Harnessing Big Data to Identify Ideal Locations for Warehouses and Bike Share Stations - Dr Hui Xiong, Rutgers University

With a sharp increase in the public use of online shopping in recent years, which has spiked further due to the COVID-19 pandemic, the importance of warehouse positioning to provide an optimal delivery service has become a significant area of focus for retailers. Similarly, bike sharing in major cities has also seen an astronomical rise in usage – prompting questions about how bike stations can be best positioned. Stations in optimised locations would require minimal interference to change the stock levels, while also ensuring the greatest accessibility to users. Dr Xiong and his colleagues at Rutgers University have been using the ubiquity of big data to better optimise these systems for both cost efficiency and standard of service to customers, testing their findings in real-world scenarios.