基于LBSN大数据的旅游目的地类簇选点及热度分析
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摘要
利用江苏省南京市手机用户微信LBSN大数据和原始气象数据,建立了一种基于CFSFDP聚类分析的旅游目的地的重点热门地点的识别方法,并结合TF-IDF文本分析法和ArcGIS地理信息技术对分析方法进行修正,对南京市紫金山和玄武湖一山一水两大旅游目的地进行类簇选点分析。在识别出具体景点之后再结合气象数据对其热度进行分析。结果表明:这种基于LBSN大数据的旅游目的地类簇点选取方法具有科学性和可行性,可以准确识别出目标区域内的热门景点,同时可以发现数据点越密集,LBSN大数据量越大,类簇点识别的准确性越高。旅游目的地的旅游热度在周末和旅游旺季,春夏两季数值较高。本文的研究结果可以在获取LBSN大数据的情况下,对相关部门进行旅游规划和游客出行提出科学性的指导。
In this paper, an identification method based on CFSFDP clustering analysis of the hot spots in scenic area was established using the original meteorological data and the LBSN data collected from WeChat users in Nanjing, Jiangsu province. With the use of TF-IDF text analyzing method and ArcGIS techniques, the identification method was revised to apply on two scenic areas in Nanjing: the Zijin Mountain area and the Xuanwu Lake area to identify hot spots. The prevailing trends of the scenic spots was analyzed after the recognition of specific scenic spot with the reference of meteorological data. The result of this study shows that this method based on LSBN Big Data is scientific and feasible in identifying hot spots in target scenic area, and the accuracy of this method is positively correlated with the density of the data spots and the amount of the LSBN Big Data. The quantification values of the prevailing trends in scenic areas are higher in weekends and tourist seasons. Values in spring and summer are also higher compared to other seasons. The achievement of this study provides a scientific guidance in scenic area planning and tourist visiting with the help of LBSN Big Data.
In this paper, an identification method based on CFSFDP clustering analysis of the hot spots in scenic area was established using the original meteorological data and the LBSN data collected from WeChat users in Nanjing, Jiangsu province. With the use of TF-IDF text analyzing method and ArcGIS techniques, the identification method was revised to apply on two scenic areas in Nanjing: the Zijin Mountain area and the Xuanwu Lake area to identify hot spots. The prevailing trends of the scenic spots was analyzed after the recognition of specific scenic spot with the reference of meteorological data. The result of this study shows that this method based on LSBN Big Data is scientific and feasible in identifying hot spots in target scenic area, and the accuracy of this method is positively correlated with the density of the data spots and the amount of the LSBN Big Data. The quantification values of the prevailing trends in scenic areas are higher in weekends and tourist seasons. Values in spring and summer are also higher compared to other seasons. The achievement of this study provides a scientific guidance in scenic area planning and tourist visiting with the help of LBSN Big Data.
引文
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[2] Hollenstein L, Purves R. Exploring place through user-generated content: Using Flickr to describe city cores[J]. Journal of Spatial Information Science, 2010, 1(1):21-48.
[3] 潘峰,唐晓岚,吴雷,等. 基于RS & GIS的内湖岛屿湖域视景资源开发[J]. 水土保持通报,2017,(03):1-6.
[4] 李克清,葛柳飞,戴欢. 基于CFSFDP与ELM相结合的半监督室内定位算法[J]. 武汉大学学报(理学版),2016,(05):451-455.
[5] 吴文浩,吴升. 多时间尺度密度聚类算法的案事件分析应用[J]. 地球信息科学学报,2015,(07):837-845.
[6] 谢文亮. 移动互联网时代学术期刊的微信公众号服务模式创新[J]. 中国科技期刊研究,2015,(01):65-72.
[7] 邱粲,曹洁,林隆超,等. 基于GIS的山东省旅游气候舒适度精细化评估[J]. 资源科学,2013,(12):2501-2506.
[8] 丰江帆,熊雨虹. 一种基于个人位置信息的重要地点识别方法[J]. 小型微型计算机系统,2013,(03):503-507.
[9] 李霞,蒋盛益,张倩生,等. 适用于大规模文本处理的动态密度聚类算法[J]. 北京大学学报(自然科学版),2013,(01):133-139.
[10] 马丽君,孙根年,马耀峰,等. 气候舒适度对热点城市入境游客时空变化的影响[J]. 旅游学刊,2011,(01):45-50.
[11] 马丽君,孙根年,王洁洁. 中国东部沿海沿边城市旅游气候舒适度评价[J]. 地理科学进展,2009,(05):713-722.
[12] 韩毅,张克菊,金碧辉. 集成概念空间与潜在语义索引的文本聚类检索研究[J]. 情报理论与实践,2009,(06):102-105.