electricsheepafrica/africa-mauritius-number-of-casualty-accidents-by-type-of-road-severity-of-a-4cb9074c

Number of Casualty Accidents by Type of Road Severity of a | Africa (MDPA)

352 rows - 1 Africa country/area - 2015-2018 - 1 indicator - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 352 rows from MDPA, covering Number of Casualty Accidents by Type of Road Severity of a. 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: The data shows Number of casualty accidents by type of road, severity of accident and collision type and by year

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	352
Countries/areas	1
First period	2015
Last period	2018
Indicators	1
Columns	21
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	352	2015	2018	Mauritius

Indicators, Variables, Or Resource Contents

• number-of-casualty-accidents-by-type-of-road-severity-of-accident-and-co-596f95f7 - Number of casualty accidents by type of road, severity of accident and collision type by year(source_units_unspecified)

Schema

Column	Type	Description	Example
indicator_id	string	Stable source or Electric Sheep Africa indicator identifier.	number-of-casualty-accidents-by-type-of-road-severity-of-accident-and...
indicator_name	string	Human-readable indicator name.	Number of casualty accidents by type of road, severity of accident an...
country_iso3	string	ISO3 country or area code.	MU
country_name	string	Country or area name.	Mauritius
year	int64	Observation year.	2015
value	double	Numeric observation value.	45.0
unit	string	Measurement unit, when supplied by the source.	source_units_unspecified
dimension_collisiontype	string	Source dimension retained during long-form normalization.	Head On
dimension_severityofaccident	string	Source dimension retained during long-form normalization.	Fatal
dimension_roadtype	string	Source dimension retained during long-form normalization.	Two Way Street
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.	Number of casualty accidents by type of road, severity of accident an...
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	casualty-by-type-of-road-severity-of-accident-and-collision-type.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	03d1ccfd-2f64-4d5a-9776-83ee6e6d48bd
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	18126aef-fc42-45cc-ae39-e97f12261c38
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/03d1ccfd-2f64-4d5a-9776-83ee6e6d48bd/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-type-of-road-severity-of-a-4cb9074c")
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: casualty-by-type-of-road-severity-of-accident-and-collision-type.csv
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T16:34:24Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-number-of-casualty-accidents-by-type-of-road-severity-of-a-4cb9074c

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_type_of_road_severity_of_a_4cb9_2018,
  title        = {Number of Casualty Accidents by Type of Road Severity of a | Africa (MDPA)},
  author       = {MDPA},
  year         = {2018},
  url          = {https://data.govmu.org/dataset/number-casualty-accidents-type-road-severity-accident-and-collision-type-year},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-number-of-casualty-accidents-by-type-of-road-severity-of-a-4cb9074c}}
}

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-type-road-severity-accident-and-collision-type-year