社交媒体地理大数据的旅游景点热度分析

Popularity analysis of tourist attraction based on geotagged social media big data

针对大数据时代,蕴含地理位置信息的社交媒体(Social Media)数据规模正呈爆炸性增长,通过对这类时空数据的挖掘可以整合用户的群体智慧,发现热门景点或线路,为用户提供更加精细的旅行服务。该文基于2005—2016年Flickr图片分享网站中用户分享的带地理标签的图片信息,通过空间聚类以及文本语义挖掘的方法对北京市的热门景点进行了提取。此外,本文还利用北京市的历史天气信息与用户图片信息进行融合,分析在不同时间、不同天气情景下,不同景点的热度分布规律,可以为旅行爱好者提供热门景点在多种视角下的游览规律。

In the big data era,the volume of geotagged social media data that contains location information has gained an explosive growth.Through integrating the group knowledge from the users,mining geotagged social media big data can discover popular tourist attractions and travel routes,and provide more precise travel services.Based on the 2005-2016 geotagged photos provided by Flickr users,this paper used spatial clustering and text semantic mining approaches to extract the popular tourist attractions in Beijing.Furthermore,together with historical weather data,we described the tourist attraction popularity patterns in Beijing under different time and different weather conditions.From multiple points of view,our work enables successfully mining travel laws for popular tourist attractions based on geotagged social media big data.