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who-is-tyler-durden
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thailand-road-traffic-injury-aggregates-2018

Languages:
English
Thai
Tags:
thailand
bangkok
road-safety
traffic-injuries
injury-surveillance
public-health
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metadata
language:
  - en
  - th
license: cc-by-4.0
tags:
  - thailand
  - bangkok
  - road-safety
  - traffic-injuries
  - injury-surveillance
  - public-health
  - tabular-data
  - csv
dataset_type: tabular
pretty_name: Thailand Road-Traffic Injury Aggregates (2018)

Thailand Road-Traffic Injury Aggregates (2018)

Canonical dataset page (EN/TH): https://bangkokfamilylawyer.com/datasets-injury-th/
DOI (this version): https://doi.org/10.5281/zenodo.17538573
Concept DOI (latest): https://doi.org/10.5281/zenodo.17538574

Author: Jean Maurice Cecilia Menzel
Publisher: AppDevBangkok / UdonLaw
License: CC BY 4.0

This repository provides aggregated indicators derived from Thailand’s official Injury Surveillance data (Department of Disease Control, Ministry of Public Health) for calendar year 2018.

All outputs are privacy-preserving aggregates; individual-level records are not included.

Contents

Key CSV files (2018 slice):

  • province_2018.csv — Cases by province.
  • bkk_quarter_2018.csv — Bangkok cases by quarter.
  • age_bins_2018.csv — Cases by age group.
  • sex_2018.csv — Cases by sex.
  • mode_mix_bkk_2018.csv — Mode / road-user mix for Bangkok.
  • top10_provinces_2018.csv — Top 10 provinces by case count.
  • bkk_top_amphoe_2018.csv — Leading Bangkok districts.
  • qa_year_counts_2018.csv — Year-level row counts (QA).
  • qa_coverage_province_2018.csv — Coverage / completeness indicators.
  • qa_summary.json — Human-readable QA summary.

File names may be extended as new QA tables or visualizations are added.

Source Data

Source dataset (not redistributed here):

The original dataset remains under its own terms and conditions. This repository only provides derived aggregates.

Methodology (summary)

  • Filtered to events in 2018.
  • Robust date parsing with support for Thai formats and Buddhist Era → Gregorian conversion.
  • Records with invalid or missing core fields are excluded from aggregates.
  • Aggregations computed by:
    • Province and (for Bangkok) district
    • Quarter (Q1–Q4)
    • Age groups
    • Sex
    • Selected mode / road-user categories
  • Small cells may be combined or omitted to reduce re-identification risk.

For full details, see the canonical documentation at:

https://bangkokfamilylawyer.com/datasets-injury-th/

How to use

Example (Python):

import pandas as pd
from datasets import load_dataset

ds = load_dataset(
    "appdevbangkok/thailand-road-traffic-injury-aggregates-2018",
    split="train"
)
# or load specific CSVs directly via raw URLs if preferred