基于微博数据的桂林旅游流时空变化分析

A Weibo Data-Based Analysis of Spatio-Temporal Change of Tourism Flows in Guilin

利用爬虫技术获取微博旅游数据,对2016-2019年到访桂林市漓江风景区、阳朔风景区的国内旅游流时空变化进行分析.结果表明:(1)漓江及阳朔旅游流的时间分布不均,两地游客时间集中指数显示旅游季节性明显;2016-2019这4年漓江各客源省份游客时间集中指数明显高于阳朔,漓江游客量季节变化程度远高于阳朔.(2)漓江和阳朔客源组成显著不同,数据分析显示两地游客重叠度极低;漓江旅游流变异系数变化较大,客源地游客量波动较大,稳定性较差.阳朔旅游流变异系数基本保持稳定,年度间旅游流空间差异变化不大,客源地游客量保持相对稳定.(3)两地旅游流都具有显著的空间自相关性,但聚集和异常区域略有不同;漓江客流高-高和低-低聚集区均不稳定,数量和区域变化较为明显,没有显著的趋势变化;阳朔客流高-高聚集区呈现微弱的扩张趋势,低-低聚集区呈现较明显的扩张趋势.在空间分布上,两地的旅游客流最大值分布在两广地区,北京是第二大客源地,其次为上海;总体上看,中部地区(湖北、河南)以及华东地区(浙江、江苏、山东)旅游客流量大于除两广和北京、上海的其他省市.(4)漓江游客量呈现缓慢的增长趋势,波动较小,游客量增长率最高的省份为青海、内蒙古、贵州及海南;阳朔游客量波动比较剧烈,总体呈现平稳略微下降趋势,增长率最高的省份为青海.(5)两地旅游流和客源地距离存在显著负相关,旅游客流量随着距离增加而显著减少.漓江及阳朔旅游流与人均可支配收入之间没有显著的相关性,原因在于来桂林旅游平均消费在城镇居民可支配收入中占比较小.(6)不考虑节事活动引发的暂时性旅游流爆发,桂林漓江及阳朔旅游流变化与温度变化曲线基本吻合,符合旅游气候适宜度分析结果,随着温度增加旅游人数上升;而节事活动的引入可以明显提升客流量.

The crawler technology was used to obtain the tourism data of Weibo,and the temporal and spatial changes of domestic tourism flows during visits to Li River Scenic Area and Yangshuo in Guangxi from 2016 to 2019 were analyzed.The results obtained were as follows.(1)The time distribution of Li River and Yangshuo tourist flows was uneven,and the time concentration index of tourists exhibited an obvious seasonality.From 2016 to 2019,the time concentration index of tourists from the Li River’s source provinces was significantly higher than that of Yangshuo,and the seasonal variation degree of tourists on the Li River was much higher than that of Yangshuo.(2)The composition of tourists from Li River and Yangshuo was significantly different,and data analysis showedthat there was very little overlap between the two places.The variation coefficient of Li River tourism flow changedmarkedly,the tourist volume of tourist source fluctuated greatly,and the stability was poor.The variation coefficient of tourism flow in Yangshuo was basically stable,the spatial difference of tourism flow between years had little change,and the number of tourists from tourist sources remained relatively stable.(3)Tourism flows of both placesshowed a significant spatial autocorrelation,but the clustering and abnormal areas were slightly different.The passenger flow in the Li River was unstable in both high-high and low-low concentration areas,with obvious changes in quantity and area andwith no obvious trend changes.Yangshuo passenger flow high-high clustering area showed a weak expansion trend,low-low clustering area showed a more obvious expansion trend.In terms of spatial distribution,the maximum tourist flow of the two places wasfrom Guangdong and Guangxi.Beijing was the second largest tourist source,followed by Shanghai.On the whole,the tourist flow in central China(Hubei,Henan and Sichuan)and east China(Zhejiang,Jiangsu and Shandong)was larger than that in other provinces/municipalities except Guangdong,Guangxi and Beijing and Shanghai.(4)Li River tourist volume showed a slow growth trend and small fluctuations.Qinghai,Inner Mongolia,Guizhou and Hainan had the highest growth rate of tourist volume.Yangshuo’s tourist volume fluctuated violently,showing a stable and slightly decreasing trend on the whole,and Qinghai had the highest growth rate.(5)There was a significant negative correlation between tourist flow and the distance of tourist source,and the tourist flow decreased significantly with the increase of the distance.There was no significant correlation between the Li River and Yangshuo tourism flows and per capita disposable income,because the average consumption of tourists to Guangxi constituted but a small part in the disposable income of urban residents.(6)Regardless of the temporary outbreak of tourism flow caused by festival activities,the changes of Li River and Yangshuo tourism flow in Guilin basically coincided with the curve of temperature change,which conformed to the results of tourism climate suitability analysis.The number of tourists rose with increasing temperature.The introduction of festival activities could significantly improve passenger flow.