Best use for travel intelligence
For travel decisions, cybercrime data mostly helps set expectations around scams, online-crime patterns, and reporting channels rather than neighborhood-level street safety.
Data cluster
Thailand Cybercrime Statistics.
Police cybercrime and online-crime public statistics, victim counts, damage value, and case-group context for Thailand safety pages.
What this changes for travelers
- Scam and online-risk context: Helps explain which public crime layers are actually strong in Thailand right now.
- Reporting expectations: Shows where cybercrime reporting and public police data are more mature than many other crime categories.
- Trend over panic: Useful for trends and categories, not for neighborhood-level danger scoring.
Best sources to start with
- Best public cybercrime source: Police Online Open Data for online-crime case counts, victim dimensions, age, sex, occupation, unit, and damage value.
- Best official context source: Royal Thai Police open data for public-service, complaint, and broader police context outside the catalog itself.
- Best developer starting point: Keep period, case group, geography, damage value, and victim dimensions separate, and do not imply any dataset exposes individual reports.
0 of 1 sources look official or agency-backed.
1 sources expose API, feed, CKAN/DataStore, JSON, XML, or similar machine-readable access.
0 sources have live, hourly, event-driven, warning, or frequent update language.
Treat cybercrime statistics as aggregated public reporting data with strong trend value but no individual-case visibility.
Last checked and source confidence
Last checked: 2026-05-11.
Source confidence: This cluster is strongest when several sources describe the same traveler problem from different angles: official context, machine-readable feeds, and slower fallback documentation.