PostgreSQL Opens the Door to Space and Time

This article discusses spatiotemporal data and its wide usage in multiple domains, such as indoor positioning, pilotless driving, and scientific research.

By digoal

Background

Spatiotemporal data is everywhere and will account for an increasing proportion in the future. The demand for spatiotemporal data in TP and AP scenarios will also increase.

Site Selection and Grid-based Operation: Automatic spatial data aggregation and analysis, time- and polygon-based tagging, retention time analysis, public opinion analysis, and so on.
Indoor Positioning: 3D coordinates, relative coordinate system, analytic reporting of marketing activity results, and so on.
Scientific Research: Space exploration, surveying and mapping, meteorology, earthquake prediction, traceability, and so on.
Pilotless Driving: Point cloud, dynamic path planning, and so on.
Space Scheduling (Cainiao, Eleme, DiDi, Amap, and Express Delivery): Real-time location update; polygon-based tagging; one-to-many, many-to-many, and many-to-one business travel problems; KNN search; and so on.
LBS: Real-time location update; recommendations based on GIS and other conditions (Tantan app); tagging based on distance and other attributes (value-added services of maps, such as searching for restaurants and gas stations); internet of vehicles (IoV); ride-hailing; and so on.
Navigation: Positioning and dynamic path planning.
Spatiotemporal Perspective: Sliding windows, stream computing, multidimensional analysis, and so on.
Visual Search: Converting text search to visual search; searching for visitors who visited a specified store (store ID) for X to Y times (number of visits) or met some other conditions; and so on.

PostGIS Case Study

Representative Spatial Database and Open-Source Geospatial Organization

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