Adaptive visualization of tourists' preferred spots and streets using trajectory articulation
Adaptive visualization of tourists' preferred spots and streets using trajectory articulation
复制标题
使用轨迹清晰度对游客喜欢的景点和街道进行自适应可视化
DOI：
10.1145/3557921.3565539
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Masatoshi Arikawa and Lu Min
中科院分区：
文献类型：
--
作者：
Iori Sasaki;Masatoshi Arikawa and Lu Min
徒步旅游是以有趣的主题组织区域资源，可以为游客提供原始的当地徒步体验。我们的项目旨在通过移动的应用程序收集用户数据，并探索潜在的地理资源，如吸引人的景点和街道，以改善城市规模的旅游。带有GPS轨迹数据的密度图是可视化它们的最简单方法之一，而无需任何建模成本。然而，用户和技术因素都使得很难以详细和简洁的方式解释热图。具体地，分析者难以使用数据基于热图来破译真实的感兴趣区域，因为与高密度GPS位置相关联的区域可能不仅仅是由于它们的吸引力，例如，休息区。此外，不保留街道地形的热图无法实现热街可视化。在我们的研究中，内置的智能手机传感器被用来区分多个用户上下文（例如，停止/步行和室内/室外），这使得每个GPS轨迹中的固有密度偏差的程度相等，并将属性添加到每个位置点。我们的分析软件累积处理后的轨迹，并通过应用不同的权重规则（例如，面向街道的规则和面向室内的规则）。我们的移动的合作方法实现了自适应热图生成分析的期望，即，简洁的热点可视化和热街可视化。
Walking tourism, in which regional resources are organized with interesting themes, can provide visitors with original local walking experiences. Our project aims to collect user data through a mobile application and explore potential geographic resources such as appealing spots and streets for improving city-scale tourism. A density map with GPS trajectory data is one of the easiest ways of visualizing them without any modeling costs. However, both user and technical factors make it difficult to interpret the heatmap in a detailed and concise way. Specifically, analysts have difficulty in deciphering the areas of real interest based on the heat map using the data as areas associated with high density of GPS locations may not be solely due to their attractiveness, e.g., rest areas. In addition, the heat map that does not retain the topography of the streets cannot achieve hot street visualization. In our research, built-in smartphone sensors are employed to distinguish multiple user contexts (e.g., stopping / walking and indoors / outdoors) during their walking tours, which equalize the degree of inherent density biases in each GPS trajectory and add attributes to each location point. Our analysis software accumulates the processed trajectories and generates a density map by applying different weight rules (e.g., a street-oriented rule and an indoor-oriented rule) based on semantic attributes and analytical requests. Our mobile cooperative approach realizes adaptive heatmap generation to the analyzer's expectations, that is, concise hot spots visualization and hot streets visualization.
点模式分析
DOI：
10.22224/gistbok/2020.1.13
发表时间：
2020
期刊：
Geographic Information Science & Technology Body of Knowledge
影响因子：
--
作者：
Yihong Yuan;Y. Qiang;Khan Mortuza Bin Asad;Edwin Chow
通讯作者：
Edwin Chow
用于回顾徒步旅行的铰接轨迹映射
影响因子：
3.4
作者：
Sasaki;I.;Arikawa;M. and Takahashi;A.
通讯作者：
A.