electricsheepafrica/africa-unep-wdpca-nam

Protected and Conserved Areas (WDPCA) in Namibia

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: NAM.

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)	177
Columns	39 (12 numeric, 27 categorical, 0 datetime)
Train split	141 rows
Test split	35 rows
Geographic scope	NAM
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 (National, International), status_yr (range 1968.0–2022.0), gov_type, own_type and 4 others.

Identifier / Metadata — objectid (range 607.0–297353.0), site_id (range 883.0–555782780.0), site_pid (range 883.0–555782780.0), name_eng (Sikunga, Okondjombo, Otjitanda), name (Fish Protection Area, Muduva Nyangana, Orupembe) and 3 others.

Other — desig (Communal Conservancy, Community Forest, National Park), desig_eng (Communal Conservancy, Community Forest, National Park), iucn_cat (Not Reported, VI, II), int_crit (Not Applicable, (i);(ii);(iv);(v);(vi), (i);(iii);(iv);(v);(vi)), realm (Terrestrial, Coastal, Marine) and 17 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unep-wdpca-nam")
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%	607.0 – 297353.0 (mean 153177.5537)
site_id	int64	0.0%	883.0 – 555782780.0 (mean 483405181.9435)
site_pid	int64	0.0%	883.0 – 555782780.0 (mean 483405181.9435)
site_type	object	0.0%	PA
name_eng	object	0.0%	Sikunga, Okondjombo, Otjitanda
name	object	0.0%	Fish Protection Area, Muduva Nyangana, Orupembe
desig	object	0.0%	Communal Conservancy, Community Forest, National Park
desig_eng	object	0.0%	Communal Conservancy, Community Forest, National Park
desig_type	object	0.0%	National, International
iucn_cat	object	0.0%	Not Reported, VI, II
int_crit	object	0.0%	Not Applicable, (i);(ii);(iv);(v);(vi), (i);(iii);(iv);(v);(vi)
realm	object	0.0%	Terrestrial, Coastal, Marine
rep_m_area	float64	0.0%	0.0 – 9497.0 (mean 53.9155)
gis_m_area	float64	0.0%	0.0 – 9425.9294 (mean 53.7714)
rep_area	float64	0.0%	0.0091 – 50766.0 (mean 2379.5632)
gis_area	float64	0.0%	0.0091 – 50766.1525 (mean 2446.243)
no_take	object	0.0%	Not Applicable, Not Reported
no_tk_area	float64	0.0%	0.0 – 0.0 (mean 0.0)
status	object	0.0%
status_yr	int64	0.0%	1968.0 – 2022.0 (mean 2007.0678)
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%	946.0 – 1856.0 (mean 1817.0452)
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%	0.0 – 4.5245 (mean 0.2132)
shape_length	float64	0.0%	0.0055 – 14.4151 (mean 1.771)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
objectid	607.0	297353.0	153177.5537	141680.0
site_id	883.0	555782780.0	483405181.9435	555542974.0
site_pid	883.0	555782780.0	483405181.9435	555542974.0
rep_m_area	0.0	9497.0	53.9155	0.0
gis_m_area	0.0	9425.9294	53.7714	0.0
rep_area	0.0091	50766.0	2379.5632	655.88
gis_area	0.0091	50766.1525	2446.243	637.9996
no_tk_area	0.0	0.0	0.0	0.0
status_yr	1968.0	2022.0	2007.0678	2008.0
metadataid	946.0	1856.0	1817.0452	1821.0
shape_area	0.0	4.5245	0.2132	0.0547
shape_length	0.0055	14.4151	1.771	1.1787

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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_nam,
  title     = {Protected and Conserved Areas (WDPCA) in Namibia},
  author    = {The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unep_wdpca_nam},
  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.