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README.md
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---
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dataset_info:
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-
features:
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- name: date
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dtype: timestamp[ns]
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- name: country
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dtype: string
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- name: country_iso
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dtype: string
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- name: admin_1
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dtype: string
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- name: geo_precision
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dtype: string
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- name: location_of_incident
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dtype: string
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- name: reported_perpetrator
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dtype: string
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- name: reported_perpetrator_name
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dtype: string
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- name: weapon_carried_used
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dtype: string
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- name: organisation_affected
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dtype: string
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- name: programme_focus
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dtype: string
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- name: aid_workers_killed
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dtype: int64
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- name: aid_workers_injured
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dtype: int64
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- name: aid_workers_kidnapped
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dtype: int64
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- name: aid_workers_arrested
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dtype: int64
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- name: known_kidnapping_or_arrest_outcome
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dtype: string
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- name: aid_workers_killed_in_captivity
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dtype: int64
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- name: international_aid_workers_killed
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dtype: int64
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- name: international_aid_workers_killed_in_captivity
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dtype: int64
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- name: national_aid_workers_killed
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dtype: int64
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- name: national_aid_workers_killed_in_captivity
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dtype: int64
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- name: female_aid_workers_killed
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dtype: int64
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- name: female_aid_workers_killed_in_captivity
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dtype: int64
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- name: male_aid_workers_killed
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dtype: int64
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- name: male_aid_workers_killed_in_captivity
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dtype: int64
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- name: international_aid_workers_injured
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dtype: int64
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- name: national_aid_workers_injured
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dtype: int64
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- name: female_aid_workers_injured
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dtype: int64
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- name: male_aid_workers_injured
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dtype: int64
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- name: international_aid_workers_kidnapped
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dtype: int64
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- name: national_aid_workers_kidnapped
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dtype: int64
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- name: female_aid_workers_kidnapped
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dtype: int64
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- name: male_aid_workers_kidnapped
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dtype: int64
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-
- name: international_aid_workers_arrested
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dtype: int64
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-
- name: national_aid_workers_arrested
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dtype: int64
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- name: female_aid_workers_arrested
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dtype: int64
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- name: male_aid_workers_arrested
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dtype: int64
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- name: sind_event_id
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dtype: int64
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- name: date_event_entered
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dtype: timestamp[ns]
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- name: date_event_modified
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dtype: timestamp[ns]
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- name: esa_source
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dtype: string
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- name: esa_processed
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dtype: string
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splits:
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-
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-
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-
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-
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num_bytes: 26224
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num_examples: 67
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download_size: 56441
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dataset_size: 129910
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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---
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| 1 |
---
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+
annotations_creators:
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- no-annotation
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language_creators:
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- found
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language:
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- en
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license: cc-by-sa-4.0
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multilinguality:
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- monolingual
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+
size_categories:
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+
- n<1K
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+
source_datasets:
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- original
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task_categories:
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- tabular-classification
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- tabular-regression
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- other
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task_ids: []
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tags:
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- africa
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- humanitarian
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- hdx
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- electric-sheep-africa
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- aid-worker-security
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- aid-workers
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- complex-emergency-conflict-security
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- conflict-violence
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- damage-assessment
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- disease
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- education
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- education-facilities-schools
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- som
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pretty_name: "Somalia (SOM): Attacks on Aid Operations, Education, Food and Water Systems and Health Care"
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dataset_info:
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splits:
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- name: train
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num_examples: 267
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- name: test
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num_examples: 66
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---
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| 42 |
+
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# Somalia (SOM): Attacks on Aid Operations, Education, Food and Water Systems and Health Care
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**Publisher:** Insecurity Insight · **Source:** [HDX](https://data.humdata.org/dataset/somalia-violence-agaiinst-civilian-and-vital-civilian-facilitites) · **License:** `cc-by-sa` · **Updated:** 2026-04-06
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---
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## Abstract
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This page contains information on reported incidents of violence and threats affecting aid operations and workers, education, food systems and health care services in [Somalia](https://insecurityinsight.org/country-pages/somalia). Also included are datasets cited in the [Safeguarding Health in Conflict Coalition (SHCC)'s](https://www.safeguardinghealth.org/) annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org
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Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the `date`, `date_event_entered` column(s). Geographic scope: **SOM**.
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*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
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---
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## Dataset Characteristics
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| 60 |
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| | |
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|---|---|
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| **Domain** | Food security and nutrition |
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| **Unit of observation** | Discrete events or incidents |
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| **Rows (total)** | 334 |
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| **Columns** | 42 (26 numeric, 13 categorical, 3 datetime) |
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| **Train split** | 267 rows |
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| **Test split** | 66 rows |
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| **Geographic scope** | SOM |
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| **Publisher** | Insecurity Insight |
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| **HDX last updated** | 2026-04-06 |
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---
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| 74 |
+
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## Variables
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| 76 |
+
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**Geographic** — `country` (Somalia), `country_iso` (SOM), `admin_1` (Banaadir, No Information, Bay), `location_of_incident` (Road, No information, Compound or Office Building), `aid_workers_killed_in_captivity` (range 0.0–7.0) and 4 others.
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| 78 |
+
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**Temporal** — `date`, `date_event_entered`, `date_event_modified`.
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| 80 |
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**Demographic** — `female_aid_workers_killed` (range 0.0–2.0), `male_aid_workers_killed` (range 0.0–3.0), `female_aid_workers_injured` (range 0.0–2.0), `male_aid_workers_injured` (range 0.0–5.0), `female_aid_workers_kidnapped` (range 0.0–3.0) and 3 others.
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**Outcome / Measurement** — `organisation_affected` (INGO, UN Agency, LNGO).
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**Identifier / Metadata** — `reported_perpetrator_name` (Unidentified armed actor, Al-Shabaab, Militia), `aid_workers_killed` (range 0.0–14.0), `aid_workers_injured` (range 0.0–7.0), `aid_workers_kidnapped` (range 0.0–12.0), `aid_workers_arrested` (range 0.0–6.0) and 12 others.
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**Other** — `geo_precision` (censored), `reported_perpetrator` (NSA, No Information, Police), `weapon_carried_used` (Firearms, No Information on the Weapon Used, Hand Grenade), `programme_focus` (No information, Health, Hunger).
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| 88 |
+
|
| 89 |
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---
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| 90 |
+
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## Quick Start
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| 92 |
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| 93 |
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```python
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| 94 |
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from datasets import load_dataset
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| 95 |
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ds = load_dataset("electricsheepafrica/africa-somalia-violence-agaiinst-civilian-and-vital-civilian-facilitites")
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train = ds["train"].to_pandas()
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test = ds["test"].to_pandas()
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print(train.shape)
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train.head()
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```
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---
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## Schema
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| 107 |
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| Column | Type | Null % | Range / Sample Values |
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| 109 |
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|---|---|---|---|
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| 110 |
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| `date` | datetime64[ns] | 0.0% | |
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| 111 |
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| `country` | object | 0.0% | Somalia |
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| 112 |
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| `country_iso` | object | 0.0% | SOM |
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| 113 |
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| `admin_1` | object | 0.0% | Banaadir, No Information, Bay |
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| 114 |
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| `geo_precision` | object | 0.0% | censored |
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| 115 |
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| `location_of_incident` | object | 0.0% | Road, No information, Compound or Office Building |
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| 116 |
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| `reported_perpetrator` | object | 0.0% | NSA, No Information, Police |
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| 117 |
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| `reported_perpetrator_name` | object | 0.0% | Unidentified armed actor, Al-Shabaab, Militia |
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| 118 |
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| `weapon_carried_used` | object | 0.0% | Firearms, No Information on the Weapon Used, Hand Grenade |
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| 119 |
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| `organisation_affected` | object | 0.0% | INGO, UN Agency, LNGO |
|
| 120 |
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| `programme_focus` | object | 0.0% | No information, Health, Hunger |
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| 121 |
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| `aid_workers_killed` | int64 | 0.0% | 0.0 – 14.0 (mean 0.6198) |
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| 122 |
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| `aid_workers_injured` | int64 | 0.0% | 0.0 – 7.0 (mean 0.5329) |
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| 123 |
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| `aid_workers_kidnapped` | int64 | 0.0% | 0.0 – 12.0 (mean 0.8174) |
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| 124 |
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| `aid_workers_arrested` | int64 | 0.0% | 0.0 – 6.0 (mean 0.1347) |
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| 125 |
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| `known_kidnapping_or_arrest_outcome` | object | 62.9% | |
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| 126 |
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| `aid_workers_killed_in_captivity` | int64 | 0.0% | 0.0 – 7.0 (mean 0.0329) |
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| 127 |
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| `international_aid_workers_killed` | int64 | 0.0% | 0.0 – 4.0 (mean 0.0749) |
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| 128 |
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| `international_aid_workers_killed_in_captivity` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
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| 129 |
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| `national_aid_workers_killed` | int64 | 0.0% | 0.0 – 14.0 (mean 0.485) |
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| 130 |
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| `national_aid_workers_killed_in_captivity` | int64 | 0.0% | 0.0 – 7.0 (mean 0.0329) |
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| 131 |
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| `female_aid_workers_killed` | int64 | 0.0% | 0.0 – 2.0 (mean 0.0479) |
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| 132 |
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| `female_aid_workers_killed_in_captivity` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
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| 133 |
+
| `male_aid_workers_killed` | int64 | 0.0% | 0.0 – 3.0 (mean 0.3772) |
|
| 134 |
+
| `male_aid_workers_killed_in_captivity` | int64 | 0.0% | 0.0 – 1.0 (mean 0.012) |
|
| 135 |
+
| `international_aid_workers_injured` | int64 | 0.0% | 0.0 – 5.0 (mean 0.0569) |
|
| 136 |
+
| `national_aid_workers_injured` | int64 | 0.0% | 0.0 – 7.0 (mean 0.3503) |
|
| 137 |
+
| `female_aid_workers_injured` | int64 | 0.0% | 0.0 – 2.0 (mean 0.0269) |
|
| 138 |
+
| `male_aid_workers_injured` | int64 | 0.0% | 0.0 – 5.0 (mean 0.2216) |
|
| 139 |
+
| `international_aid_workers_kidnapped` | int64 | 0.0% | 0.0 – 12.0 (mean 0.2545) |
|
| 140 |
+
| `national_aid_workers_kidnapped` | int64 | 0.0% | 0.0 – 10.0 (mean 0.4641) |
|
| 141 |
+
| `female_aid_workers_kidnapped` | int64 | 0.0% | 0.0 – 3.0 (mean 0.0629) |
|
| 142 |
+
| `male_aid_workers_kidnapped` | int64 | 0.0% | |
|
| 143 |
+
| `international_aid_workers_arrested` | int64 | 0.0% | |
|
| 144 |
+
| `national_aid_workers_arrested` | int64 | 0.0% | |
|
| 145 |
+
| `female_aid_workers_arrested` | int64 | 0.0% | |
|
| 146 |
+
| `male_aid_workers_arrested` | int64 | 0.0% | |
|
| 147 |
+
| `sind_event_id` | int64 | 0.0% | |
|
| 148 |
+
| `date_event_entered` | datetime64[ns] | 0.0% | |
|
| 149 |
+
| `date_event_modified` | datetime64[ns] | 0.0% | |
|
| 150 |
+
| `esa_source` | object | 0.0% | |
|
| 151 |
+
| `esa_processed` | object | 0.0% | |
|
| 152 |
+
|
| 153 |
+
---
|
| 154 |
+
|
| 155 |
+
## Numeric Summary
|
| 156 |
+
|
| 157 |
+
| Column | Min | Max | Mean | Median |
|
| 158 |
+
|---|---|---|---|---|
|
| 159 |
+
| `aid_workers_killed` | 0.0 | 14.0 | 0.6198 | 0.0 |
|
| 160 |
+
| `aid_workers_injured` | 0.0 | 7.0 | 0.5329 | 0.0 |
|
| 161 |
+
| `aid_workers_kidnapped` | 0.0 | 12.0 | 0.8174 | 0.0 |
|
| 162 |
+
| `aid_workers_arrested` | 0.0 | 6.0 | 0.1347 | 0.0 |
|
| 163 |
+
| `aid_workers_killed_in_captivity` | 0.0 | 7.0 | 0.0329 | 0.0 |
|
| 164 |
+
| `international_aid_workers_killed` | 0.0 | 4.0 | 0.0749 | 0.0 |
|
| 165 |
+
| `international_aid_workers_killed_in_captivity` | 0.0 | 0.0 | 0.0 | 0.0 |
|
| 166 |
+
| `national_aid_workers_killed` | 0.0 | 14.0 | 0.485 | 0.0 |
|
| 167 |
+
| `national_aid_workers_killed_in_captivity` | 0.0 | 7.0 | 0.0329 | 0.0 |
|
| 168 |
+
| `female_aid_workers_killed` | 0.0 | 2.0 | 0.0479 | 0.0 |
|
| 169 |
+
| `female_aid_workers_killed_in_captivity` | 0.0 | 0.0 | 0.0 | 0.0 |
|
| 170 |
+
| `male_aid_workers_killed` | 0.0 | 3.0 | 0.3772 | 0.0 |
|
| 171 |
+
| `male_aid_workers_killed_in_captivity` | 0.0 | 1.0 | 0.012 | 0.0 |
|
| 172 |
+
| `international_aid_workers_injured` | 0.0 | 5.0 | 0.0569 | 0.0 |
|
| 173 |
+
| `national_aid_workers_injured` | 0.0 | 7.0 | 0.3503 | 0.0 |
|
| 174 |
+
|
| 175 |
+
---
|
| 176 |
+
|
| 177 |
+
## Curation
|
| 178 |
+
|
| 179 |
+
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`. 3 column(s) with >80% missing values were removed: `event_description`, `latitude`, `longitude`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
|
| 180 |
+
|
| 181 |
+
---
|
| 182 |
+
|
| 183 |
+
## Limitations
|
| 184 |
+
|
| 185 |
+
- Data originates from Insecurity Insight and has not been independently validated by ESA.
|
| 186 |
+
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
|
| 187 |
+
- The following columns have >20% missing values and should be treated with caution in modelling: `known_kidnapping_or_arrest_outcome`.
|
| 188 |
+
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/somalia-violence-agaiinst-civilian-and-vital-civilian-facilitites) for the publisher's own methodology notes and caveats.
|
| 189 |
+
|
| 190 |
+
---
|
| 191 |
+
|
| 192 |
+
## Citation
|
| 193 |
+
|
| 194 |
+
```bibtex
|
| 195 |
+
@dataset{hdx_africa_somalia_violence_agaiinst_civilian_and_vital_civilian_facilitites,
|
| 196 |
+
title = {Somalia (SOM): Attacks on Aid Operations, Education, Food and Water Systems and Health Care},
|
| 197 |
+
author = {Insecurity Insight},
|
| 198 |
+
year = {2026},
|
| 199 |
+
url = {https://data.humdata.org/dataset/somalia-violence-agaiinst-civilian-and-vital-civilian-facilitites},
|
| 200 |
+
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
|
| 201 |
+
}
|
| 202 |
+
```
|
| 203 |
+
|
| 204 |
+
---
|
| 205 |
+
|
| 206 |
+
*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
|