基于微博签到数据的出行行为分析

应用Python爬虫程序,通过新浪API端口爬取了新浪微博2012年的地点签到数据,共计5 028 980条。将这些数据按城市划分,共分为340个地级以上的城市或地区。通过统计发现,签到次数最多的3个城市为北京、上海和广州,说明微博用户更多地活跃在这三个城市。进一步通过相关性分析发现,这些城市的微博用户签到流量和当地GDP呈一定的相关性,说明经济发展水平会影响用户的旅行行为。此外,本文还按照用户的出行流量对各大城市进行了聚类划分,进一步印证了经济发达城市对微博用户签到的吸引会高于其他经济欠发达的城市。

Travel behavior analysis based on Weibo check in data

Using Python crawler, the location check in data of Sina Weibo in the year of 2012 were crawled through the Sina API port. The data set consisted of 5,028,980 records. These data were divided into 340 cities or regions above prefecture level. Data statistics showed that there was the largest number of check-in in 3 cities: Beijing, Shanghai and Guangzhou, which revealed that Weibo users were more active there. Furthermore, through correlation analysis, it was found that the Weibo users′ attendance flow in these cities was related to the local GDP, indicating that the level of city economic development would affect the users′ travel behavior. In addition, this paper also divided the major cities into clusters according to the users' trip volume, further confirming that the developed cities were more attractive to Weibo users than other economically underdeveloped cities.