A Graph Neural Network-Based Map Tiles Extraction Method Considering POIs Priority Visualization on Web Map Zoom Dimension

Abstract

Owing to the tremendous popularity of mobile networks, point-of-interest (POI) data of location-based social networks (LBSN) 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, to implement 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 56% for each zoom level on 60 randomly-selected map tiles in Kyoto City.