electricsheepafrica/africa-mauritius-accident-prone-areas-9a43716c

Accident Prone Areas | Africa (MDPA)

48 rows - 1 Africa country/area - detected - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 48 rows from MDPA, covering Accident Prone Areas. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

Source-provided context: list of accident prone areas around the island

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: source_period_start_year.
• Time coverage basis: source_period_start_year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	48
Countries/areas	1
First period	detected
Last period	detected
Indicators	0
Columns	16
Source format	CSV

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
MU	48	detected	detected	Mauritius

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	3e12e27b-71c5-47b6-8585-f498e82d32cc:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	MU
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Mauritius
the_geom	string	Source column from the original resource.	MULTILINESTRING ((57.4820959 -20.1747444, 57.4830401 -20.1724483, 57....
name	string	Source column from the original resource.	M1: Belle Village up to Caudan
source_period_start_year	int64	Start year inferred from source metadata.	``
source_period_end_year	int64	End year inferred from source metadata.	``
source_period_label	string	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	MDPA
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Accident Prone Areas
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	accident_prone_areas_4.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	a2481450-88ad-40bc-a2b7-b279206a5dbe
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	3e12e27b-71c5-47b6-8585-f498e82d32cc
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/a2481450-88ad-40bc-a2b7-b279206a5dbe/r...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	CC-BY-SA-4.0
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-08T16:26:20Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-accident-prone-areas-9a43716c")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MU"]

Time-Series Pattern

if "value" in df.columns and "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: source_period_start_year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: MDPA
• Publisher: MDPA
• Portal: https://data.govmu.org
• Resource: accident_prone_areas_4.csv
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T16:41:35Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-accident-prone-areas-9a43716c

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Track mobility over time
• Compare routes or geographies
• Join with economic and population data
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_accident_prone_areas_9a43716c_2026,
  title        = {Accident Prone Areas | Africa (MDPA)},
  author       = {MDPA},
  year         = {2026},
  url          = {https://data.govmu.org/dataset/accident-prone-areas},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-accident-prone-areas-9a43716c}}
}

License

Released under CC BY-SA 4.0.

Original data is published by MDPA. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

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

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/accident-prone-areas