基于网络游记的青岛市国内旅游客源市场结构与旅游流时空特征分析

doi:10.13249/j.cnki.sgs.2019.12.010

地理科学 ›› 2019, Vol. 39 ›› Issue (12): 1919-1928.doi: 10.13249/j.cnki.sgs.2019.12.010

基于网络游记的青岛市国内旅游客源市场结构与旅游流时空特征分析

王新越^1,², 曹婵婵¹()

1. 中国海洋大学管理学院,山东青岛 266100
2. 中国海洋大学海洋发展研究院,山东青岛 266100

收稿日期:2018-11-08 修回日期:2019-02-17 出版日期:2019-12-10 发布日期:2020-03-01
通讯作者: 曹婵婵 E-mail:caochanchan@163.com
作者简介:王新越（1977-）,女,黑龙江密山人,博士,副教授,主要研究方向为旅游开发与规划、区域经济。E-mail: lily6527@sina.com
基金资助:
山东省社科规划项目(19CQXJ13);文化和旅游部2019年度文化艺术和旅游研究项目资助(19DY13)

Domestic Tourist Market Structure and Spatial-temporal Characteristics of Tourism Flow in Qingdao City Based on Online Travel Notes

Wang Xinyue^1,², Cao Chanchan¹()

1. Management College, Ocean University of China, Qingdao 266100, Shandong, China
2. Ocean Development Research Institute, Ocean University of China, Qingdao, 266100, Shandong, China

Received:2018-11-08 Revised:2019-02-17 Online:2019-12-10 Published:2020-03-01
Contact: Cao Chanchan E-mail:caochanchan@163.com
Supported by:
Social Science Planning Project of Shandong Province(19CQXJ13);Ministry of Culture and Tourism 2019 Cultural Arts and Tourism Research Project(19DY13)

摘要/Abstract

摘要：

随着“互联网+”的普及和自由行的广泛参与,以网络游记为代表的旅游信息逐渐增多,为旅游客源市场结构与旅游流时空特征研究提供了可靠数据源和新的视角。从携程旅行网与马蜂窝网站采集2016年1月1日至2018年12月31日共3 a的青岛市网络游记并清洗、归纳,共得1 891篇有效样本,涉及全国28个省区市共131个城市,结合统计分析及ArcGIS空间分析方法,以地级市为研究单元深入探索青岛市国内客源市场结构与旅游流时空特征。结果表明:① 青岛市国内客源市场可按客源城市游记样本数量指标划分为核心层、次核心层、中间层、次边缘层、边缘层5个层级,“胡焕庸线”两侧差异显著。② 客源吸引半径达590.68 km,覆盖京津冀、长三角、辽、豫、皖部分地区;引力主场集中在1 500 km范围内,在接近500 km处出现客源最高峰。③ 旅游流年内变化季节差异显著,存在节假日及“峰-谷”循环的周末效应;沿海区市与内陆区市的旅游流空间分布密度极不均衡,4个季度差异显著;旅游流轨迹集中于胶州湾东西两岸,尚未形成串联各景区景点的闭合式旅游环线。

关键词: 大数据, 国内旅游客源市场, 结构特征, 旅游流, 时空特征

Abstract:

With the popularization of ‘Internet +’ and the wide participation of free travel, the tourism information represented by online travel notes has gradually increased, providing a reliable data source and a new perspective for the study of the structure of tourist source market and the temporal and spatial characteristics of tourism flow. By using the crawler tool, we collected and cleaned the online travel notes of Qingdao from Ctrip Travel website and Mafengwo Travel websit from January 1, 2016 to December 31, 2018, and got 1 891 effective samples, involving 131 cities in 28 provinces, regions and cities in China. Combined with statistical analysis and ArcGIS spatial analysis methods, use the prefecture-level city as a research unit to deeply explore the spatial and temporal characteristics of Qingdao's domestic source market structure and tourism flow. The results show that: 1) according to the sample number of online travel notes of domestic tourist source cities, the domestic passenger market in Qingdao can be divided into five levels: the core level, the sub core level, the middle level, the sub edge level and the edge level. There are significant differences between the two sides of 'Hu Line'. 2) the attraction radius of tourist source reaches 590.68 km, covering parts of Beijing, Tianjin, Hebei, Yangtze River Delta, Liaoning, Henan and Anhui provinces; the main attraction is concentrated within 1500 km, and the highest peak of passenger flow appear around 500 km. 3) There are significant seasonal differences in tourism flows during the year, with weekends and holidays effects; the spatial distribution density of tourism flows in coastal and inland areas is extremely uneven, with significant differences in four quarters; the trajectory of tourism flows is concentrated on both sides of the east and west of Jiaozhou Bay, and closed tourist routes connecting scenic spots in series have not yet been formed.

Key words: big data, domestic tourism market, structure characteristics, tourism flow, spatial and temporal characteristics

中图分类号:

王新越, 曹婵婵. 基于网络游记的青岛市国内旅游客源市场结构与旅游流时空特征分析[J]. 地理科学, 2019, 39(12): 1919-1928.

Wang Xinyue, Cao Chanchan. Domestic Tourist Market Structure and Spatial-temporal Characteristics of Tourism Flow in Qingdao City Based on Online Travel Notes[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(12): 1919-1928.

图/表 6

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样本数量（个）	客源地	样本数量（个）	客源地
433	北京	22	长沙
188	上海	21	重庆
142	杭州	19	福州、哈尔滨
98	天津	18	南昌、徐州
70	南京	15	深圳、长春
69	宁波	10	嘉兴、厦门
67	沈阳	9	常州、东莞
60	广州	8	南通、台州
56	武汉	7	湖州、连云港、唐山
42	成都	6	贵阳
38	大连	5	保定、大庆、金华、扬州
36	西安、郑州	4	沧州、邯郸、呼和浩特、兰州、廊坊、丽水、洛阳、衢州、舟山
35	温州	3	蚌埠、佳木斯、江门、昆明、泉州、商丘、铁岭、咸阳、盐城、阳泉
25	石家庄	2	包头、丹东、佛山、呼伦贝尔、淮安、淮北、揭阳、绵阳、南宁、绍兴、泰州、芜湖、忻州、新乡、宜春、银川、张家口
24	苏州、太原	1	安庆、安阳、鞍山、白城、宝鸡、本溪、亳州、大理白族自治州、大同、大兴安岭地区、德阳、鄂尔多斯、抚顺、阜阳、赣州、广元、桂林、葫芦岛、黄山、黄石、惠州、焦作、金昌、锦州、晋中、九江、开封、辽阳、临汾、牡丹江、平顶山、普洱、齐齐哈尔、清远、汕头、朔州、四平、铜陵、乌鲁木齐、西宁、湘潭、襄阳、孝感、邢台、许昌、宣城、宜昌、榆林、岳阳、运城、湛江、肇庆、中山、周口
23	合肥、无锡	1

基于网络游记的青岛市国内旅游客源市场结构与旅游流时空特征分析

Domestic Tourist Market Structure and Spatial-temporal Characteristics of Tourism Flow in Qingdao City Based on Online Travel Notes

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