electricsheepafrica/africa-unep-wdpca-tcd

Protected and Conserved Areas (WDPCA) in Chad

Publisher: The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) · Source: HDX · License: cc-by-igo · Updated: 2026-03-03

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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: TCD.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Water, sanitation and hygiene (wash)
Unit of observation	Individual-level records
Rows (total)	7
Columns	35 (8 numeric, 27 categorical, 0 datetime)
Train split	5 rows
Test split	1 rows
Geographic scope	TCD
Publisher	The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)
HDX last updated	2026-03-03

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Variables

Geographic — site_type (PA), desig_type (International, National), status_yr (range 0.0–2008.0), gov_type, own_type and 4 others.

Identifier / Metadata — objectid (range 231.0–4957.0), site_id (range 14084.0–902874.0), site_pid (range 14084.0–902874.0), name_eng (Beinamar, Goz-Beida, Larmanaye), name (Beinamar, Goz-Beida, Larmanaye) and 3 others.

Other — desig (Wetland of International Importance (Ramsar Site), Faunal Reserve, National Park), desig_eng (Wetland of International Importance (Ramsar Site), Faunal Reserve, National Park), iucn_cat (Not Reported), int_crit (Not Applicable, (ii);(iii);(vii), (i);(ii);(iii);(v);(vii);(viii)), realm (Terrestrial) and 13 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unep-wdpca-tcd")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
objectid	int64	0.0%	231.0 – 4957.0 (mean 2324.1429)
site_id	int64	0.0%	14084.0 – 902874.0 (mean 408235.1429)
site_pid	int64	0.0%	14084.0 – 902874.0 (mean 408235.1429)
site_type	object	0.0%	PA
name_eng	object	0.0%	Beinamar, Goz-Beida, Larmanaye
name	object	0.0%	Beinamar, Goz-Beida, Larmanaye
desig	object	0.0%	Wetland of International Importance (Ramsar Site), Faunal Reserve, National Park
desig_eng	object	0.0%	Wetland of International Importance (Ramsar Site), Faunal Reserve, National Park
desig_type	object	0.0%	International, National
iucn_cat	object	0.0%	Not Reported
int_crit	object	0.0%	Not Applicable, (ii);(iii);(vii), (i);(ii);(iii);(v);(vii);(viii)
realm	object	0.0%	Terrestrial
rep_m_area	float64	0.0%	0.0 – 0.0 (mean 0.0)
rep_area	float64	0.0%	763.0 – 49220.0 (mean 18221.9543)
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 – 2008.0 (mean 1145.7143)
restrict	object	0.0%
gov_type	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%
verif	object	0.0%
inlnd_wtrs	object	0.0%
metadataid	int64	0.0%	1856.0 – 1965.0 (mean 1902.7143)
prnt_iso3	object	0.0%
iso3	object	0.0%
govsubtype	object	0.0%
ownsubtype	object	0.0%
oecm_asmt	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
objectid	231.0	4957.0	2324.1429	855.0
site_id	14084.0	902874.0	408235.1429	109008.0
site_pid	14084.0	902874.0	408235.1429	109008.0
rep_m_area	0.0	0.0	0.0	0.0
rep_area	763.0	49220.0	18221.9543	16481.68
no_tk_area	0.0	0.0	0.0	0.0
status_yr	0.0	2008.0	1145.7143	2001.0
metadataid	1856.0	1965.0	1902.7143	1856.0

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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.

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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 for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_unep_wdpca_tcd,
  title     = {Protected and Conserved Areas (WDPCA) in Chad},
  author    = {The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unep_wdpca_tcd},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.