Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
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.
Recommended Citation
Xie, Huaze; Li, Da; Wang, Yuanyuan; and Kawai, Yukiko, "Visualization of POI Category on the Dynamic Rasterized Map Tiles from Geo-Tagged Social Media (Twitter) with SZ-GAT" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 12.
https://aisel.aisnet.org/hicss-56/dsm/data_analytics/12
Visualization of POI Category on the Dynamic Rasterized Map Tiles from Geo-Tagged Social Media (Twitter) with SZ-GAT
Online
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.
https://aisel.aisnet.org/hicss-56/dsm/data_analytics/12