Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy.
Queries the relevant table based on the selected dataset type, applies
filters, enforces k-anonymity by suppressing groups with fewer than 5
observations, and returns structured data.
WHEN TO USE:
- Exporting audience data for external analysis
- Building datasets for machine learning or reporting
- Getting structured vehicle or commerce data for a specific time/place
- Creating cross-signal datasets for correlation analysis
RETURNS:
- data: Array of dataset rows (schema varies by dataset type)
- metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range }
- suggested_next_queries: Related exports or analyses
Dataset types:
- observations: Raw observation stream data (all families)
- audience: Audience-specific data (face_count, demographics, attention, emotion)
- vehicle: Vehicle counting and classification data
- cross_signal: Pre-computed cross-signal correlation insights
EXAMPLE:
User: "Export audience data from retail venues last week"
export_dataset({
dataset: "audience",
filters: {
time_range: { start: "2026-03-09", end: "2026-03-16" },
venue_type: ["retail"]
},
format: "json"
})
User: "Get vehicle data near geohash 9q8yy"
export_dataset({
dataset: "vehicle",
filters: {
time_range: { start: "2026-03-15", end: "2026-03-16" },
geo: "9q8yy"
}
})
