electricsheepafrica/africa-education-senegal

Syria - 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: SEN.

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)	49
Columns	13 (5 numeric, 8 categorical, 0 datetime)
Train split	39 rows
Test split	9 rows
Geographic scope	SEN
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, #meta+category), indicator_friendly (Urban population – Cities, Share in national urban population, Average annual rate of change of population – Urban), type_data (p, 1000, #indicator+type), latitude (range 33.5–36.23), longitude (range 36.3–38.0) and 3 others.

Outcome / Measurement — value (range 0.1–4065.0).

Identifier / Metadata — name (Damascus, Syrian arab republic, Hamah), esa_source (HDX), esa_processed (2026-04-27).

Other — indicator (urban_population_cities, urban_population_share_national, avg_annual_rate_change_percentage_urban).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-education-senegal")
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, #meta+category
indicator	object	0.0%	urban_population_cities, urban_population_share_national, avg_annual_rate_change_percentage_urban
indicator_friendly	object	0.0%	Urban population – Cities, Share in national urban population, Average annual rate of change of population – Urban
type_data	object	0.0%	p, 1000, #indicator+type
latitude	float64	2.0%	33.5 – 36.23 (mean 34.8101)
longitude	float64	2.0%	36.3 – 38.0 (mean 37.0117)
region_id	float64	2.0%	589.0 – 589.0 (mean 589.0)
country_id	object	0.0%	SY, #country+code+v_iso2
name	object	0.0%	Damascus, Syrian arab republic, Hamah
year	float64	2.0%	2000.0 – 2020.0 (mean 2007.6667)
value	float64	2.0%	0.1 – 4065.0 (mean 645.8596)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	33.5	36.23	34.8101	35.0
longitude	36.3	38.0	37.0117	36.73
region_id	589.0	589.0	589.0	589.0
year	2000.0	2020.0	2007.6667	2006.0
value	0.1	4065.0	645.8596	26.75

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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_education_senegal,
  title     = {Syria - 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-sy-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.