electricsheepafrica/africa-unhabitat-ci-indicators

Ivory coast - Demographic, Health, Education and Transport indicators

Publisher: United Nations Human Settlements Programmes, Data and Analytics Section · Source: HDX · License: cc-by-igo · Updated: 2024-03-28

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Abstract

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2024-03-28. Geographic scope: CIV.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	First-level administrative unit observations
Rows (total)	84
Columns	13 (5 numeric, 8 categorical, 0 datetime)
Train split	67 rows
Test split	16 rows
Geographic scope	CIV
Publisher	United Nations Human Settlements Programmes, Data and Analytics Section
HDX last updated	2024-03-28

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Variables

Geographic — category (Slum dwellers, Population, City prosperity), indicator_friendly (Total population, Proportion of urban population with durable housing – Cities, Proportion of population with access to improved sanitation – Cities), type_data (p, 1000, #indicator+type), latitude (range 5.32–8.0), longitude (range -5.2755–-4.04) and 3 others.

Outcome / Measurement — value (range 0.1–34704.0).

Identifier / Metadata — name (CÔte d'ivoire, Abidjan, #country+name), esa_source (HDX), esa_processed (2026-04-15).

Other — indicator (population, durable_housing_urban_population_cities, improved_sanitation_cities).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unhabitat-ci-indicators")
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
category	object	0.0%	Slum dwellers, Population, City prosperity
indicator	object	0.0%	population, durable_housing_urban_population_cities, improved_sanitation_cities
indicator_friendly	object	0.0%	Total population, Proportion of urban population with durable housing – Cities, Proportion of population with access to improved sanitation – Cities
type_data	object	0.0%	p, 1000, #indicator+type
latitude	float64	1.2%	5.32 – 8.0 (mean 6.9848)
longitude	float64	1.2%	-5.2755 – -4.04 (mean -4.6448)
region_id	float64	1.2%	289.0 – 289.0 (mean 289.0)
country_id	object	0.0%	CI, #country+code+v_iso2
name	object	0.0%	CÔte d'ivoire, Abidjan, #country+name
year	float64	1.2%	1950.0 – 2050.0 (mean 2000.7711)
value	float64	1.2%	0.1 – 34704.0 (mean 4435.0552)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-15

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	5.32	8.0	6.9848	8.0
longitude	-5.2755	-4.04	-4.6448	-5.0
region_id	289.0	289.0	289.0	289.0
year	1950.0	2050.0	2000.7711	2000.0
value	0.1	34704.0	4435.0552	86.4

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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. 5 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 United Nations Human Settlements Programmes, Data and Analytics Section 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_unhabitat_ci_indicators,
  title     = {Ivory coast - Demographic, Health, Education and Transport indicators},
  author    = {United Nations Human Settlements Programmes, Data and Analytics Section},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/unhabitat-ci-indicators},
  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.