City names in the voting data can appear in many forms (e.g., "NYC", "New York", "New York City"). Design an approach to cluster or normalize these variants into canonical entities so votes aggregate correctly. Describe rules-based and learning-based methods (e.g., token normalization, fuzzy matching, phonetic keys, vector embeddings), how to select similarity thresholds, handle ambiguous names (e.g., "Springfield"), and evaluate and maintain the mapping over time.