Automatic Zoom Level Prediction for Informal Location Descriptions

Abstract

This paper is concerned with the automatic prediction of the zoom level at which to present a map with results for an informal location description. We propose the use of identifiability (relative uniqueness) and zoom level of each component geospatial expression (GE) in the location description, as a means of predicting the appropriate zoom level for the overall description. We apply a simple classification approach to zoom level prediction, and compare results using gold-standard and automatically-inferred GE information. We find the approach to have strong promise, including relative to the zoom level used in results from Google Maps for our location descriptions dataset.