Describing Explored Places through OpenStreetMap Data

要旨

Mobile navigation applications are good at providing efficient navigation instructions. However, they currently lack the capability to facilitate free exploration. Therefore, users are limited to encountering only places close to the shortest paths, neglecting places that could diversify navigation and foster spatial learning. To better understand what characteristics places have that users like to explore we collected a dataset with a mobile application that encourages free exploration using gamification (n = 39, t = 455 days, 106.50 km2). Using OpenStreetMap data, we found highly frequented freely explored places comprising office, educational, retail, touristic and commercial places. When comparing the characteristics of the freely explored places to those along the shortest path, those categories were different. Based on our findings, we propose that implementing more diverse routing algorithms can enhance navigation diversity, improve spatial learning, and optimise the utilisation of urban spaces for travel.

著者
Eve Schade
University of St. Gallen, St. Gallen, Switzerland
Gian-Luca Savino
University of St. Gallen, St. Gallen, Switzerland
Jasmin Niess
University of Oslo, Oslo, Norway
Johannes Schöning
University of St. Gallen, St. Gallen, Switzerland
DOI

10.1145/3706598.3713695

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713695

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Crowdsourcing and Tech in the Wild

Annex Hall F204
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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