electricsheepafrica/africa-ucdp-data-for-south-africa

South Africa - Data on Conflict Events

Publisher: HDX · Source: HDX · License: cc-by-igo · Updated: 2026-04-03

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Abstract

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the date_start, date_end column(s). Geographic scope: ZAF.

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

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Dataset Characteristics

Domain	Conflict and security
Unit of observation	First-level administrative unit observations
Rows (total)	2,895
Columns	47 (26 numeric, 18 categorical, 2 datetime)
Train split	2,316 rows
Test split	579 rows
Geographic scope	ZAF
Publisher	HDX
HDX last updated	2026-04-03

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Variables

Geographic — year (range 1989.0–2024.0), active_year, type_of_violence (range 1.0–3.0), dyad_dset_id (range 102.0–16983.0), dyad_new_id (range 641.0–16983.0) and 9 others.

Temporal — date_prec (range 1.0–5.0), date_start, date_end.

Outcome / Measurement — number_of_sources (range -1.0–6.0), deaths_a (range 0.0–33.0), deaths_b, deaths_civilians, deaths_unknown.

Identifier / Metadata — id (range 13263.0–551051.0), relid (SAF-1989-1-516-8, SAF-1992-2-312-8, SAF-1992-2-312-300), code_status (Clear), conflict_dset_id (range 102.0–16983.0), conflict_new_id (range 298.0–15477.0) and 12 others.

Other — where_prec (range 1.0–6.0), where_description (Mpumalanga town (township), Soweto town (township), Alexandra town (township) (outskirts of Johannesburg town)), adm_1, adm_2, geom_wkt and 3 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ucdp-data-for-south-africa")
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
id	int64	0.0%	13263.0 – 551051.0 (mean 30819.6062)
relid	object	0.0%	SAF-1989-1-516-8, SAF-1992-2-312-8, SAF-1992-2-312-300
year	int64	0.0%	1989.0 – 2024.0 (mean 1992.3119)
active_year	bool	0.0%
code_status	object	0.0%	Clear
type_of_violence	int64	0.0%	1.0 – 3.0 (mean 2.1755)
conflict_dset_id	int64	0.0%	102.0 – 16983.0 (mean 4349.7606)
conflict_new_id	int64	0.0%	298.0 – 15477.0 (mean 3950.7244)
conflict_name	object	0.0%	Supporters of ANC - Supporters of IFP, Government of South Africa - Civilians, Supporters of IFP - Supporters of UDF
dyad_dset_id	int64	0.0%	102.0 – 16983.0 (mean 4356.514)
dyad_new_id	int64	0.0%	641.0 – 16983.0 (mean 4528.3606)
dyad_name	object	0.0%	Supporters of ANC - Supporters of IFP, Government of South Africa - Civilians, Supporters of IFP - Supporters of UDF
side_a_dset_id	int64	0.0%	102.0 – 8245.0 (mean 880.6877)
side_a_new_id	int64	0.0%	102.0 – 8245.0 (mean 880.6877)
side_a	object	0.0%	Supporters of ANC, Government of South Africa, Supporters of IFP
side_b_dset_id	int64	0.0%	461.0 – 9999.0 (mean 2495.0967)
side_b_new_id	int64	0.0%	1.0 – 8244.0 (mean 543.8463)
side_b	object	0.0%	Supporters of IFP, Civilians, Supporters of UDF
number_of_sources	int64	0.0%	-1.0 – 6.0 (mean -0.8984)
source_article	object	0.0%	The report of the Truth and Reconciliation Commission (TRC report) https://www.justice.gov.za/trc/report/, "warinangola.com/,2007-12-31,Operation Merlyn, 1989: The Nine Days War", CSVR "From Low Intensity War to Mafia War: Taxi Violence in South Africa 1987-2000", Jackie Dugard, Violence and Transition Series, vol. 4, May 2001
source_original	object	76.8%	police, SA, Police
where_prec	int64	0.0%	1.0 – 6.0 (mean 1.277)
where_coordinates	object	0.0%	Empangeni town, Port Shepstone town, Cape Town city
where_description	object	0.9%	Mpumalanga town (township), Soweto town (township), Alexandra town (township) (outskirts of Johannesburg town)
adm_1	object	0.4%
adm_2	object	13.7%
latitude	float64	0.0%	-34.1831 – -17.3362 (mean -28.7203)
longitude	float64	0.0%	13.0 – 32.3644 (mean 29.1114)
geom_wkt	object	0.0%
priogrid_gid	int64	0.0%	80318.0 – 104794.0 (mean 88325.571)
country	object	0.0%
iso3	object	0.0%
country_id	int64	0.0%	560.0 – 560.0 (mean 560.0)
region	object	0.0%
event_clarity	int64	0.0%	1.0 – 2.0 (mean 1.0756)
date_prec	int64	0.0%	1.0 – 5.0 (mean 1.362)
date_start	datetime64[ns]	0.0%
date_end	datetime64[ns]	0.0%
deaths_a	int64	0.0%	0.0 – 33.0 (mean 0.4069)
deaths_b	int64	0.0%
deaths_civilians	int64	0.0%
deaths_unknown	int64	0.0%
best	int64	0.0%
high	int64	0.0%
low	int64	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	13263.0	551051.0	30819.6062	16095.0
year	1989.0	2024.0	1992.3119	1992.0
type_of_violence	1.0	3.0	2.1755	2.0
conflict_dset_id	102.0	16983.0	4349.7606	5450.0
conflict_new_id	298.0	15477.0	3950.7244	4840.0
dyad_dset_id	102.0	16983.0	4356.514	5450.0
dyad_new_id	641.0	16983.0	4528.3606	5450.0
side_a_dset_id	102.0	8245.0	880.6877	1086.0
side_a_new_id	102.0	8245.0	880.6877	1086.0
side_b_dset_id	461.0	9999.0	2495.0967	620.0
side_b_new_id	1.0	8244.0	543.8463	620.0
number_of_sources	-1.0	6.0	-0.8984	-1.0
where_prec	1.0	6.0	1.277	1.0
latitude	-34.1831	-17.3362	-28.7203	-29.5169
longitude	13.0	32.3644	29.1114	30.3506

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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. 5 column(s) with >80% missing values were removed: source_office, source_date, source_headline, gwnoa, gwnob. 2 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.

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Limitations

• Data originates from HDX and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: source_original.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_ucdp_data_for_south_africa,
  title     = {South Africa - Data on Conflict Events},
  author    = {HDX},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ucdp-data-for-south-africa},
  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.