Semantic Place Descriptors for Classification and Map Discovery

TL;DR

This paper presents a method to automatically annotate city maps with semantic place categories by analyzing large-scale mobile phone and social media data, improving map understanding and discovery.

Contribution

It introduces a novel approach using kernel density estimation on mobile and Twitter data to automatically identify and annotate semantic places in urban environments.

Findings

Usage data strongly predicts semantic place categories

Automatic map annotation improves with large-scale mobile data

Social media data enhances place discovery accuracy

Abstract

Urban environments develop complex, non-obvious structures that are often hard to represent in the form of maps or guides. Finding the right place to go often requires intimate familiarity with the location in question and cannot easily be deduced by visitors. In this work, we exploit large-scale samples of usage information, in the form of mobile phone traces and geo-tagged Twitter messages in order to automatically explore and annotate city maps via kernel density estimation. Our experiments are based on one year's worth of mobile phone activity collected by Nokia's Mobile Data Challenge (MDC). We show that usage information can be a strong predictor of semantic place categories, allowing us to automatically annotate maps based on the behavior of the local user base.