世界地理研究 ›› 2022, Vol. 31 ›› Issue (4): 837-848.DOI: 10.3969/j.issn.1004-9479.2022.04.2020454
张小青1(), 张建新1, 刘培学2(), 魏瑞兵1, 唐璐1
收稿日期:
2020-07-15
修回日期:
2020-10-11
出版日期:
2022-07-15
发布日期:
2022-07-24
通讯作者:
刘培学
作者简介:
张小青(1995-),女,硕士研究生,研究方向为旅游地理与旅游大数据,E-mail:august_cinderella@163.com。
基金资助:
Xiaoqing ZHANG1(), Jianxin ZHANG1, Peixue LIU2(), Ruibing WEI1, Lu TANG1
Received:
2020-07-15
Revised:
2020-10-11
Online:
2022-07-15
Published:
2022-07-24
Contact:
Peixue LIU
摘要:
旅游景区是旅游业发展的关键,分析边疆省域旅游网络结构,识别网络中的景区角色,可凸显景区空间功能,促进边疆地区旅游业高效发展。研究基于在线预定数据,利用模块化分析、景区角色识别模型和最优尺度回归方法,对典型边疆省域云南省内126家旅游景区进行网络结构及影响因素分析。结果表明:①云南省旅游流网络呈现6个群集模块,模块在旅游流网络中地位由其主要角色决定,目的地内景区模块化趋势明显,形成“三核心、多轴线”的模式;②旅游景区识别为核心景区、重要景区、普通景区及边缘景区4种角色, 高等级景区核心枢纽地位突出,具有显著的集聚扩散效应,重要景区与核心景区联系紧密,普通和边缘景区集中在滇东南部,联系疏散;③边疆省域内民族性强、海拔高、差异大、资源禀赋高的景区客流量大。研究结果可为云南省加强全域旅游建设、推动旅游产业布局及景区角色定位、品质升级方面提供一定参考。
张小青, 张建新, 刘培学, 魏瑞兵, 唐璐. 边疆省域旅游流的网络结构及影响因素[J]. 世界地理研究, 2022, 31(4): 837-848.
Xiaoqing ZHANG, Jianxin ZHANG, Peixue LIU, Ruibing WEI, Lu TANG. Tourist flow network structure and its influencing factors in peripheral tourism areas ————An empirical study of Yunnan Province based on online booking data[J]. World Regional Studies, 2022, 31(4): 837-848.
分类 | 变量名称 | 符号 | 变量解释 | 变量类型 |
---|---|---|---|---|
景区 属性 | 景区等级 | Level | 分为5A、4A、3A及其他 | 有序变量 |
景区类型 | Type | 分为自然、人文、民族主题及综合类 | 名义变量 | |
景区门票 | LN(Price) | 存在较多免门票的景区,加1后取对数处理 | 数值变量 | |
景区满意度 | Satisfaction | 根据携程网获取景区满意度评分 | 数值变量 | |
景区海拔高度 | LN(Height) | 景区海拔高度,取对数 | 数值变量 | |
所属区域特征 | 距市中心距离 | LN(Centro) | 景区到所属市中心城区的距离,取对数 | 数值变量 |
距最近机场距离 | LN(Airport) | 景区到最近机场的距离,取对数 | 数值变量 | |
所属城市 | City | 景区所在城市 | 名义变量 | |
城市经济发展水平 | LN(GDP) | 景区所在城市近五年GDP的均值,取对数 | 数值变量 |
表1 变量定义及描述
Tab.1 Variable definition and description
分类 | 变量名称 | 符号 | 变量解释 | 变量类型 |
---|---|---|---|---|
景区 属性 | 景区等级 | Level | 分为5A、4A、3A及其他 | 有序变量 |
景区类型 | Type | 分为自然、人文、民族主题及综合类 | 名义变量 | |
景区门票 | LN(Price) | 存在较多免门票的景区,加1后取对数处理 | 数值变量 | |
景区满意度 | Satisfaction | 根据携程网获取景区满意度评分 | 数值变量 | |
景区海拔高度 | LN(Height) | 景区海拔高度,取对数 | 数值变量 | |
所属区域特征 | 距市中心距离 | LN(Centro) | 景区到所属市中心城区的距离,取对数 | 数值变量 |
距最近机场距离 | LN(Airport) | 景区到最近机场的距离,取对数 | 数值变量 | |
所属城市 | City | 景区所在城市 | 名义变量 | |
城市经济发展水平 | LN(GDP) | 景区所在城市近五年GDP的均值,取对数 | 数值变量 |
变量 | 标准化系数 | df值 | F | 重要性 | 容差 | |
---|---|---|---|---|---|---|
转换后 | 转换前 | |||||
Level | 0.344** | 1.000 | 5.251 | 0.089 | 0.746 | 0.744 |
Type | 0.160** | 3.000 | 2.804 | 0.048 | 0.906 | 0.859 |
LN(Price) | -0.239** | 1.000 | 4.044 | -0.001 | 0.680 | 0.677 |
Satisfaction | 0.054 | 1.000 | 0.309 | 0.003 | 0.935 | 0.927 |
LN(Height) | 0.590*** | 7.000 | 8.087 | 0.225 | 0.720 | 0.851 |
LN(Centro) | -0.377* | 1.000 | 2.755 | 0.176 | 0.247 | 0.256 |
LN(Airport) | 0.249 | 1.000 | 1.052 | -0.110 | 0.222 | 0.257 |
City | 1.168*** | 5.000 | 10.008 | 0.370 | 0.250 | 0.632 |
LN(GDP) | -1.244*** | 3.000 | 6.080 | 0.198 | 0.229 | 0.694 |
表2 最优尺度回归分析结果汇总
Tab.2 The coefficients and results of test
变量 | 标准化系数 | df值 | F | 重要性 | 容差 | |
---|---|---|---|---|---|---|
转换后 | 转换前 | |||||
Level | 0.344** | 1.000 | 5.251 | 0.089 | 0.746 | 0.744 |
Type | 0.160** | 3.000 | 2.804 | 0.048 | 0.906 | 0.859 |
LN(Price) | -0.239** | 1.000 | 4.044 | -0.001 | 0.680 | 0.677 |
Satisfaction | 0.054 | 1.000 | 0.309 | 0.003 | 0.935 | 0.927 |
LN(Height) | 0.590*** | 7.000 | 8.087 | 0.225 | 0.720 | 0.851 |
LN(Centro) | -0.377* | 1.000 | 2.755 | 0.176 | 0.247 | 0.256 |
LN(Airport) | 0.249 | 1.000 | 1.052 | -0.110 | 0.222 | 0.257 |
City | 1.168*** | 5.000 | 10.008 | 0.370 | 0.250 | 0.632 |
LN(GDP) | -1.244*** | 3.000 | 6.080 | 0.198 | 0.229 | 0.694 |
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