electricsheepafrica/africa-gis-survey-of-daro-lebu-woreda-education-facility-distribution

---
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:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- education-facilities-schools
- geodata
- eth
pretty_name: "GIS survey of Daro Lebu woreda education facility distribution"
dataset_info:
splits:
- name: train
num_examples: 39
- name: test
num_examples: 9
---

# GIS survey of Daro Lebu woreda education facility distribution

**Publisher:** International Rescue Committee Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/gis-survey-of-daro-lebu-woreda-education-facility-distribution) · **License:** `cc-by` · **Updated:** 2024-10-07

---

## Abstract

Daro lebu Woreda Education location facilities

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

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 49 |
| **Columns** | 21 (6 numeric, 14 categorical, 1 datetime) |
| **Train split** | 39 rows |
| **Test split** | 9 rows |
| **Geographic scope** | ETH |
| **Publisher** | International Rescue Committee Ethiopia |
| **HDX last updated** | 2024-10-07 |

---

## Variables

**Geographic** — `woreda` (Darolebu), `lon` (range 643255.0–676915.0), `lat` (range 924208.0–959460.0), `symbol` (Waypoint), `category` (Education).

**Temporal** — `date`.

**Demographic** — `village` (Mariyam, Goro, Gerbi).

**Identifier / Metadata** — `objectid` (range 1.0–49.0), `name_of_da` (Murad, Shimelis), `code` (range 248.0–738.0), `esa_source`, `esa_processed`.

**Other** — `altitude` (range 1540.1–1902.1), `kebele_pa` (Micheta Town, Mechara Town 01, Kotera), `school_fac` (Primary & Secondary school (G 1-8th), 1st Cycle primary school (G1-4), Secondary school (G9-10th)), `is_there_w` (Yes, No), `is_there_s` (Yes, No) and 4 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-gis-survey-of-daro-lebu-woreda-education-facility-distribution")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `objectid` | int64 | 0.0% | 1.0 – 49.0 (mean 25.0) |
| `woreda` | object | 0.0% | Darolebu |
| `name_of_da` | object | 0.0% | Murad, Shimelis |
| `code` | int64 | 0.0% | 248.0 – 738.0 (mean 472.7143) |
| `lon` | int64 | 0.0% | 643255.0 – 676915.0 (mean 653289.0612) |
| `lat` | int64 | 0.0% | 924208.0 – 959460.0 (mean 941554.3265) |
| `altitude` | float64 | 0.0% | 1540.1 – 1902.1 (mean 1693.6041) |
| `date` | datetime64[ns] | 0.0% | |
| `symbol` | object | 0.0% | Waypoint |
| `kebele_pa` | object | 0.0% | Micheta Town, Mechara Town 01, Kotera |
| `village` | object | 0.0% | Mariyam, Goro, Gerbi |
| `category` | object | 0.0% | Education |
| `school_fac` | object | 0.0% | Primary & Secondary school (G 1-8th), 1st Cycle primary school (G1-4), Secondary school (G9-10th) |
| `is_there_w` | object | 2.0% | Yes, No |
| `is_there_s` | object | 2.0% | Yes, No |
| `no_of_room` | int64 | 0.0% | 0.0 – 16.0 (mean 6.2857) |
| `is_it_labe` | object | 6.1% | Yes, No |
| `sanitation` | object | 6.1% | |
| `remark` | object | 8.2% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `objectid` | 1.0 | 49.0 | 25.0 | 25.0 |
| `code` | 248.0 | 738.0 | 472.7143 | 465.0 |
| `lon` | 643255.0 | 676915.0 | 653289.0612 | 652127.0 |
| `lat` | 924208.0 | 959460.0 | 941554.3265 | 942961.0 |
| `altitude` | 1540.1 | 1902.1 | 1693.6041 | 1686.0 |
| `no_of_room` | 0.0 | 16.0 | 6.2857 | 8.0 |

---

## 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`. 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 Rescue Committee Ethiopia 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/gis-survey-of-daro-lebu-woreda-education-facility-distribution) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_gis_survey_of_daro_lebu_woreda_education_facility_distribution,
title = {GIS survey of Daro Lebu woreda education facility distribution},
author = {International Rescue Committee Ethiopia},
year = {2024},
url = {https://data.humdata.org/dataset/gis-survey-of-daro-lebu-woreda-education-facility-distribution},
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.*