electricsheepafrica/africa-wfp-food-security-indicators-for-zimbabwe

Zimbabwe - Food Security Indicators

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by-igo · Updated: 2023-05-16

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Abstract

The World Food Programme (WFP) launched the mobile Vulnerability Analysis and Mapping (mVAM) project in 2013, beginning in DRC and Somalia. mVAM uses mobile technology to track food security trends in real-time, providing high-frequency data that supports humanitarian decision-making. Data collection methods are tailored to the needs of each country that mVAM operates in. This dataset contains data from the mVAM databank covering various indicators (one per resource).

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

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Time-series observations
Rows (total)	2,697
Columns	9 (1 numeric, 7 categorical, 1 datetime)
Train split	2,157 rows
Test split	539 rows
Geographic scope	ZWE
Publisher	WFP - World Food Programme
HDX last updated	2023-05-16

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Variables

Geographic — svydate, adminstrata (Mashonaland Central, Mashonaland East, Masvingo).

Identifier / Metadata — adm0_name (Zimbabwe, #country+name), esa_source (HDX), esa_processed (2026-04-06).

Other — variable (rCSI, rCSI>=1, ReduceNumMeals>=1), variabledescription (prevalence-->equals to 1 if household uses this strategy 1 or more times per week, reduced coping strategy, prevalence-->equals to 1 if household using coping strategies), demographic (M, F, BlairPitLatrine), mean (range 0.063–30.3653).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-security-indicators-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
svydate	datetime64[ns]	0.0%
adm0_name	object	0.0%	Zimbabwe, #country+name
adminstrata	object	24.2%	Mashonaland Central, Mashonaland East, Masvingo
variable	object	0.0%	rCSI, rCSI>=1, ReduceNumMeals>=1
variabledescription	object	9.2%	prevalence-->equals to 1 if household uses this strategy 1 or more times per week, reduced coping strategy, prevalence-->equals to 1 if household using coping strategies
demographic	object	50.6%	M, F, BlairPitLatrine
mean	float64	0.5%	0.063 – 30.3653 (mean 4.8989)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
mean	0.063	30.3653	4.8989	0.9194

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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. 2 column(s) with >80% missing values were removed: adm1_name, adm2_name. 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 WFP - World Food Programme 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: adminstrata, demographic.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_wfp_food_security_indicators_for_zimbabwe,
  title     = {Zimbabwe - Food Security Indicators},
  author    = {WFP - World Food Programme},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/wfp-food-security-indicators-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.