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# city-transformers
Generates dataset of cities (US only for now) and their geodesic distances.
Uses that dataset to fine-tune a neural-net to understand that cities closer to one another are more similar.
Distances become `labels` through the formula `1 - distance/MAX_DISTANCE`, where `MAX_DISTANCE=20_037.5 # km` represents half of the Earth's circumfrence.
There are other factors that can make cities that are "close together" on the globe "far apart" in reality, due to political borders.
Factors like this are not considered in this model, it is only considering geography.
However, for use-cases that involve different measures of distances (perhaps just time-zones, or something that considers the reality of travel), the general principals proven here should be applicable (pick a metric, generate data, train).
A particularly useful addition to the dataset here:
- airports: they (more/less) have unique codes, and this semantic understanding would be helpful for search engines.
- aliases for cities: the dataset used for city data (lat/lon) contains a pretty exhaustive list of aliases for the cities. It would be good to generate examples of these with a distance of 0 and train the model on this knowledge.
see `Makefile` for instructions.