electricsheepafrica/africa-idmc-idp-data-nam

Namibia - Internal Displacements (New Displacements) – IDPs

Publisher: Internal Displacement Monitoring Centre (IDMC) · Source: HDX · License: cc-by-igo · Updated: 2026-03-18

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Abstract

The Global Internal Displacement Database (GIDD), maintained by the Internal Displacement Monitoring Centre (IDMC), provides comprehensive, validated annual estimates of internal displacement worldwide. It defines internally displaced persons (IDPs) in line with the 1998 Guiding Principles, as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

The GIDD tracks two primary metrics: "People Displaced" or population "Stock" figures, which represent the total number of people living in displacement at year-end, and "New Displacement," which counts new displacement incidents (population Flows) rather than individual people, accounting for potential multiple displacements by the same person. This dataset serves as a crucial resource for understanding long-term trends and validated displacement figures globally. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/.

"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.

"Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.

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

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

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Dataset Characteristics

Domain	Conflict and security
Unit of observation	Country-level aggregates
Rows (total)	23
Columns	21 (9 numeric, 10 categorical, 2 datetime)
Train split	18 rows
Test split	4 rows
Geographic scope	NAM
Publisher	Internal Displacement Monitoring Centre (IDMC)
HDX last updated	2026-03-18

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Variables

Geographic — iso3 (NAM), country_name (Namibia), year (range 2009.0–2024.0), start_date_accuracy (Day, Month, Week), end_date_accuracy (Day, Week, Month) and 11 others.

Temporal — start_date, end_date.

Identifier / Metadata — event_name (Namibia: Flood - 01/01/2009, Namibia: Flood - 5 regions - 12/04/2018, Namibia: Storm - Kavango West (Katwitwi) - 27/11/2024), esa_source (HDX), esa_processed (2026-04-12).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-idmc-idp-data-nam")
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
iso3	object	0.0%	NAM
country_name	object	0.0%	Namibia
year	int64	0.0%	2009.0 – 2024.0 (mean 2017.6087)
start_date	datetime64[ns]	0.0%
start_date_accuracy	object	0.0%	Day, Month, Week
end_date	datetime64[ns]	17.4%
end_date_accuracy	object	17.4%	Day, Week, Month
event_name	object	0.0%	Namibia: Flood - 01/01/2009, Namibia: Flood - 5 regions - 12/04/2018, Namibia: Storm - Kavango West (Katwitwi) - 27/11/2024
hazard_category	int64	0.0%	2.0 – 2.0 (mean 2.0)
hazard_category_name	object	0.0%	Weather related
hazard_sub_category	int64	0.0%	2.0 – 4.0 (mean 3.1739)
hazard_sub_category_name	object	0.0%	Hydrological, Meteorological, Climatological
hazard_type	int64	0.0%	9.0 – 14.0 (mean 10.8261)
hazard_type_name	object	0.0%	Flood, Storm, Wildfire
hazard_sub_type	int64	0.0%	11.0 – 21.0 (mean 14.5652)
new_displacement	int64	0.0%	1.0 – 60000.0 (mean 6517.1739)
new_displacement_rounded	int64	0.0%	1.0 – 60000.0 (mean 6539.3913)
total_displacement	float64	78.3%	8.0 – 1060.0 (mean 391.8)
total_displacement_rounded	float64	78.3%	8.0 – 1100.0 (mean 398.4)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-12

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Numeric Summary

Column	Min	Max	Mean	Median
year	2009.0	2024.0	2017.6087	2018.0
hazard_category	2.0	2.0	2.0	2.0
hazard_sub_category	2.0	4.0	3.1739	3.0
hazard_type	9.0	14.0	10.8261	10.0
hazard_sub_type	11.0	21.0	14.5652	13.0
new_displacement	1.0	60000.0	6517.1739	100.0
new_displacement_rounded	1.0	60000.0	6539.3913	100.0
total_displacement	8.0	1060.0	391.8	305.0
total_displacement_rounded	8.0	1100.0	398.4	300.0

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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. 2 column(s) with >80% missing values were removed: hazard_subtype_name, event_codes. 2 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 Internal Displacement Monitoring Centre (IDMC) 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: total_displacement, total_displacement_rounded.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_idmc_idp_data_nam,
  title     = {Namibia - Internal Displacements (New Displacements) – IDPs},
  author    = {Internal Displacement Monitoring Centre (IDMC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/idmc-idp-data-nam},
  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.