README.md

metadata

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-classification
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - conflict-violence
  - food-security
  - cmr
  - cod
  - irq
  - lbn
  - mli
pretty_name: Conflict-Related Incidents Affecting Water Systems
dataset_info:
  splits:
    - name: train
      num_examples: 340
    - name: test
      num_examples: 85

Conflict-Related Incidents Affecting Water Systems

Publisher: Insecurity Insight · Source: HDX · License: cc-by-igo · Updated: 2026-05-04

---

Abstract

This page contains data on conflict events with clearly foreseeable impacts on or links to water systems based on agency-and open source events. Categorized by country. Covers Cameroon, DRC, Lebanon, Mali, Myanmar, Niger, oPt, Somalia, Sudan, Syria and Yemen. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the date, date_event_entered column(s). Geographic scope: CMR, COD, IRQ, LBN, MLI, NER, SSD, PSE, and 4 others.

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

---

Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Discrete events or incidents
Rows (total)	426
Columns	15 (2 numeric, 10 categorical, 3 datetime)
Train split	340 rows
Test split	85 rows
Geographic scope	CMR, COD, IRQ, LBN, MLI, NER, SSD, PSE, and 4 others
Publisher	Insecurity Insight
HDX last updated	2026-05-04

---

Variables

Geographic — country (OPT, Syria, Ukraine), country_iso (PSE, SYR, UKR), admin_1 (West Bank, Gaza Strip, South Governorate), water_infrastructure_category_affected (Water Distribution, Multi Purpose/Function Infrastructure, Water Storage), number_of_attacks_damaging_destroying_water_infrastructure (range 1.0–7.0).

Temporal — date, date_event_entered, date_event_modified.

Identifier / Metadata — reported_perpetrator_name (Israeli Defence Forces, Armed Forces of the Russian Federation, Unidentified Armed Actor), sind_event_id (range 4537.0–126962.0), esa_source (HDX), esa_processed (2026-05-04).

Other — geo_precision (censored), reported_perpetrator (Government: Military, Foreign Forces: Military, NSA), weapon_carried_used (Firearms, Aerial Bomb: Plane, Aerial Bomb: Drone).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-food-security-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
date	datetime64[ns]	0.0%
country	object	0.0%	OPT, Syria, Ukraine
country_iso	object	0.0%	PSE, SYR, UKR
admin_1	object	0.0%	West Bank, Gaza Strip, South Governorate
geo_precision	object	0.0%	censored
reported_perpetrator	object	0.0%	Government: Military, Foreign Forces: Military, NSA
reported_perpetrator_name	object	0.0%	Israeli Defence Forces, Armed Forces of the Russian Federation, Unidentified Armed Actor
weapon_carried_used	object	0.0%	Firearms, Aerial Bomb: Plane, Aerial Bomb: Drone
water_infrastructure_category_affected	object	8.9%	Water Distribution, Multi Purpose/Function Infrastructure, Water Storage
number_of_attacks_damaging_destroying_water_infrastructure	float64	36.4%	1.0 – 7.0 (mean 1.0554)
sind_event_id	int64	0.0%	4537.0 – 126962.0 (mean 105978.0587)
date_event_entered	datetime64[ns]	0.0%
date_event_modified	datetime64[ns]	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-04

---

Numeric Summary

Column	Min	Max	Mean	Median
number_of_attacks_damaging_destroying_water_infrastructure	1.0	7.0	1.0554	1.0
sind_event_id	4537.0	126962.0	105978.0587	100152.5

---

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. 13 column(s) with >80% missing values were removed: event_description, latitude, longitude, number_of_attacks_where_water_infrastructure_was_contaminated, number_of_attacks_where_water_infrastructure_was_looted, number_of_attacks_where_water_infrastructure_was_obstructed.... 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 Insecurity Insight 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: number_of_attacks_damaging_destroying_water_infrastructure.
• This dataset spans 12 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_asia_food_security_all,
  title     = {Conflict-Related Incidents Affecting Water Systems},
  author    = {Insecurity Insight},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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