README.md

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- conflict-violence
- displacement
- forced-displacement
- internally-displaced-persons-idp
- bgd
pretty_name: "Bangladesh IOM Displacement Tracking Matrix (DTM) from API"
dataset_info:
  splits:
    - name: train
      num_examples: 58
    - name: test
      num_examples: 14
---

# Bangladesh IOM Displacement Tracking Matrix (DTM) from API

**Publisher:** International Organization for Migration (IOM) · **Source:** [HDX](https://data.humdata.org/dataset/bgd-iom-dtm-from-api) · **License:** `hdx-other` · **Updated:** 2026-05-04

---

## Abstract

This dataset comes from the International Organization for Migration (IOM)'s displacement tracking matrix (DTM) [publicly accessible API](https://dtm.iom.int/data-and-analysis/dtm-api). This API allows the humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by DTM. The DTM API only provides non-sensitive IDP figures, aggregated at the country, Admin 1 (states, provinces, or equivalent), and Admin 2 (smaller subnational administrative areas) levels. For more detailed information, please see the [country-specific DTM datasets on HDX](https://data.humdata.org/dataset/?dataseries_name=IOM%20-%20DTM%20Baseline%20Assessment&dataseries_name=IOM%20-%20DTM%20Event%20and%20Flow%20Tracking&dataseries_name=IOM%20-%20DTM%20Site%20and%20Location%20Assessment&organization=international-organization-for-migration&q=&sort=last_modified%20desc&ext_page_size=25).

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `reportingdate` column(s). Geographic scope: **BGD**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Conflict and security |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 73 |
| **Columns** | 21 (6 numeric, 14 categorical, 1 datetime) |
| **Train split** | 58 rows |
| **Test split** | 14 rows |
| **Geographic scope** | BGD |
| **Publisher** | International Organization for Migration (IOM) |
| **HDX last updated** | 2026-05-04 |

---

## Variables

**Geographic** — `admin0name` (Bangladesh), `admin0pcode` (BGD), `admin1name` (Dhaka, Chittagong, Khulna), `admin1pcode` (BD30, BD20, BD40), `admin2name` (Rajbari, Rangpur, Kurigram) and 7 others.

**Temporal** — `reportingdate`, `monthreportingdate` (range 10.0–10.0).

**Outcome / Measurement** — `roundnumber` (range 1.0–1.0).

**Identifier / Metadata** — `id` (range 360.0–136356.0), `numpresentidpind` (range 1121.0–4955527.0), `esa_source`, `esa_processed`.

**Other** — `operation` (Mobility Monitoring due to Disasters), `operationstatus`.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-displacement-bangladesh-iom-dtm-from-api")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id` | float64 | 1.4% | 360.0 – 136356.0 (mean 59232.5) |
| `operation` | object | 0.0% | Mobility Monitoring due to Disasters |
| `admin0name` | object | 0.0% | Bangladesh |
| `admin0pcode` | object | 0.0% | BGD |
| `admin1name` | object | 1.4% | Dhaka, Chittagong, Khulna |
| `admin1pcode` | object | 1.4% | BD30, BD20, BD40 |
| `admin2name` | object | 12.3% | Rajbari, Rangpur, Kurigram |
| `admin2pcode` | object | 12.3% | BD3082, BD5585, BD5549 |
| `adminlevel` | int64 | 0.0% | 0.0 – 2.0 (mean 1.863) |
| `numpresentidpind` | int64 | 0.0% | 1121.0 – 4955527.0 (mean 203651.7945) |
| `reportingdate` | datetime64[ns] | 0.0% |  |
| `yearreportingdate` | int64 | 0.0% | 2025.0 – 2025.0 (mean 2025.0) |
| `monthreportingdate` | int64 | 0.0% | 10.0 – 10.0 (mean 10.0) |
| `roundnumber` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `displacementreason` | object | 0.0% | Natural disaster |
| `idporiginadmin1name` | object | 0.0% | Not available |
| `idporiginadmin1pcode` | object | 0.0% | Not available |
| `assessmenttype` | object | 0.0% |  |
| `operationstatus` | object | 0.0% |  |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 360.0 | 136356.0 | 59232.5 | 56194.0 |
| `adminlevel` | 0.0 | 2.0 | 1.863 | 2.0 |
| `numpresentidpind` | 1121.0 | 4955527.0 | 203651.7945 | 60003.0 |
| `yearreportingdate` | 2025.0 | 2025.0 | 2025.0 | 2025.0 |
| `monthreportingdate` | 10.0 | 10.0 | 10.0 | 10.0 |
| `roundnumber` | 1.0 | 1.0 | 1.0 | 1.0 |

---

## 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`. 2 column(s) with >80% missing values were removed: `numbermales`, `numberfemales`. 1 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/bgd-iom-dtm-from-api) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_asia_displacement_bangladesh_iom_dtm_from_api,
  title     = {Bangladesh IOM Displacement Tracking Matrix (DTM) from API},
  author    = {International Organization for Migration (IOM)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/bgd-iom-dtm-from-api},
  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.*