基于互联网大数据的旅游需求分析——以北京怀柔为例

Tourism demand analysis based on Internet big data:The case of Huairou,Beijing

互联网大数据为经济金融以及旅游等行业的准确分析及预测提供了良好的数据基础．随着我国旅游业的蓬勃发展，旅游需求分析及旅游管理迫切需要实时且准确的数据作为支撑．本文以北京怀柔为例，收集两类互联网大数据进行实证研究．首先通过挖掘互联网搜索数据分析旅游目的地的热度，进而利用主成分分析方法构建搜索指数衡量旅游需求；其次通过利用携程网怀柔游客大数据分析游客的基本特征及游客旅游行为，即游客对旅游产品、景区和酒店的偏好．综合大数据分析结果，为旅游管理实践提出相应的建议．本研究有望为大数据时代下的旅游管理提供新的基于数据的分析视角．

Internet big data have provided new data sources for timely analysis and accurate forecasting of economy, finance, and other industries. With the development of tourism industry in China, it is necessary to incorporate more accurate data sources for tourism demand and tourism management. This paper collected two kinds of Internet big data to examine the application of big data in Huairou, Beijing. We first crawled the Internet search data from Baidu search engine, and used principle component analysis to construct search index in order to represent tourism demand. Then, we used online travel data generated fl-om Ctrip to depict the tourists＇ characteristics such as preference to tourism spots and hotels. Based on the empirical results, we offer suggestions for tourism management. This paper will provide innovative perspectives for tourism management in the big data era.