A Graph Neural Network-based Map Tiles Extraction Method Considering POIs Priority Visualization on Web Map Zoom Dimension
A Graph Neural Network-based Map Tiles Extraction Method Considering POIs Priority Visualization on Web Map Zoom Dimension
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DOI：
10.1109/access.2022.3182497
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Huaze Xie;Da Li;Yuanyuan Wang;Yukiko Kawai
中科院分区：
文献类型：
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作者：
Huaze Xie;Da Li;Yuanyuan Wang;Yukiko Kawai
由于移动的网络的巨大普及，基于位置的社交网络（LSBN）的兴趣点（POI）数据提供了重要的地图上的地理信息，并可以用来讨论动态特性的地图瓦片分割的城市道路。针对地图瓦片动态特性分析问题，提出了一种基于全局注意力机制的空间缩放图注意力模型（SZ-GAT）。此外，社交媒体数据集（Twitter与地理位置）被用来促进POI可视化在不同的缩放级别，并提高聚合效率的地理记录在缩放尺寸。在实验中，我们从Twitter中提取POI地理特征，并在每个地图缩放级别上显示用户最喜欢的POI特征和5维推文属性。我们评估的准确性的POI预测谷歌，OpenStreetMap，Bing和雅虎!通过比较推特的访问历史来绘制地图。在京都市随机选取的60幅地图上，该方法的预测性能在每个缩放级别上都超过86%。
Owing to the tremendous popularity of mobile networks, point-of-interest (POI) data of location-based social networks (LSBN) provide significant geographic information on maps and can be utilized to discuss the dynamic characteristics of map tiles as segmented by city roads. In this study, motivated by the problem of the dynamic characteristic analysis of the map tile, we propose a spatial-zoom graph-attention model (SZ-GAT) based on a global-attention mechanism and 5-category POI attributes for each map tile zoom dimension. Furthermore, a social-media dataset (Twitter with geolocation) is utilized to promote POI visualization at different zoom levels and improve the aggregation efficiency of geographic records in zoom dimensions. In the experiments, we extract POI geo-features from Twitter and display the user’s favorite POI features at each map zooming level with 5-dimensional tweet attributes. We evaluate the accuracy of the POI prediction on Google, OpenStreetMap, Bing, and Yahoo! maps by comparing the tweets’ visit history. The predictive performance of the proposed method is more than 86% for each zoom level on 60 randomly-selected map tiles in Kyoto City.