Geospatial visualization was carried out to compare and choose a better model for classifying anomalies on the city's streets. The main reason why KNN was a better fit was that it did not require **data** scaling for its inputs. Thus, the KNN accurately handled a street with deep potholes, speed bumps without any paint or signaling, and a generally more chaotic avenue that generated bigger sensor signals.