Geo-ts.com 【2025-2027】
"sensor_id": "S-1023", "lon": -73.935, "lat": 40.730, "ts": "2026-04-16T08:00:00Z", "pm25": 12.4 “Find all sensors within 1 km of Central Park where pm25 > 35.0 in the last 2 hours, output 5-min rolling averages.”
| Component | Description | |-----------|-------------| | | REST or MQTT endpoints accepting GeoJSON, NetCDF, or CSV with geometry+time fields | | Storage engine | Distributed, sharded by space (e.g., S2 cells) and time (e.g., month partitions) | | Query API | OGC-compliant (WFS 3.0) with time filters; plus GraphQL for custom aggregations | | Visualization layer | Dynamic maps with time sliders (e.g., Deck.gl, OpenLayers, Kepler.gl) | | Export & webhooks | Download subsets as GeoParquet; trigger actions on spatio-temporal events | 4. Example Use Case: Urban Air Quality Monitoring Assume geo-ts.com serves a network of 10,000 IoT sensors sending PM2.5 readings every minute. geo-ts.com
Author: AI Research Unit Date: April 16, 2026 Publication Type: Technical White Paper / Domain Analysis Abstract The integration of geospatial data with time series analytics (geo-temporal data) is critical for climate monitoring, urban planning, logistics, and IoT sensor networks. This paper examines the conceptual and technical architecture of geo-ts.com , a hypothetical or observed domain specializing in geo-temporal data services. We propose a standard framework for such a platform, including data ingestion, storage, query processing, and visualization. The paper concludes with best practices for implementing scalable geo-temporal solutions. 1. Introduction Traditional databases handle either spatial data (e.g., PostGIS) or time series (e.g., InfluxDB) separately. However, many modern applications—such as tracking vehicle fleets, monitoring sea surface temperatures, or analyzing satellite imagery—require joint queries: “Show me all temperature readings within this polygon over the last 72 hours.” "sensor_id": "S-1023", "lon": -73
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