基于决策树的山岳景区游客路径影响因素研究——以世界遗产地三清山为例
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摘要
将决策树方法引入到小尺度旅游目的地的游客空间行为研究中,分析人口统计特征与旅行特征对游客空间行为的影响。通过问卷调查获取江西省三清山风景名胜区510位游客在景区内的路径轨迹,通过K-means方法聚类为6种游线路径,使用CHAID决策树模型构建游客属性特征与实际游览路径、主要景点的交互关系。结果表明:影响游客路径选择的因素依次为旅游方式、停留时间、主要交通方式、职业和月收入。不同游客群体寻路行为呈现规律性特征,参团游客与亲朋出游游客会倾向不同的入口,中远距离的参团游客停留时间影响其路径选择,年轻的驴友倾向于遍历更多的景点。
It is a valuable exploration of the decision tree method in small-scale tourist destinations by introducing it into the analysis of the visitor's space behavior and studying the influence of the tourist demographic characteristics and travel characteristics on the spatial behavior. In this paper,510 tourist path trajectories were obtained through questionnaire survey. The K-means method was used to cluster the tourists' trajectories into 6 kinds of tour route. Moreover,the CHAID decision tree model was used to construct the interactive relationship among 11 kinds of tourist attributes,their actual tour route types and selection of pivotal attractions. The empirical results showed that the factors affecting the choice of tourist routes are travelling ways,length of stay,transportation means,occupations,and average monthly income. There were also regularities of path finding behavior of different tourist groups: Travelers of long-distance prefer different routes depending on the days they stay; Young tourists tend to travel as many sights as possible,and choose longer distance,spend more time to visit remote sites. This study has provided new ideas for the optimization of the scenic route,the solution to the crowdedness,and the personalized planning of the tourist route.
It is a valuable exploration of the decision tree method in small-scale tourist destinations by introducing it into the analysis of the visitor's space behavior and studying the influence of the tourist demographic characteristics and travel characteristics on the spatial behavior. In this paper,510 tourist path trajectories were obtained through questionnaire survey. The K-means method was used to cluster the tourists' trajectories into 6 kinds of tour route. Moreover,the CHAID decision tree model was used to construct the interactive relationship among 11 kinds of tourist attributes,their actual tour route types and selection of pivotal attractions. The empirical results showed that the factors affecting the choice of tourist routes are travelling ways,length of stay,transportation means,occupations,and average monthly income. There were also regularities of path finding behavior of different tourist groups: Travelers of long-distance prefer different routes depending on the days they stay; Young tourists tend to travel as many sights as possible,and choose longer distance,spend more time to visit remote sites. This study has provided new ideas for the optimization of the scenic route,the solution to the crowdedness,and the personalized planning of the tourist route.
引文
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[17]刘乐乐,汪云甲,陈国良,等.基于多源遥感数据的城市变化监测研究——以郑州市为例[J].地域研究与开发,2010,29(1):136-140.
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[24]李建伟,熊鹏,张競予,等.基于女性主义的城市公园游憩行为时空分异特征研究:以西安市丰庆公园为例[J].人文地理,2011,26(5):60-64.
[25] LEE H,JOH C. Tourism Behaviour in Seoul:An Analysis of Tourism Activity Sequence Using Multidimensional Sequence Alignments[J]. Tourism Geographies,2010,12(4):487-504.
[26] SMALLWOOD C B,BECKLEY L E,MOORE S A. An Analysis of Visitor Movement Patterns Using Travel Networks in A Large Marine Park,North-western Australia[J]. Tourism Management,2012,33(3):517-528.
[27]??LTEKIN A,FABRIKANT S I,LACAYO M. Exploring the Efficiency of Users’ Visual Analytics Strategies Based on Sequence Analysis of Eye Movement Recordings[J]. International Journal of Geographical Information Science,2010,24(10):1559-1575.
[28] SHOVAL N,ISAACSON M. Tourist Mobility and Advanced Tracking Technologies[M]. New York:Routledge,2009.
[29] MALDONADO A,WACHOWICZ M,VAZQUEZ-HOEHNE A.Movement Surface:A Multilevel Approach for Predicting Visitor Movement in Nature Areas[J]. Environment and Planning B:Planning and Design,2011,38(5):864-878.
[30]郭英之,陈芸,黄剑锋,等.基于多维交互决策树模型的赴日旅游意愿研究[J].旅游学刊,2015,30(1):42-53.
[31] VALLE P O D,PINTASSILGO P,MATIAS A,et al. Tourist Attitudes towards An Accommodation Tax Earmarked for Environmental Protection:A Survey in the Algarve[J]. Tourism Management,2012,33(6):1408-1416.
[32] BYRD E T,CHARTERS S. Using Decision Trees to Identify Tourism Stakeholders:The Case of Two Eastern North Carolina Counties[J]. Tourism and Hospitality Research,2007,7(3/4):176-193.
[33] TICHTER G,GROSSMANN W,WERTHNER H. Using Data Mining in Analysing Local Tourism Patterns[M]//BUHALIS D,SCHERTLER W. Information and Communication Technologies in Tourism 1999. New York:Springer-Verlag/Wein,1999:1-10.
[34]苏醒,张捷,张宏磊,等.历史文化名城名胜组景的时空演变特征研究——以金陵四十八景为例[J].地域研究与开发,2014,33(5):171-176.
[2] SEDDIGHI H R,THEOCHAROUS A L. A Model of Tourism Destination Choice:A Theoretical and Empirical Analysis[J]. Tourism Management,2002,23(5):475-487.
[3] GOLLEDGE R G. Wayfinding Behavior:Cognitive Mapping and Other Spatial Processes[M]. Baltimore:JHU Press,1999.
[4] BATTY M,DESYLLAS J,DUXBURY E. The Discrete Dynamics of Small-scale Spatial Events:Agent-based Models of Mobility in Carnivals and Street Parades[J]. International Journal of Geographical Information Science,2003,17(7):673-697.
[5] ANTONINI G. A Discrete Choice Modeling Framework for Pedestrian Walking Behavior with Application to Human Tracking in Video Sequences[R]. Lausanne:EPFL,2005.
[6] XIA J. Modelling the Spatial-temporal Movement of Tourists[D]. Melbourne:RMIT University,2007.
[7] LEW A,Mckercher B. Modeling Tourist Movements[J].Annals of Tourism Research,2006,33(2):403-423.
[8] SHOVAL N,Isaacson M. Sequence Alignment as A Method for Human Activity Analysis in Space and Time[J].Annals of the Association of American Geographers,2007,97(2):282-297.
[9] ORELLANA D,BREGT A K,LIGTENBERG A,et al. Exploring Visitor Movement Patterns in Natural Recreational Areas[J]. Tourism Management,2012,33(3):672-682.
[10]管婧婧,俞璇.旅游者景区游线选择影响因素效用评价及管理启示[J].旅游科学,2014,28(4):29-37.
[11]李洋,吴静,熊光练. 1998—2010年我国城乡居民医疗负担及影响因素分析[J].中国卫生经济,2013,32(8):20-22.
[12]王未卿,龚晓狄,刘澄.基于决策树C5. 0的商业银行客户细分研究[J].中国管理信息化,2014,17(5):26-29.
[13]葛莹,吴野.地理作用和集聚演化:基于数据挖掘的分析[J].地理科学进展,2009,28(6):855-862.
[14]王茂军,宋国庆,许洁.基于决策树法的北京城市居民通勤距离模式挖掘[J].地理研究,2009,28(6):1516-1527.
[15]熊丽芳,甄峰,钱前,等.基于CHAID决策树方法的城市居民通勤时间影响因素分析——以南京为例[J].人文地理,2013,28(6):68-73.
[16]王伟娜,葛莹.基于决策树方法的区域可达性研究——以浙江省为例[J].测绘科学,2011,36(2):158-161.
[17]刘乐乐,汪云甲,陈国良,等.基于多源遥感数据的城市变化监测研究——以郑州市为例[J].地域研究与开发,2010,29(1):136-140.
[18] BEERLI A,MARTIN J D. Tourists’Characteristics and the Perceived Image of Tourist Destinations:A Quantitative Analysis:A Case Study of Lanzarote,Spain[J].Tourism Management,2004,25(5):623-636.
[19] BALOGLU S,MCCLEARY K W. A Model of Destination Image Formation[J]. Annals of Tourism Research,1999,26(4):868-897.
[20] KIM S S,TIMOTHY D J,Hwang J. Understanding Japanese Tourists’Shopping Preferences Using the Decision Tree Analysis Method[J]. Tourism Management,2011,32(3):544-554.
[21]雷金纳德·戈列奇,罗伯特·斯廷森著.空间行为的地理学[M].柴彦威,曹小曙,龙韬,等译.北京:商务印书馆,2013.
[22] CHANG H. Wayfinding Strategies and Tourist Anxiety in Unfamiliar Destinations[J]. Tourism Geographies,2013,15(3):529-550.
[23] SCHMITZ S. Gender Differences in Acquisition of Environmental Knowledge Related to Wayfinding Behavior,Spatial Anxiety and Self-estimated Environmental Competencies[J]. Sex Roles,1999,41(1/2):71-93.
[24]李建伟,熊鹏,张競予,等.基于女性主义的城市公园游憩行为时空分异特征研究:以西安市丰庆公园为例[J].人文地理,2011,26(5):60-64.
[25] LEE H,JOH C. Tourism Behaviour in Seoul:An Analysis of Tourism Activity Sequence Using Multidimensional Sequence Alignments[J]. Tourism Geographies,2010,12(4):487-504.
[26] SMALLWOOD C B,BECKLEY L E,MOORE S A. An Analysis of Visitor Movement Patterns Using Travel Networks in A Large Marine Park,North-western Australia[J]. Tourism Management,2012,33(3):517-528.
[27]??LTEKIN A,FABRIKANT S I,LACAYO M. Exploring the Efficiency of Users’ Visual Analytics Strategies Based on Sequence Analysis of Eye Movement Recordings[J]. International Journal of Geographical Information Science,2010,24(10):1559-1575.
[28] SHOVAL N,ISAACSON M. Tourist Mobility and Advanced Tracking Technologies[M]. New York:Routledge,2009.
[29] MALDONADO A,WACHOWICZ M,VAZQUEZ-HOEHNE A.Movement Surface:A Multilevel Approach for Predicting Visitor Movement in Nature Areas[J]. Environment and Planning B:Planning and Design,2011,38(5):864-878.
[30]郭英之,陈芸,黄剑锋,等.基于多维交互决策树模型的赴日旅游意愿研究[J].旅游学刊,2015,30(1):42-53.
[31] VALLE P O D,PINTASSILGO P,MATIAS A,et al. Tourist Attitudes towards An Accommodation Tax Earmarked for Environmental Protection:A Survey in the Algarve[J]. Tourism Management,2012,33(6):1408-1416.
[32] BYRD E T,CHARTERS S. Using Decision Trees to Identify Tourism Stakeholders:The Case of Two Eastern North Carolina Counties[J]. Tourism and Hospitality Research,2007,7(3/4):176-193.
[33] TICHTER G,GROSSMANN W,WERTHNER H. Using Data Mining in Analysing Local Tourism Patterns[M]//BUHALIS D,SCHERTLER W. Information and Communication Technologies in Tourism 1999. New York:Springer-Verlag/Wein,1999:1-10.
[34]苏醒,张捷,张宏磊,等.历史文化名城名胜组景的时空演变特征研究——以金陵四十八景为例[J].地域研究与开发,2014,33(5):171-176.