Papers with Point-to-City
Identifying Linguistic Areas for Geolocation (D19-55)
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| Challenge: | a recent study shows that social media posts are often given as continuous coordinates . but, the resulting discrete coordinates do not always correspond to existing linguistic areas . |
| Approach: | They propose an algorithm for clustering coordinates and associating them with towns using point-to-city (P2C) they compare accuracy of a state-of-the-art geolocation model with P2C labels to one with regular k-d tree labels. |
| Outcome: | The proposed method improves accuracy at 100 miles, but degrades for finer-grained distinctions . iterative k-d tree-based method can cluster coordinates and associate them with towns . |