electricsheepafrica/africa-idmc-idp-data-moz

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- conflict-violence
- displacement
- internally-displaced-persons-idp
- natural-disasters
- moz
pretty_name: "Mozambique - Internal Displacements (New Displacements) – IDPs"
dataset_info:
splits:
- name: train
num_examples: 7
- name: test
num_examples: 1
---

# Mozambique - Internal Displacements (New Displacements) – IDPs

**Publisher:** Internal Displacement Monitoring Centre (IDMC) · **Source:** [HDX](https://data.humdata.org/dataset/idmc-idp-data-moz) · **License:** `cc-by-igo` · **Updated:** 2026-03-18

---

## Abstract

The [Global Internal Displacement Database (GIDD)](http://www.internal-displacement.org/database/displacement-data), maintained by the [Internal Displacement Monitoring Centre (IDMC)](https://www.internal-displacement.org/), provides comprehensive, validated annual estimates of internal displacement worldwide. It defines internally displaced persons (IDPs) in line with the [1998 Guiding Principles](https://www.internal-displacement.org/internal-displacement/guiding-principles-on-internal-displacement/), 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. Data was last updated on HDX on 2026-03-18. Geographic scope: **MOZ**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Conflict and security |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 9 |
| **Columns** | 9 (5 numeric, 4 categorical, 0 datetime) |
| **Train split** | 7 rows |
| **Test split** | 1 rows |
| **Geographic scope** | MOZ |
| **Publisher** | Internal Displacement Monitoring Centre (IDMC) |
| **HDX last updated** | 2026-03-18 |

---

## Variables

**Geographic** — `iso3` (MOZ), `country_name` (Mozambique), `year` (range 2016.0–2024.0), `new_displacement` (range 126.0–592037.0), `new_displacement_rounded` (range 130.0–592000.0) and 2 others.

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-06).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-idmc-idp-data-moz")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `iso3` | object | 0.0% | MOZ |
| `country_name` | object | 0.0% | Mozambique |
| `year` | int64 | 0.0% | 2016.0 – 2024.0 (mean 2020.0) |
| `new_displacement` | int64 | 0.0% | 126.0 – 592037.0 (mean 152307.4444) |
| `new_displacement_rounded` | int64 | 0.0% | 130.0 – 592000.0 (mean 152247.7778) |
| `total_displacement` | int64 | 0.0% | 10213.0 – 1029857.0 (mean 416357.5556) |
| `total_displacement_rounded` | int64 | 0.0% | 10000.0 – 1030000.0 (mean 416333.3333) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2016.0 | 2024.0 | 2020.0 | 2020.0 |
| `new_displacement` | 126.0 | 592037.0 | 152307.4444 | 40742.0 |
| `new_displacement_rounded` | 130.0 | 592000.0 | 152247.7778 | 41000.0 |
| `total_displacement` | 10213.0 | 1029857.0 | 416357.5556 | 579688.0 |
| `total_displacement_rounded` | 10000.0 | 1030000.0 | 416333.3333 | 580000.0 |

---

## 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`. 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 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/idmc-idp-data-moz) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_idmc_idp_data_moz,
title = {Mozambique - Internal Displacements (New Displacements) – IDPs},
author = {Internal Displacement Monitoring Centre (IDMC)},
year = {2026},
url = {https://data.humdata.org/dataset/idmc-idp-data-moz},
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.*