_55float646.2%
unnamed_56object3.1%
unnamed_57object3.1%
unnamed_58float646.2%
unnamed_59object3.1%
unnamed_60object3.1%
unnamed_61object3.1%
foodobject3.1%
unnamed_63object3.1%
unnamed_64object3.1%
unnamed_65object3.1%
unnamed_66object3.1%
healthobject3.1%
unnamed_68object3.1%
unnamed_69object3.1%
unnamed_70object3.1%
unnamed_71object3.1%
unnamed_72object3.1%
educationobject3.1%
unnamed_74object3.1%
livelihoodobject3.1%
unnamed_76object3.1%
unnamed_77object3.1%
unnamed_78object3.1%
unnamed_79object3.1%
unnamed_80object3.1%
protectionobject3.1%
unnamed_82object3.1%
unnamed_83float646.2%
unnamed_84float646.2%
unnamed_85object3.1%
unnamed_86object3.1%
unnamed_87object3.1%
informationobject3.1%
unnamed_89object3.1%
commentsobject3.1%
esa_sourceobject0.0%
esa_processedobject0.0%

Numeric Summary

ColumnMinMaxMeanMedian
unnamed_22.02.02.02.0
unnamed_86.40376.89016.66976.6631
unnamed_980.181280.593480.381680.3667
idps_demography4.0201.083.666773.0
unnamed_180.08.03.23332.5
unnamed_190.02.00.83331.0
unnamed_200.034.013.812.0
unnamed_210.031.012.110.0
unnamed_220.065.026.123.0
unnamed_230.061.024.421.0
unnamed_241.0240.094.933383.0
unnamed_250.0272.0107.766794.0
unnamed_260.072.028.425.0
unnamed_271.056.022.066719.0
unnamed_288.0841.0333.6333290.0

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. 29 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: unnamed_50, unnamed_51.
  • —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

bibtex
@dataset{hdx_asia_displacement_sri_lanka_displacement_idps_baseline_ass,
  title     = {Sri Lanka Displacement - [IDPs] - Baseline Assessment - [IOM DTM]},
  author    = {International Organization for Migration (IOM)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/sri-lanka-displacement-idps-baseline-assessment-iom-dtm},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.