LOCAST: Optimal Location Casting by Crowdsourcing and Open Data Integration

Abstract

Social media dominance largely affects multi-store brands success potential. How can a brand choose the optimal place to locate its stores, given the social media pulse? Which are the suitable metrics to guide such challenging decisions? This work addresses such crucial problems by a novel location casting approach which extracts and integrates knowledge from open data and social media, providing specific indicators and a systematic pipeline for effective locations casting. Emphasis is placed on how the derived knowledge will assess the particular characteristics of accessibility, interest, and centrality to identify fine grained urban indicators. The proposed pipeline predicts the success potential of a chain store's location, under individual or combined such indicators individually. The experimentation under qualitative tests, indicates that the proposed approach provides reliable estimations of brand's locations suitability, and also outperforms existing similar state-of-the-art approaches.