electricsheepafrica/africa-ucdp-data-for-zimbabwe

Zimbabwe - 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: ZWE.

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)	68
Columns	48 (27 numeric, 18 categorical, 2 datetime)
Train split	54 rows
Test split	13 rows
Geographic scope	ZWE
Publisher	HDX
HDX last updated	2026-04-03

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Variables

Geographic — year (range 1989.0–2020.0), active_year, type_of_violence (range 3.0–3.0), dyad_dset_id (range 101.0–498.0), dyad_new_id (range 943.0–1029.0) and 9 others.

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

Outcome / Measurement — number_of_sources (range -1.0–3.0), deaths_a (range 0.0–0.0), deaths_b, deaths_civilians, deaths_unknown.

Identifier / Metadata — id (range 15245.0–435646.0), relid (ZIM-1995-3-552-2, ZIM-2008-3-552-46, ZIM-2011-3-552-1), code_status (Clear), conflict_dset_id (range 101.0–498.0), conflict_new_id (range 476.0–562.0) and 12 others.

Other — where_prec (range 1.0–6.0), where_description (Zimbabwe, Chiadzwa, a part of Marange, Harare city (near)), adm_1, adm_2, geom_wkt and 4 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ucdp-data-for-zimbabwe")
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%	15245.0 – 435646.0 (mean 76156.7941)
relid	object	0.0%	ZIM-1995-3-552-2, ZIM-2008-3-552-46, ZIM-2011-3-552-1
year	int64	0.0%	1989.0 – 2020.0 (mean 2007.3824)
active_year	bool	0.0%
code_status	object	0.0%	Clear
type_of_violence	int64	0.0%	3.0 – 3.0 (mean 3.0)
conflict_dset_id	int64	0.0%	101.0 – 498.0 (mean 136.0294)
conflict_new_id	int64	0.0%	476.0 – 562.0 (mean 483.5882)
conflict_name	object	0.0%	Government of Zimbabwe (Rhodesia) - Civilians, Renamo - Civilians
dyad_dset_id	int64	0.0%	101.0 – 498.0 (mean 136.0294)
dyad_new_id	int64	0.0%	943.0 – 1029.0 (mean 950.5882)
dyad_name	object	0.0%	Government of Zimbabwe (Rhodesia) - Civilians, Renamo - Civilians
side_a_dset_id	int64	0.0%	101.0 – 498.0 (mean 136.0294)
side_a_new_id	int64	0.0%	101.0 – 498.0 (mean 136.0294)
side_a	object	0.0%	Government of Zimbabwe (Rhodesia), Renamo
side_b_dset_id	int64	0.0%	9999.0 – 9999.0 (mean 9999.0)
side_b_new_id	int64	0.0%	1.0 – 1.0 (mean 1.0)
side_b	object	0.0%	Civilians
number_of_sources	int64	0.0%	-1.0 – 3.0 (mean -0.5147)
source_article	object	0.0%	HRW 2009-06-26 "Diamonds in the rough" http://www.hrw.org/sites/default/files/reports/zimbabwe0609web.pdf, SPT 2008-05-21, "Punishing dissent, silencing citizens: The Zimbabwe elections 2008, Amnesty International2008-06-20 "Twelve bodies found in Zimbabwe - victims tortured to death
source_original	object	11.8%	MDC, victim interviews, Zimbabwe Association of Doctors for Human Rights
where_prec	int64	0.0%	1.0 – 6.0 (mean 2.0588)
where_coordinates	object	0.0%	Harare town, Chiadzwa mining area, Zimbabwe (Rhodesia)
where_description	object	0.0%	Zimbabwe, Chiadzwa, a part of Marange, Harare city (near)
adm_1	object	11.8%
adm_2	object	19.1%
latitude	float64	0.0%	-21.3584 – -16.4051 (mean -18.329)
longitude	float64	0.0%	25.8307 – 32.8333 (mean 30.9633)
geom_wkt	object	0.0%
priogrid_gid	int64	0.0%	99062.0 – 106263.0 (mean 103372.0735)
country	object	0.0%
iso3	object	0.0%
country_id	int64	0.0%	552.0 – 552.0 (mean 552.0)
region	object	0.0%
event_clarity	int64	0.0%	1.0 – 2.0 (mean 1.1324)
date_prec	int64	0.0%	1.0 – 5.0 (mean 2.0147)
date_start	datetime64[ns]	0.0%
date_end	datetime64[ns]	0.0%
deaths_a	int64	0.0%	0.0 – 0.0 (mean 0.0)
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%
gwnoa	float64	8.8%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	15245.0	435646.0	76156.7941	28544.5
year	1989.0	2020.0	2007.3824	2008.0
type_of_violence	3.0	3.0	3.0	3.0
conflict_dset_id	101.0	498.0	136.0294	101.0
conflict_new_id	476.0	562.0	483.5882	476.0
dyad_dset_id	101.0	498.0	136.0294	101.0
dyad_new_id	943.0	1029.0	950.5882	943.0
side_a_dset_id	101.0	498.0	136.0294	101.0
side_a_new_id	101.0	498.0	136.0294	101.0
side_b_dset_id	9999.0	9999.0	9999.0	9999.0
side_b_new_id	1.0	1.0	1.0	1.0
number_of_sources	-1.0	3.0	-0.5147	-1.0
where_prec	1.0	6.0	2.0588	1.0
latitude	-21.3584	-16.4051	-18.329	-17.9664
longitude	25.8307	32.8333	30.9633	31.056

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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. 4 column(s) with >80% missing values were removed: source_office, source_date, source_headline, 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.
• 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_zimbabwe,
  title     = {Zimbabwe - Data on Conflict Events},
  author    = {HDX},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ucdp-data-for-zimbabwe},
  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.