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カワイ ユキコ
KAWAI YUKIKO
河合 由起子 所属 京都産業大学 情報理工学部 情報理工学科 職種 教授 |
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| 発行・発表の年月 | 2023 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | Visualization of POI Category on the Dynamic Rasterized Map Tiles from Geo-Tagged Social Media (Twitter) with SZ-GAT |
| 執筆形態 | その他 |
| 掲載誌名 | Proceedings of the Annual Hawaii International Conference on System Sciences |
| 出版社・発行元 | ScholarSpace |
| 巻・号・頁 | 2023-January,2210-2219頁 |
| 著者・共著者 | Huaze Xie,Da Li,Yuanyuan Wang,Yukiko Kawai |
| 概要 | Spatial zooming graph attention networks (SZ-GAT) is an emerging framework to improve the quality of recommended places visualization on the map. With the advent of location sharing on social networks via mobile devices, the geographic characteristics of the user's points of interest (POIs) contain the visit history, map check-in positions, recommended places, and route plans. In the context of user-preferred POI prediction with map zooming SZ-GAT framework, we propose a visualization for raster category exploration that uses tweet user visit history to represent the POI visit popularity of the raster units. We concentrate on the performance of the POI data visualized map layer zooming process and our results show that the SZ-GAT framework has a better performance of raster category regression with the baselines. Raster category prediction will be used for urban area division, dynamic category feature extraction with user visit history, and government policy-making based on user behaviors of map tiles. This study promotes the progress of deep learning and data mining in the field of human geographic information. |
| ISSN | 1530-1605 |
| DBLP ID | conf/hicss/XieL0K23 |
| PermalinkURL | https://hdl.handle.net/10125/102904 |
| researchmap用URL | https://dblp.uni-trier.de/rec/conf/hicss/2023 |