electricsheepafrica/africa-mozambique-cyclone-idai-4w

Mozambique Cyclone Idai 3W/4W

Publisher: OCHA Mozambique · Source: HDX · License: cc-by-igo · Updated: 2025-04-08

---

Abstract

Mozambique 3W/4W (Response Tracking) for the the Cyclone Idai response.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the date_report column(s). Geographic scope: MOZ.

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

---

Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Time-series observations
Rows (total)	17,844
Columns	25 (3 numeric, 21 categorical, 1 datetime)
Train split	14,275 rows
Test split	3,568 rows
Geographic scope	MOZ
Publisher	OCHA Mozambique
HDX last updated	2025-04-08

---

Variables

Geographic — activity_activity_code (HEA 0179, HEA 0047, HEA 0083), org_type_name_short (INGO, Gov., UN), org_type_name_role (Lead Organisation, Implementing partner, ONGs internacionais), activity_activity_type_name (Food distribution In Kind, Distribuição de alimentos em espécie, Water), activity_activity_name (2.4 - Rehabilitation and disinfection of existing water points, Mother and Child Health (Nutrition and EPI), 4.2 - Household level hygiene promotion activities) and 2 others.

Temporal — date_report, date_start (range 49.0–412453.0), date_end (range -509603.0–44499.0).

Identifier / Metadata — org_name (WFP, COSACA, UNICEF), org_name_short (WFP, MISAU, COSACA), sector_cluster_name (Saúde, Segurança Alimentar, Água, Saneamento e Higiene), output_name (food aid, Mixed food, Learning Materials), adm1_name and 9 others.

Other — reached_ind (range 0.0–1500000.0).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mozambique-cyclone-idai-4w")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
date_report	datetime64[ns]	2.2%
activity_activity_code	object	0.0%	HEA 0179, HEA 0047, HEA 0083
org_name	object	0.5%	WFP, COSACA, UNICEF
org_name_short	object	3.9%	WFP, MISAU, COSACA
org_type_name_short	object	6.1%	INGO, Gov., UN
org_type_name_role	object	2.4%	Lead Organisation, Implementing partner, ONGs internacionais
sector_cluster_name	object	0.0%	Saúde, Segurança Alimentar, Água, Saneamento e Higiene
activity_activity_type_name	object	34.2%	Food distribution In Kind, Distribuição de alimentos em espécie, Water
activity_activity_name	object	53.4%	2.4 - Rehabilitation and disinfection of existing water points, Mother and Child Health (Nutrition and EPI), 4.2 - Household level hygiene promotion activities
output_name	object	53.5%	food aid, Mixed food, Learning Materials
modality_name	object	64.7%	em espécie (in-kind), dinheiro em espécie, cupom
adm1_name	object	1.9%
adm1_code	object	4.5%
adm2_name	object	11.9%
adm2_code	object	14.5%
adm3_name	object	58.2%
adm3_code	object	62.0%
loc_name	object	63.5%
reached_ind	float64	50.8%	0.0 – 1500000.0 (mean 5572.9276)
beneficiary_type_name	object	78.4%
date_start	float64	53.0%	49.0 – 412453.0 (mean 43168.0132)
date_end	float64	64.8%	-509603.0 – 44499.0 (mean 43449.7293)
status_name	object	8.1%
esa_source	object	0.0%
esa_processed	object	0.0%

---

Numeric Summary

Column	Min	Max	Mean	Median
reached_ind	0.0	1500000.0	5572.9276	500.0
date_start	49.0	412453.0	43168.0132	43556.0
date_end	-509603.0	44499.0	43449.7293	43564.0

---

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. 4,993 exact duplicate rows were removed. 3 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.

---

Limitations

• Data originates from OCHA Mozambique 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: activity_activity_type_name, activity_activity_name, output_name, modality_name, adm3_name, adm3_code, loc_name, reached_ind....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_mozambique_cyclone_idai_4w,
  title     = {Mozambique Cyclone Idai 3W/4W},
  author    = {OCHA Mozambique},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mozambique-cyclone-idai-4w},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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