Spatio-temporal dependency analysis for temporally-shifted Web Sensors

We experience or forecast various phenomena (e.g., rain, snow, earthquake) in the physical world, while we carry out various actions (e.g., blogging, searching, e-shopping) in the Web world. There have been many researches to mine the exploding Web world for knowledge about various phenomena and events in the physical world, and also Web services with the Web-mined knowledge have been made available for the public. However, there are few detailed investigations on how accurately Web-mined data reflect physical-world data. It must be socially-problematic to idolatrously utilize the Web-mined data in public Web services without ensuring their accuracy sufficiently. This paper introduces temporally-shifted Web Sensors with a temporal shift parameter δ to extract spatiotemporal numerical value about a physical phenomenon from Web documents searched by linguistic keyword(s) representing the physical phenomenon, and analyzes the spatiotemporal dependency of the temporal shift parameter δ with respect to their coefficient correlation with Japan Meteorological Agency's spatiotemporal statistics.