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name
stringclasses
798 values
iso
stringclasses
38 values
id
stringclasses
810 values
country
stringclasses
1 value
admin_level
stringclasses
3 values
category
stringclasses
1 value
range_type
stringclasses
1 value
range
int64
600
6k
population_type
stringclasses
6 values
population
int64
0
185M
population_share
float64
0
100
population_interval
int64
0
62.4M
population_interval_share
float64
0
100
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
600
adults
195,153
96.23
195,153
96.23
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
600
elderly
10,946
95.72
10,946
95.72
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
600
school_age
96,244
96.15
96,244
96.15
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
600
total
196,072
96.21
196,072
96.21
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
600
under_5
23,230
96.13
23,230
96.13
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
600
women_childbearing
54,830
96.27
54,830
96.27
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,200
adults
202,793
100
7,640
3.77
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,200
elderly
11,436
100
490
4.28
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,200
school_age
100,100
100
3,856
3.85
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,200
total
203,806
100
7,734
3.79
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,200
under_5
24,165
100
935
3.87
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,200
women_childbearing
56,956
100
2,126
3.73
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,800
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,800
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,800
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,800
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,800
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
1,800
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
2,400
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
2,400
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
2,400
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
2,400
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
2,400
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
2,400
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,000
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,000
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,000
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,000
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,000
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,000
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,600
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,600
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,600
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,600
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,600
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
3,600
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,200
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,200
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,200
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,200
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,200
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,200
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,800
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,800
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,800
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,800
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,800
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
4,800
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
5,400
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
5,400
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
5,400
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
5,400
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
5,400
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
5,400
women_childbearing
56,956
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
6,000
adults
202,793
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
6,000
elderly
11,436
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
6,000
school_age
100,100
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
6,000
total
203,806
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
6,000
under_5
24,165
100
0
0
Aba North
null
59680162B53329994117155
NGA
ADM2
hospitals
TIME
6,000
women_childbearing
56,956
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
600
adults
456,044
90.38
456,044
90.38
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
600
elderly
22,565
90.33
22,565
90.33
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
600
school_age
222,850
90.36
222,850
90.36
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
600
total
455,322
90.38
455,322
90.38
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
600
under_5
49,732
90.39
49,732
90.39
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
600
women_childbearing
126,604
90.4
126,604
90.4
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,200
adults
504,595
100
48,551
9.62
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,200
elderly
24,981
100
2,416
9.67
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,200
school_age
246,632
100
23,782
9.64
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,200
total
503,809
100
48,487
9.62
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,200
under_5
55,019
100
5,287
9.61
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,200
women_childbearing
140,052
100
13,448
9.6
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,800
adults
504,595
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,800
elderly
24,981
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,800
school_age
246,632
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,800
total
503,809
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,800
under_5
55,019
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
1,800
women_childbearing
140,052
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
2,400
adults
504,595
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
2,400
elderly
24,981
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
2,400
school_age
246,632
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
2,400
total
503,809
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
2,400
under_5
55,019
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
2,400
women_childbearing
140,052
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,000
adults
504,595
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,000
elderly
24,981
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,000
school_age
246,632
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,000
total
503,809
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,000
under_5
55,019
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,000
women_childbearing
140,052
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,600
adults
504,595
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,600
elderly
24,981
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,600
school_age
246,632
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,600
total
503,809
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,600
under_5
55,019
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
3,600
women_childbearing
140,052
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
4,200
adults
504,595
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
4,200
elderly
24,981
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
4,200
school_age
246,632
100
0
0
Aba South
null
59680162B9128777704300
NGA
ADM2
hospitals
TIME
4,200
total
503,809
100
0
0
End of preview. Expand in Data Studio

Nigeria Hospitals Accessibility Indicators | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: parquet - Sector: health - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.

Dataset context from the existing Hugging Face card: Nigeria Hospitals Accessibility Indicators This dataset provides insights into spatial accessibility to hospital services across Nigeria. It uses travel-time isochrones to calculate the population within time intervals from 10 to 120 minutes away. Data Structure name: Region or country name. iso: ISO3 country code. id: Unique identifier for the administrative unit. country: ISO3 country code. admin_level: Administrative level of the unit (ADM1-ADM4). category: hospitals.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigeria-hospitals-accessibility.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/nigeria-hospitals-accessibility
Sector health
Topic tags geospatial, nigeria, hospitals, infrastructure
Modalities geospatial, tabular, text
Formats parquet
Size category 10K<n<100K
Countries Nigeria
ISO3 coverage NGA
Last modified on HF 2025-12-30 06:21:26+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/nigeria-hospitals-accessibility")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_nigeria_hospitals_accessibility_2026,
  title        = {Nigeria Hospitals Accessibility Indicators | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/nigeria-hospitals-accessibility},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/nigeria-hospitals-accessibility}}
}

License

Released under Open Data Commons Open Database License.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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