Datasets:

electricsheepafrica
/

nigerian_electricity_state_electricity_access

state_name stringclasses 37 values	year int64 2.02k 2.02k	total_access_pct float64 37.5 98	rural_access_pct float64 14.1 45.4	urban_access_pct float64 58 99	urban_population_pct int64 20 95	adjustment_factor float64 0.68 1.62
Lagos	2,018	91.8	42.8	99	95	1.624
Lagos	2,018	91.3	42.6	99	95	1.616
FCT	2,018	89.2	41.6	99	90	1.579
Rivers	2,018	85.1	39.7	99	70	1.506
Anambra	2,018	79.9	37.2	99	65	1.413
Kano	2,018	78.4	36.6	99	60	1.388
Oyo	2,018	80.1	37.4	99	60	1.418
Abia	2,018	61	28.4	92.6	58	1.08
Delta	2,018	63	29.4	95.6	55	1.115
Imo	2,018	55.1	25.7	83.7	55	0.975
Edo	2,018	64.6	30.1	98.1	55	1.144
Kaduna	2,018	70.7	33	99	52	1.252
Ogun	2,018	63.1	29.4	95.8	50	1.117
Akwa Ibom	2,018	58.5	27.3	88.8	48	1.035
Enugu	2,018	51.3	23.9	77.9	48	0.908
Ondo	2,018	48.3	22.5	73.3	45	0.855
Osun	2,018	50.5	23.6	76.7	45	0.894
Kwara	2,018	50.9	23.7	77.2	42	0.9
Cross River	2,018	52.3	24.4	79.5	40	0.926
Plateau	2,018	48.9	22.8	74.3	40	0.866
Katsina	2,018	43.1	20.1	65.4	38	0.762
Benue	2,018	48.2	22.5	73.2	35	0.853
Bauchi	2,018	46.5	21.7	70.6	35	0.823
Niger	2,018	50.9	23.7	77.3	35	0.901
Kogi	2,018	45.3	21.1	68.8	35	0.803
Sokoto	2,018	43.8	20.4	66.5	32	0.775
Borno	2,018	40.6	18.9	61.6	30	0.719
Adamawa	2,018	38.2	17.8	58	30	0.676
Taraba	2,018	44	20.5	66.8	28	0.778
Jigawa	2,018	42.7	19.9	64.8	25	0.755
Yobe	2,018	39.6	18.5	60.1	25	0.7
Kebbi	2,018	41.3	19.2	62.6	22	0.73
Zamfara	2,018	43.3	20.2	65.8	20	0.767
Gombe	2,018	44.9	21	68.2	35	0.795
Bayelsa	2,018	51	23.8	77.4	45	0.902
Ekiti	2,018	54.3	25.3	82.4	40	0.96
Ebonyi	2,018	43.1	20.1	65.5	30	0.763
Nasarawa	2,018	52.3	24.4	79.5	35	0.926
Lagos	2,019	90	35.2	99	95	1.624
Lagos	2,019	89.5	35	99	95	1.616
FCT	2,019	87.5	34.2	99	90	1.579
Rivers	2,019	83.4	32.6	99	70	1.506
Anambra	2,019	78.3	30.6	99	65	1.413
Kano	2,019	76.9	30.1	99	60	1.388
Oyo	2,019	78.5	30.7	99	60	1.418
Abia	2,019	59.8	23.4	95.1	58	1.08
Delta	2,019	61.7	24.2	98.2	55	1.115
Imo	2,019	54	21.1	85.9	55	0.975
Edo	2,019	63.4	24.8	99	55	1.144
Kaduna	2,019	69.3	27.1	99	52	1.252
Ogun	2,019	61.9	24.2	98.4	50	1.117
Akwa Ibom	2,019	57.3	22.4	91.2	48	1.035
Enugu	2,019	50.3	19.7	80	48	0.908
Ondo	2,019	47.4	18.5	75.3	45	0.855
Osun	2,019	49.5	19.4	78.8	45	0.894
Kwara	2,019	49.9	19.5	79.3	42	0.9
Cross River	2,019	51.3	20.1	81.6	40	0.926
Plateau	2,019	48	18.8	76.3	40	0.866
Katsina	2,019	42.2	16.5	67.1	38	0.762
Benue	2,019	47.3	18.5	75.2	35	0.853
Bauchi	2,019	45.6	17.8	72.5	35	0.823
Niger	2,019	49.9	19.5	79.4	35	0.901
Kogi	2,019	44.5	17.4	70.7	35	0.803
Sokoto	2,019	42.9	16.8	68.3	32	0.775
Borno	2,019	39.8	15.6	63.3	30	0.719
Adamawa	2,019	37.5	14.7	59.6	30	0.676
Taraba	2,019	43.1	16.9	68.6	28	0.778
Jigawa	2,019	41.8	16.4	66.5	25	0.755
Yobe	2,019	38.8	15.2	61.7	25	0.7
Kebbi	2,019	40.5	15.8	64.3	22	0.73
Zamfara	2,019	42.5	16.6	67.6	20	0.767
Gombe	2,019	44.1	17.2	70	35	0.795
Bayelsa	2,019	50	19.6	79.5	45	0.902
Ekiti	2,019	53.2	20.8	84.6	40	0.96
Ebonyi	2,019	42.3	16.5	67.3	30	0.763
Nasarawa	2,019	51.3	20.1	81.6	35	0.926
Lagos	2,020	90	34	99	95	1.624
Lagos	2,020	89.5	33.8	99	95	1.616
FCT	2,020	87.5	33	99	90	1.579
Rivers	2,020	83.4	31.5	99	70	1.506
Anambra	2,020	78.3	29.6	99	65	1.413
Kano	2,020	76.9	29	99	60	1.388
Oyo	2,020	78.5	29.6	99	60	1.418
Abia	2,020	59.8	22.6	95.1	58	1.08
Delta	2,020	61.7	23.3	98.2	55	1.115
Imo	2,020	54	20.4	85.9	55	0.975
Edo	2,020	63.4	23.9	99	55	1.144
Kaduna	2,020	69.3	26.2	99	52	1.252
Ogun	2,020	61.9	23.3	98.4	50	1.117
Akwa Ibom	2,020	57.3	21.6	91.2	48	1.035
Enugu	2,020	50.3	19	80	48	0.908
Ondo	2,020	47.4	17.9	75.3	45	0.855
Osun	2,020	49.5	18.7	78.8	45	0.894
Kwara	2,020	49.9	18.8	79.3	42	0.9
Cross River	2,020	51.3	19.4	81.6	40	0.926
Plateau	2,020	48	18.1	76.3	40	0.866
Katsina	2,020	42.2	15.9	67.1	38	0.762
Benue	2,020	47.3	17.8	75.2	35	0.853
Bauchi	2,020	45.6	17.2	72.5	35	0.823
Niger	2,020	49.9	18.8	79.4	35	0.901

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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

State-Level Electricity Access

⚠️ SYNTHETIC DATA DISCLAIMER

This dataset contains synthetic/modeled data, not direct measurements.

Purpose: Research, education, and methodology demonstration
Generation: Geospatial disaggregation model using proxy indicators
Validation: Grounded in official World Bank national data
Limitations: State/LGA estimates are modeled, not measured
Use with caution: Not suitable for operational decisions without validation

For official data, consult: World Bank, NERC, REA, DISCOs directly.

Dataset Description

State-by-state electricity access rates disaggregated from national data using geospatial proxy indicators (night-time lights, grid proximity, urbanization, DISCO performance).

Rows: 228
Columns: 7
Period: 2018-2023 (where applicable)
License: MIT

Data Quality

⭐⭐⭐⭐ Modeled estimates using validated methodology

Methodology

Data Generation Process

This dataset is part of a geospatial electrification analysis project that addresses the lack of state-level electricity access data in Nigeria.

Challenge: World Bank provides only national-level access rates. No state-by-state breakdown exists.

Solution: Geospatial disaggregation model using weighted proxy indicators:

State_Access = National_Rate × Adjustment_Factor

Adjustment_Factor = (
    35% × Night-time Lights Index +
    25% × Grid Proximity Index +
    20% × Urban Population Share +
    15% × DISCO Performance Index +
    5% × Historical Baseline
)

Validation:

State averages match national figures (< 0.1% difference)
Adjustment factors normalized (mean = 1.0)
Realistic bounds applied (10-98% access range)
Urban > Rural access (consistent with known patterns)

Data Sources

World Bank API: National electricity access rates (2018-2023)
GADM: Administrative boundaries (37 states, 775 LGAs)
Proxy indicators: Urbanization rates, DISCO coverage, infrastructure patterns
Public reports: NERC quarterly reports, REA project data

Data Dictionary

Column	Type	Description	Example
`state_name`	object	State Name	Lagos
`year`	int64	Year	2018
`total_access_pct`	float64	Total Access Pct	91.8
`rural_access_pct`	float64	Rural Access Pct	42.8
`urban_access_pct`	float64	Urban Access Pct	99.0
`urban_population_pct`	int64	Urban Population Pct	95
`adjustment_factor`	float64	Adjustment Factor	1.624

Usage

Load with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/nigerian_electricity_state_electricity_access")
df = dataset['train'].to_pandas()

Load with Pandas

import pandas as pd

# From Parquet (recommended)
df = pd.read_parquet("hf://datasets/electricsheepafrica/nigerian_electricity_state_electricity_access/nigerian_electricity_state_electricity_access.parquet")

# From CSV
df = pd.read_csv("hf://datasets/electricsheepafrica/nigerian_electricity_state_electricity_access/nigerian_electricity_state_electricity_access.csv")

Sample Data

state_name  year  total_access_pct  rural_access_pct  urban_access_pct  urban_population_pct  adjustment_factor
     Lagos  2018              91.8              42.8              99.0                    95              1.624
     Lagos  2018              91.3              42.6              99.0                    95              1.616
       FCT  2018              89.2              41.6              99.0                    90              1.579

Use Cases

Policy research: Identify underserved areas for electrification programs
Investment analysis: Assess market opportunities for off-grid solutions
Academic research: Study determinants of electricity access
Methodology validation: Compare geospatial disaggregation approaches
SDG 7 tracking: Monitor progress toward universal energy access

Limitations

Synthetic data: State and LGA estimates are modeled, not measured
Time period: Limited to 2018-2023
Granularity: No settlement-level data (requires GRID3 integration)
Validation: Limited by availability of ground-truth data
Simplifications: Actual electrification patterns are more complex

Citation

@dataset{nigerian_electricity_access_2025,
  title = {Nigerian Electricity Access: State-Level Electricity Access},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  note = {Geospatial disaggregation using proxy indicators},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_electricity_state_electricity_access}
}