An Efficient Method for Recommending Branch Locations to Reduce the Transportation Distance between Stations and Urban Events

Urban areas need to deploy a lot of services and stations. This work considers the issue of establishing new branches for a certain service. Given a number of stations we plan to construct, our goal is to recommend locations as deploy placements and transportation cost could be efficiently reduced by jointly considering road network, existing stations and spatial event data. Our model can be divided into four parts: 1) Adopting DBSCAN clustering method to find hot spots of spatial events. 2) Doing community detection for road network to split the road network to smaller components. 3) Exploiting a refined closeness centrality to identify a good candidate location in each community. 4) Developing a greedy-based distance minimized method to establish stations sequentially. The results show our solution is effective and efficient for a large crime event dataset of Chicago.