electricsheepafrica/africa-unep-wdpca-ssd

---
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-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- environment
- geodata
- ssd
pretty_name: "Protected and Conserved Areas (WDPCA) in South Sudan"
dataset_info:
splits:
- name: train
num_examples: 19
- name: test
num_examples: 4
---

# Protected and Conserved Areas (WDPCA) in South Sudan

**Publisher:** The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) · **Source:** [HDX](https://data.humdata.org/dataset/unep_wdpca_ssd) · **License:** `cc-by-igo` · **Updated:** 2026-03-03

---

## Abstract

The World Database on Protected and Conserved Areas (WDPCA) combines the formerly separate World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM). The WDPCA is the most comprehensive global database of marine and terrestrial protected areas and other effective area-based conservation measures, updated on a monthly basis, and is one of the key global biodiversity datasets being widely used by scientists, businesses, governments, international secretariats, and others to inform planning, policy decisions, and management.

The WDPCA is part of the Protected Planet Initiative, a joint product of the UN Environment Programme and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPCA is carried out by the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments and other stakeholders. Data and information on the world's protected and conserved areas compiled in the WDPCA is used for reporting on progress towards reaching Target 3 of the Kunming-Montreal Global Biodiversity Framework, which calls for 30% of the world’s land and waters to be effectively conserved by 2030.

Additionally, the WDPCA is used for reporting to the UN to track progress towards the 2030 Sustainable Development Goals, tracking of core indicators of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), and providing information for other international assessments and reports including the Global Biodiversity Outlook. UNEP-WCMC and IUCN periodically release the Protected Planet Report on the status of the world's protected and conserved areas.

Many platforms are incorporating the WDPCA to provide integrated information to diverse users, including businesses and governments, in a range of sectors. For example, the WDPCA is included in the Integrated Biodiversity Assessment Tool (IBAT), an innovative decision support tool that gives commercial users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary.

The reach of the WDPCA is further enhanced by the UN Biodiversity Lab as well as services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPCA demonstrate the growing value and significance of the Protected Planet initiative.

Each row in this dataset represents individual-level records. Data was last updated on HDX on 2026-03-03. Geographic scope: **SSD**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Water, sanitation and hygiene (wash) |
| **Unit of observation** | Individual-level records |
| **Rows (total)** | 24 |
| **Columns** | 39 (12 numeric, 27 categorical, 0 datetime) |
| **Train split** | 19 rows |
| **Test split** | 4 rows |
| **Geographic scope** | SSD |
| **Publisher** | The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) |
| **HDX last updated** | 2026-03-03 |

---

## Variables

**Geographic** — `site_type` (PA), `desig_type` (National, International), `status_yr` (range 0.0–2006.0), `gov_type`, `own_type` and 4 others.

**Identifier / Metadata** — `objectid` (range 619.0–171171.0), `site_id` (range 903.0–555583111.0), `site_pid` (range 903.0–555583111.0), `name_eng` (Southern, Nimule, Badingilo Extension), `name` (Southern, Nimule, Badingilo Extension) and 3 others.

**Other** — `desig` (Game Reserve, National Park, Forest Reserve), `desig_eng` (Game Reserve, National Park, Forest Reserve), `iucn_cat` (VI, II, V), `int_crit` (Not Applicable, (i);(ii);(iii);(iv);(v);(vi);(vii);(viii)), `realm` (Terrestrial) and 17 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-unep-wdpca-ssd")
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% | 619.0 – 171171.0 (mean 55638.2917) |
| `site_id` | int64 | 0.0% | 903.0 – 555583111.0 (mean 162085699.7917) |
| `site_pid` | int64 | 0.0% | 903.0 – 555583111.0 (mean 162085699.7917) |
| `site_type` | object | 0.0% | PA |
| `name_eng` | object | 0.0% | Southern, Nimule, Badingilo Extension |
| `name` | object | 0.0% | Southern, Nimule, Badingilo Extension |
| `desig` | object | 0.0% | Game Reserve, National Park, Forest Reserve |
| `desig_eng` | object | 0.0% | Game Reserve, National Park, Forest Reserve |
| `desig_type` | object | 0.0% | National, International |
| `iucn_cat` | object | 0.0% | VI, II, V |
| `int_crit` | object | 0.0% | Not Applicable, (i);(ii);(iii);(iv);(v);(vi);(vii);(viii) |
| `realm` | object | 0.0% | Terrestrial |
| `rep_m_area` | float64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `gis_m_area` | float64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `rep_area` | float64 | 0.0% | 0.0 – 57000.0 (mean 6942.4583) |
| `gis_area` | float64 | 0.0% | 54.549 – 24217.3847 (mean 5424.5714) |
| `no_take` | object | 0.0% | Not Applicable |
| `no_tk_area` | float64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `status` | object | 0.0% | |
| `status_yr` | int64 | 0.0% | 0.0 – 2006.0 (mean 1714.4167) |
| `restrict` | object | 0.0% | |
| `gov_type` | object | 0.0% | |
| `verif` | object | 0.0% | |
| `inlnd_wtrs` | object | 0.0% | |
| `own_type` | object | 0.0% | |
| `mang_auth` | object | 0.0% | |
| `mang_plan` | object | 0.0% | |
| `cons_obj` | object | 0.0% | |
| `supp_info` | object | 0.0% | |
| `metadataid` | int64 | 0.0% | 1847.0 – 1856.0 (mean 1847.375) |
| `prnt_iso3` | object | 0.0% | |
| `iso3` | object | 0.0% | |
| `govsubtype` | object | 0.0% | |
| `ownsubtype` | object | 0.0% | |
| `oecm_asmt` | object | 0.0% | |
| `shape_area` | float64 | 0.0% | 0.0044 – 1.9881 (mean 0.4439) |
| `shape_length` | float64 | 0.0% | 0.375 – 12.6088 (mean 3.099) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `objectid` | 619.0 | 171171.0 | 55638.2917 | 3766.5 |
| `site_id` | 903.0 | 555583111.0 | 162085699.7917 | 10735.5 |
| `site_pid` | 903.0 | 555583111.0 | 162085699.7917 | 10735.5 |
| `rep_m_area` | 0.0 | 0.0 | 0.0 | 0.0 |
| `gis_m_area` | 0.0 | 0.0 | 0.0 | 0.0 |
| `rep_area` | 0.0 | 57000.0 | 6942.4583 | 1350.0 |
| `gis_area` | 54.549 | 24217.3847 | 5424.5714 | 2303.188 |
| `no_tk_area` | 0.0 | 0.0 | 0.0 | 0.0 |
| `status_yr` | 0.0 | 2006.0 | 1714.4167 | 1939.0 |
| `metadataid` | 1847.0 | 1856.0 | 1847.375 | 1847.0 |
| `shape_area` | 0.0044 | 1.9881 | 0.4439 | 0.1879 |
| `shape_length` | 0.375 | 12.6088 | 3.099 | 2.1945 |

---

## 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 The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) 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/unep_wdpca_ssd) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_unep_wdpca_ssd,
title = {Protected and Conserved Areas (WDPCA) in South Sudan},
author = {The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)},
year = {2026},
url = {https://data.humdata.org/dataset/unep_wdpca_ssd},
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.*