There are two approaches I can think of, depending on your gut about your data set. If single or minimal-word phrases are enough (e.g. 'kws' 'kei' 'hei' 'keih'

There are two approaches I can think of, depending on your gut about your data set. If single or minimal-word phrases are enough (e.g. 'kws' 'kei' 'hei' 'keih' etc), something like word2vec. Except instead of training to predict distance between words, you're generating an embedding between words and ports. If more complex modeling is required (e.g. 'tokyo tuesday kei then chiba'), something like LSTM [1]. The lat-long data you'd want to squish into a closest-port training set via geospacial math. Haven't done anything in the space, but I'd imagine chewing through lat-long legs and computing closest-approach distances to various ports (that is, to canonical port lat-long). Optimized for computation and space. Probably compressed down into a "visit / not-visit" binary feature. Might take awhile, but the math seems straightforward for this bit. [1] http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/