Urban emergency logistics route planning model based on geospatial data mining and spatial searching tree algorithm

In response to the current situation of the material transportation required at delivery points during the major urban emergencies, we construct an urban emergency logistics route planning model based on the geospatial data mining and spatial searching tree algorithm under the geospatial constraints. Firstly, a k-center clustering algorithm is constructed for the delivery points in the urban geographic space to achieve the spatial clusters. On this basis, we construct an algorithm for selecting the starting and ending distribution center based on the number of delivery points and clusters that require materials, and determine the distribution center for the material transfer. Finally, a spatial searching tree algorithm is constructed based on the transportation cost, in which, an optimal complete binary tree of transportation costs under various logistics route conditions is output to confirm the route with the lowest logistics cost, providing the decision support for the emergency management departments. Experimental results show that the constructed algorithm model can effectively cluster delivery points and determine the optimal distribution center and the logistics route with the lowest cost. Comparing with the traditional route planning algorithm such as Dijkstra algorithm, the proposed algorithm can reduce the logistics route cost and has higher efficiency.