electricsheepafrica/africa-mauritius-number-of-casualty-accidents-by-severity-of-accident-and-t-53597b91

Number of Casualty Accidents by Severity of Accident and T | Africa (MDPA)

45 rows - 1 Africa country/area - 2017-2021 - 1 indicator - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 45 rows from MDPA, covering Number of Casualty Accidents by Severity of Accident and T. 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: Dataset shows Number of Casualty Accidents by Severity of Accident and Type of Road for the year 2017 and 2021

How To Read This Dataset

• One row means: one indicator observation for one geography, time period, and optional source dimensions.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3, year, indicator_id.

Coverage

Dimension	Value
Rows	45
Countries/areas	1
First period	2017
Last period	2021
Indicators	1
Columns	20
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	45	2017	2021	Mauritius

Indicators, Variables, Or Resource Contents

• number-of-casualty-accidents-by-severity-of-accident-and-type-of-road-dfd26d5b - Number of Casualty Accidents by Severity of Accident and Type of Road(source_units_unspecified)

Schema

Column	Type	Description	Example
indicator_id	string	Stable source or Electric Sheep Africa indicator identifier.	number-of-casualty-accidents-by-severity-of-accident-and-type-of-road...
indicator_name	string	Human-readable indicator name.	Number of Casualty Accidents by Severity of Accident and Type of Road
country_iso3	string	ISO3 country or area code.	MU
country_name	string	Country or area name.	Mauritius
year	int64	Observation year.	2017
value	double	Numeric observation value.	5.0
unit	string	Measurement unit, when supplied by the source.	source_units_unspecified
dimension_type_of_road	string	Source dimension retained during long-form normalization.	One Way Street
dimension_severity_of_accident	string	Source dimension retained during long-form normalization.	Fatal
source_period_start_year	int64	Start year inferred from source metadata.	2017
source_period_end_year	int64	End year inferred from source metadata.	2021
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2017-2021
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.	Number of Casualty Accidents by Severity of Accident and Type of Road
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Number-of-Casualty-Accidents-by-Severity-of-Accident-and-Type-of-Road...
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	9a5c4b4c-3fa0-44a6-9acc-ced5e102866b
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	87f3efd4-57b4-4968-9ef3-45f87a8b3ce2
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/9a5c4b4c-3fa0-44a6-9acc-ced5e102866b/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-number-of-casualty-accidents-by-severity-of-accident-and-t-53597b91")
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 "year" in df.columns:
    trend = df.sort_values("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: 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: Number-of-Casualty-Accidents-by-Severity-of-Accident-and-Type-of-Road.csv
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T16:34:34Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-number-of-casualty-accidents-by-severity-of-accident-and-t-53597b91

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
• Pivot to geography x period or indicator x period matrices
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_number_of_casualty_accidents_by_severity_of_accident_and_t_5359_2021,
  title        = {Number of Casualty Accidents by Severity of Accident and T | Africa (MDPA)},
  author       = {MDPA},
  year         = {2021},
  url          = {https://data.govmu.org/dataset/number-casualty-accidents-severity-accident-and-type-road},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-number-of-casualty-accidents-by-severity-of-accident-and-t-53597b91}}
}

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/number-casualty-accidents-severity-accident-and-type-road