Intelligent Marketing in Smart Cities: Crowdsourced Data for Geo-Conquesting

Abstract

The authors' approach to intelligent marketing in smart cities uses large-scale crowdsourcing based on mobile user behavior for market planning. Unlike most work, which focuses on the client side, the proposed method is intended for market planning from a marketer perspective. The proposed system tracks and examines user trails via mobile devices. To support distributed analysis for big data, computer clusters project user trajectories onto blocks in a layout map of an actual geo-location. The trajectory falling into a block is independently handled by a distributed computer that detects crowd flows. Because traffic flows are highly relevant to geo-conquesting marketing, two methods are proposed: local longest common subsequence-based (LLCS-based) clustering, and the computation of transition flows and heat maps. Both help marketers analyze crowd flows for geo-conquesting. Simulation of the proposed methods revealed that the system facilitated computation of large-scale crowdsourced data.

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