Uncovering Spatiotemporal and Semantic Aspects of Tourists Mobility Using Social Sensing

TL;DR

This paper leverages social sensing data to analyze tourists' spatiotemporal and semantic mobility patterns across major cities, revealing behavioral differences and proposing a topic model for automatic pattern identification.

Contribution

It introduces a novel social sensing approach combined with a topic model to automatically identify and analyze tourists' mobility patterns considering cultural and regional factors.

Findings

Distinct behavioral patterns between tourists and residents.

Cultural and regional influences significantly affect mobility.

The proposed model effectively identifies mobility themes.

Abstract

Tourism favors more economic activities, employment, revenues and plays a significant role in development; thus, the improvement of this activity is a strategic task. In this work, we show how social sensing can be used to understand the key characteristics of the behavior of tourists and residents. We observe distinct behavioral patterns in those classes, considering the spatial and temporal dimensions, where cultural and regional aspects might play an important role. Besides, we investigate how tourists move and the factors that influence their movements in London, New York, Rio de Janeiro and Tokyo. In addition, we propose a new approach based on a topic model that enables the automatic identification of mobility pattern themes, ultimately leading to a better understanding of users' profiles. The applicability of our results is broad, helping to provide better applications and services in the tourism segment.