Add README.md

README.md

@@ -1,62 +1,168 @@
 ---
+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:
-  features:
-  - name: id
-    dtype: float64
-  - name: operation
-    dtype: string
-  - name: admin0name
-    dtype: string
-  - name: admin0pcode
-    dtype: string
-  - name: admin1name
-    dtype: string
-  - name: admin1pcode
-    dtype: string
-  - name: admin2name
-    dtype: string
-  - name: admin2pcode
-    dtype: string
-  - name: adminlevel
-    dtype: int64
-  - name: numpresentidpind
-    dtype: int64
-  - name: reportingdate
-    dtype: timestamp[ns]
-  - name: yearreportingdate
-    dtype: int64
-  - name: monthreportingdate
-    dtype: int64
-  - name: roundnumber
-    dtype: int64
-  - name: displacementreason
-    dtype: string
-  - name: idporiginadmin1name
-    dtype: string
-  - name: idporiginadmin1pcode
-    dtype: string
-  - name: assessmenttype
-    dtype: string
-  - name: operationstatus
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 14405
-    num_examples: 58
-  - name: test
-    num_bytes: 3697
-    num_examples: 15
-  download_size: 20945
-  dataset_size: 18102
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - 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.*