Delivery times and Regionalization - Börjesson Lindgren, Aron (My University, 2024)
This project aimed to find a solution to partition geographical maps into regions based on historical lead time data of delivered parcels. The guidelines of the partitioning were to keep lead time distributions of areas within one region as similar as possible, while keeping distributions of different lead times as different as possible. The partitioning algorithm would also preferably execute the task in reasonable time. Two algorithms were implemented and tested. One was an adaptation of Ant Colony Optimization, that proved to strongly respect the specified criteria, but at the cost of very high computation time. The second algorithm goes by the name REDCAP for spatial clustering, and this one was more thoroughly explored. It works by building a minimum spanning tree between all atomic level areas as nodes, clustering them together in a bottom-up approach while considering up to all nodes in every currently merged cluster along the way. The algorithm comes with some options to be specified: How to compute the dissimilarity between two clusters (single-linkage, complete-linkage, average-linkage), and which nodes of the clusters to consider (first-order constraint, full-order constraint). The data, represented as histogram vectors with each bin representing days passed since pickup, all split up by pickup day of the week, also had to be compared using some measure of dissimilarity. The dissimilarity measures explored were L1-distance, L2-distance, earth mover distance, and Jaccard index. This allowed for many different possible partitionings despite the same principle. To compare the result, consensus clustering was applied to measure how consistently the same algorithm partitions the same nodes of a data set together when making small adjustments to the data itself. It allowed to determine the quality of each option combination, and its ability to select how many regions to suggest. For a couple of data sets, single-linkage and earth-mover distance failed to give out a clear result and were removed as options. First-order constraint had a habit of cutting out single node size regions, and were therefore also removed. The combinations of the other options will all run on any new data set, and they each got a vote for the final partitioning. Some visual plots confirm that this approach seems to give reasonable results. Since deliveries tend to take longer to islands and remote locations, the algorithm tends to perform most of its partitioning there, and group country mainlands into one single region. Deliveries in Sweden showed to be an exception to this pattern, due to a lot of remoteness in the mainland, yielding regions of more equal area size. These regions were compared to those of the provided estimations of lead time, which turned out to match well overall, with some minor differences.
Keywords: Regionalization, Spatial clustering, Graph theory, REDCAP, ACO, Consensus clustering.