Thematic Geo-Density Heatmapping for Walking Tourism Analytics using Semi-Ready GPS Trajectories
Thematic Geo-Density Heatmapping for Walking Tourism Analytics using Semi-Ready GPS Trajectories
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DOI：
10.1109/bigdata55660.2022.10020743
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Iori Sasaki;M. Arikawa;Min Lu;Ryo Sato
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文献类型：
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作者：
Iori Sasaki;M. Arikawa;Min Lu;Ryo Sato
为了生成有益的报告，以改善步行旅游的内容，本文讨论了方法，可视化游客的兴趣在城市的地理特征，通过自动收集他们的反馈，通过移动的应用程序。使用原始轨迹的地理密度地图通常难以确定其上呈现的密集区域是否比城市中的其他区域更有吸引力，因为环境、用户行为和技术因素呈现不同程度的密度，而不管游客活动如何。此外，轨迹中复杂的点模式低估了折线形状的特征，如人行道和街道，这是步行旅游中最重要的组成部分，它们的密度图阻止了热街道的推断。所提出的框架监视用户行为，例如，进入/离开室内空间和步行/停止，使用各种移动的传感器通过（1）均衡轨迹中的固有密度偏差，（2）减轻GPS记录的误差，（3）添加描述旅游期间的具体用户事件的属性来生成用于城市规模旅游者分析的主题热图的半就绪轨迹数据。此外，我们的客户端系统演示了从半就绪数据中绘制的热门街道热图、面向用户生成内容（UGC）的热点热图和面向室内的热点热图。本文进行的评估实验表明，所提出的数据能够更准确地表示实际的步行轨迹比原始GPS轨迹和现有的线简化方法。
With the aim to generate beneficial reports to improve the content of walking tourism, this paper discusses methods to visualize tourist interest in geographic features in a city by automatically collecting their feedback through mobile applications. Geo-density maps that use raw trajectories generally have difficulty in determining whether the dense areas presented on them are more appealing than the other areas in a city, since environmental, user-behavioral, and technical factors present various degrees of density regardless of tourist activity. Additionally, the complicated point patterns in a trajectory underestimate polyline-shaped features, such as footpaths and streets, which are some of the most important components of walking tourism, and their density maps prevent the inference of hot streets. The proposed framework monitors user behavior, e.g., entering/leaving indoor spaces and walking/stopping, using various mobile sensors to generate semi-ready trajectory data for thematic heatmaps of city-scale tourist analysis by (1) equalizing the inherent density bias in a trajectory, (2) mitigating the errors of GPS recording, (3) adding attributes that describe concrete user events during tourism. Furthermore, our client system demonstrates hot street heatmaps, user-generated content (UGC)-oriented hot spot heatmaps, and indoor-oriented hot spot heatmaps drawn from semi-ready data. The evaluation experiment conducted in this paper illustrates that the proposed data were able to represent the actual walking trajectory more accurately than the raw GPS trajectory and existing line simplification approaches.