基于机器学习的南京市旅游地个性及其文化景观表征

doi:10.11821/dlxb201710012

地理学报 ›› 2017, Vol. 72 ›› Issue (10): 1886-1903.doi: 10.11821/dlxb201710012

基于机器学习的南京市旅游地个性及其文化景观表征

张郴^1,²(), 黄震方^1,²(), 张捷³, 葛军莲^1,²

1. 南京师范大学地理科学学院,南京 210023
2. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
3. 南京大学地理与海洋科学学院,南京 210093

收稿日期:2016-10-16 修回日期:2017-02-17 出版日期:2017-11-10 发布日期:2017-11-06
作者简介:
作者简介：张郴(1982-), 女, 江苏南京人, 博士, 讲师, 研究领域为旅游地理与旅游规划。E-mail:zhangc@njnu.edu.cn
基金资助:
国家自然科学基金项目(41301145, 41671137);国家旅游局青年专家培养计划(TYETP201526);江苏高校优势学科建设工程资助项目

Urban tourism destination personality and its cultural landscape representation via machine learning: A case study of Nanjing

Chen ZHANG^1,²(), Zhenfang HUANG^1,²(), Jie ZHANG³, Junlian GE^1,²

1. School of Geography Science, Nanjing Normal University, Nanjing 210023, China
2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3. Department of Land Resources and Tourism Sciences, Nanjing University, Nanjing 210093, China

Received:2016-10-16 Revised:2017-02-17 Published:2017-11-10 Online:2017-11-06
Supported by:
National Natural Science Foundation of China, No.41301145, No.41671137;Tourism Young Expert Training Program, No.TYETP201526;Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要：

旅游地具有物质和精神双重属性。鉴于旅游地精神属性在量化表达和测量方面的困难,以往旅游地研究较少涉及。旅游地个性概念的提出为旅游地精神属性研究打开了思路。由于开展时间不长,当前旅游地个性研究大多基于营销学中的品牌个性研究思路展开,缺乏对地方适用性的考量,且尚未深入到对个性表征形式及表征机制的探讨。基于此,从地理学视角出发,选取城市旅游地作为研究对象,着眼地方特性,系统构建城市旅游地个性量表,在此基础上,开展针对案例地城市南京的个性测量和分析,并以城市文化景观作为表征媒介,深入探究城市旅游地个性的景观表征形式及表征机制。研究发现：城市旅游地个性主要包含态度、气质、性格、能力四大特征;城市文化景观是城市旅游地个性的重要表征媒介,包含现代空间景观、传统空间景观、生态景观、生活景观、社会景观五大维度;以上景观维度及其所辖景观元素在表征城市旅游地个性方面发挥不同作用。研究过程及结论为包括旅游地在内的地方精神属性研究提供了一种概念框架和方法体系,是对精神层面人地关系认识上的丰富。机器学习这一智能化探索型数据分析手段的运用为处理地理学中普遍存在的高维非线性噪声数据提供了有力的方法支撑。

关键词: 地方精神属性, 城市旅游地个性, 文化景观表征, 机器学习, 南京市

Abstract:

Tourism destination has both material and spiritual attributes. Compared to the material attributes of place, the spiritual attributes are more abstract, which leads to the difficulty in description and measurement for scholars. This may be the main reason for the scarcity of research in this direction. Ekinci and Hosany proposed the concept of 'tourism destination personality' and used Aaker's brand personality scale to measure destination personalities that can be viewed as a pioneering work for investigating spiritual aspects of tourism destination. However, as a new direction, most research on tourism destination personality has followed the way of brand personality research, and moreover, it has not touched some in-depth questions such as the representation and its mechanism referring to tourism destination personality. In view of this, taking urban tourism destination as an example, this paper aims to design a personality scale for urban tourism destination, and to find urban tourism destination personality traits and their landscape representation mechanism. Nanjing is selected as the case in this study. A questionnaire survey was conducted in 2015, and 789 valid questionnaires were finally collected. Machine learning, as an intelligent data analysis tool, is used in this study. By analyzing the collected data, it can be concluded that: (1) Urban tourism destination personalities can be divided into four dimensions, such as 'attitude', 'glamour', 'disposition' and 'capability'. (2) Urban cultural landscapes are important in representing urban destination personalities, and can be divided into five dimensions, i.e., 'modern space landscape', 'traditional space landscape', 'ecological landscape', 'social landscape' and 'landscape for living'. (3) The effects of urban cultural landscapes in representing urban tourism personalities are quite different. This study provides a conceptual framework and method for tourism destination personality research. The findings provide new insights for human-environment interaction from the spiritual perspectives.

Key words: spiritual attribute of place, urban tourism destination personality, cultural landscape representation, machine learning, Nanjing

张郴, 黄震方, 张捷, 葛军莲. 基于机器学习的南京市旅游地个性及其文化景观表征[J]. 地理学报, 2017, 72(10): 1886-1903.

Chen ZHANG, Zhenfang HUANG, Jie ZHANG, Junlian GE. Urban tourism destination personality and its cultural landscape representation via machine learning: A case study of Nanjing[J]. Acta Geographica Sinica, 2017, 72(10): 1886-1903.

图/表 8

表1

表2

表3

表4

表5

图1

图2

表6

参考文献 50

[1]	Lefebvre H, Nicholson-Smith D. trans. The Production of Space. Oxford: Wiley-Blackwell, 1991.
[2]	Relph E.Place and Placelessness. London: Pion, 1976.
[3]	Tuan Y F.Space and Place: The Perspective of Experience.Minneapolis: University of Minnesota Press, 1977.
[4]	Ismail R.Ramadan and Bussorah street: The spirit of place. GeoJournal, 2006, 66: 243-256. doi: 10.1007/s10708-006-9027-y
[5]	Bao Lisi.Study of historic district protection and rehabilitation based on the perspective of spirit of place theory: Case study of Chengdu Broad and Narrow Alley. Ecological Economy, 2014, 30(4): 181-184.
	[鲍黎丝. 基于场所精神视角下历史街区的保护和复兴研究: 以成都宽窄巷子为例. 生态经济, 2014, 30(4): 181-184.]
[6]	Xi Yueting, Zhao Rong.Coupling strategy of cultural heritage management and recreation system based on the spirit of place. Journal of Northwest University (Natural Science Edition), 2013, 43(2): 314-318. doi: 10.3969/j.issn.1000-274X.2013.02.027
	[席岳婷, 赵荣. 场所精神下文化遗产保护与游憩体系耦合研究. 西北大学学报(自然科学版), 2013, 43(2): 314-318.] doi: 10.3969/j.issn.1000-274X.2013.02.027
[7]	Norberg-Schulz C.Genius Loci, towards a Phenomenology of Architecture. New York: Rizzoli, 1980.
[8]	Ekinci Y, Hosany S.Destination personality: An application of brand personality to tourism destinations. Journal of Travel Research, 2006, 45(2): 127-139. doi: 10.1177/0047287506291603
[9]	Aaker J L.Dimensions of brand personality. Journal of Marketing Research, 1997, 34(3): 347-356. doi: 10.2307/3151897
[10]	Murphy L, Moscardo G, Benckendorff P.Using brand personality to differentiate regional tourism destinations. Journal of Travel Research, 2007, 46(1): 5-14. doi: 10.1177/0047287507302371
[11]	Kim S, Lehto X Y.Projected and perceived destination brand personalities: The case of South Korea. Journal of Travel Research, 2013, 52(1): 117-130. doi: 10.1177/0047287512457259
[12]	Mao Duanqian, Liu Yufei, Liu Chunyan, et al.The brand personality of Jinggangshan Mountain as a tourist destination. Economic Geography, 2015, 35(7): 204-208. doi: 10.15957/j.cnki.jjdl.2015.07.030
	[毛端谦, 刘宇飞, 刘春燕, 等. 井冈山旅游品牌个性特征研究.经济地理, 2015, 35(7): 204-208.] doi: 10.15957/j.cnki.jjdl.2015.07.030
[13]	D'Astous A, Boujbel L. Positioning countries on personality dimensions: Scale development and implications for country marketing. Journal of Business Research, 2007, 60(3): 231-239. doi: 10.1016/j.jbusres.2006.11.005
[14]	Kumar V, Nayak J K.Destination personality: Scale development and validation. Journal of Hospitality & Tourism Research, 2014: 1-23. doi: 10.1177/1096348014561027
[15]	Chen C F, Phou S.A closer look at destination: Image, personality, relationship and loyalty. Tourism Management, 2013, 36(3): 269-278. doi: 10.1016/j.tourman.2012.11.015
[16]	Hultman M, Skarmeas D, Oghazi P, et al.Achieving tourist loyalty through destination personality, satisfaction, and identification. Journal of Business Research, 2015, 68(11): 2227-2231. doi: 10.1016/j.jbusres.2015.06.002
[17]	Zhang Chunhui, Bai Kai.Brand personality of rural tourism destination and tourists' loyalty: Mediating effect of place attachment. Tourism Tribune, 2011, 26(2): 49-57.
	[张春晖, 白凯. 乡村旅游地品牌个性与游客忠诚: 以场所依赖为中介变量. 旅游学刊, 2011, 26(2): 49-57.]
[18]	Papadimitriou D, Apostolopoulou A, Kaplanidou K.Destination personality, affective image, and behavioral intentions in domestic urban tourism. Journal of Travel Research, 2013, 54(3): 302-315. doi: 10.1177/0047287513516389
[19]	Servidio R.Images, affective evaluation and personality traits in tourist behaviour: An exploratory study with Italian postcards. Tourism Management Perspectives, 2015, 16(1): 237-246. doi: 10.1016/j.tmp.2015.08.003
[20]	Usakli A, Baloglu S.Brand personality of tourist destinations: An application of self-congruity theory. Tourism Management, 2011, 32(1): 114-127. doi: 10.1016/j.tourman.2010.06.006
[21]	Xie K L. Lee J S.Toward the perspective of cognitive destination image and destination personality: The case of Beijing. Journal of Travel & Tourism Marketing, 2013, 30(6): 538-556. doi: 10.1080/10548408.2013.810993
[22]	Baloglu S, Henthorne T L, Sahin S.Destination image and brand personality of Jamaica: A model of tourist behavior. Journal of Travel & Tourism Marketing, 2014, 31(8): 1057-1070. doi: 10.1080/10548408.2014.892468
[23]	Li Weiwei, Bai Kai, Zhang Chunhui.Research on brand personality and behavior intention of tourists in national geo-park in Shanxi province. Human Geography, 2014, 29(3): 143-149.
	[李薇薇, 白凯, 张春晖. 国家地质公园品牌个性对游客行为意图的影响: 以陕西翠华山国家地质公园为例. 人文地理, 2014, 29(3): 143-149.]
[24]	Apostolopoulou A, Papadimitriou D.The role of destination personality in predicting tourist behaviour: Implications for branding mid-sized destinations. Current Issues in Tourism, 2015, 18(12): 1132-1151. doi: 10.1080/13683500.2013.878319
[25]	Matzler K, Strobl A, Stokburger-Sauer N, et al.Brand personality and culture: The role of cultural differences on the impact of brand personality perceptions on tourists' visit intentions. Tourism Management, 2016, 52: 507-520. doi: 10.1016/j.tourman.2015.07.017
[26]	Pan L, Zhang M, Gursoy D, et al.Development and validation of a destination personality scale for mainland Chinese travelers. Tourism Management, 2017, 59: 338-348. doi: 10.1016/j.tourman.2016.08.005
[27]	Mitchell T. Machine Learning.New York: McGraw-Hill, 1997.
[28]	Mowen J C. Consumer Behaviour.2nd ed. New York: Macmillan, 1990.
[29]	Moon Y.Personalization and personality: Some effects of customizing message style based on consumer personality. Journal of Consumer Psychology, 2002, 12(4): 313-326. doi: 10.1016/S1057-7408(16)30083-3
[30]	Fournier S M.Consumers and their brand: Developing relationship theory in marketing. Journal of consumer Research, 1998, 24(4): 343-373. doi: 10.1086/jcr.1998.24.issue-4
[31]	Blythe J.Advertising creatives and brand personality: A grounded theory perspective. Journal of Brand Management, 2007, 14: 284-294. doi: 10.1057/palgrave.bm.2550071
[32]	Mccrae R R, Costa P T.Comparison of EPI and psychoticism scales with measures of the five-factor model of personality. Personality and Individual Differences, 1985, 6: 587-597. doi: 10.1016/0191-8869(85)90008-X
[33]	Smith S L.The tourism product. Annals of Tourism Research, 1994, 21(3): 582-595. doi: 10.1016/0160-7383(94)90121-X
[34]	Tasci A D A, Kozak M. Destination brands vs destination images: Do we know what we mean? Journal of Vacation Marketing, 2006, 12(4): 299-317. doi: 10.1177/1356766706067603
[35]	Geuens M, Weijters B, Wulf K D.A new measure of brand personality. International Journal of Research in Marketing, 2009, 26(2): 97-107. doi: 10.1016/j.ijresmar.2008.12.002
[36]	Kumar V, Nayak J K.The measurement & conceptualization of destination personality. Tourism Management Perspectives, 2014, 12: 88-93. doi: 10.1016/j.tmp.2014.09.002
[37]	Zhou Shangyi, Wu Liping, Yuan Weichao.The relation of landscape representation power and local culture succession. Human Geography, 2010, 25(5): 1-5.
	[周尚意, 吴莉萍, 苑伟超. 景观表征权力与地方文化演替的关系: 以北京前门—大栅栏商业区景观改造为例. 人文地理, 2010, 25(5): 1-5.]
[38]	Hao Yonghua.Representation: From 'Zaixian' (Representation) to 'Biaozheng' (Representation): On the theory of cultural 'Biaozheng' (Representation) of Stuart Hall. Journal of Jiangxi Normal University (Social Sciences), 2008, 41(6): 73-80.
	[郝永华. Representation: 从再现到表征: 论斯图尔特·霍尔的文化表征理论. 江西师范大学学报, 2008, 41(6): 73-80.]
[39]	Dai Guangquan, Chen Xin.The visual representation analysis of Shangxiajiu Street in Guangzhou city. Human Geography, 2010, 24(5): 148-153.
	[戴光全, 陈欣. 广州上下九的视觉表征分析: 以互联网营销图片为例. 人文地理, 2010, 24(5): 148-153.]
[40]	Liu Danping.Visual representation and social construction of place: Cultural turn in Western tourism advertising researches. Tourism Science, 2007, 21(4): 63-71.
	[刘丹萍. “地方”的视觉表征与社会建构: 西方旅游广告研究的“文化转向”思潮. 旅游科学, 2007, 21(4): 63-71.]
[41]	Sauer C O.The morphology of landscape. University of California Publications in Geography, 1925, 2: 19-54.
[42]	Tang Maolin.The inventory and progress of cultural landscape study. Progress in Geography, 2000, 19(1): 70-79. doi: 10.11820/dlkxjz.2000.01.011
	[汤茂林. 文化景观的内涵及其研究进展. 地理科学进展, 2000, 19(1): 70-79.] doi: 10.11820/dlkxjz.2000.01.011
[43]	Jin Cheng, Xu Jing, Huang Zhenfang, et al.Analyzing the characteristics of tourist flows between the scenic spots in inner city based on tourism strategies: A case study in Nanjing. Acta Geographica Sinica, 2014, 69(12): 18858-1870. doi: 10.11821/dlxb201412011
	[靳诚, 徐菁, 黄震方, 等. 南京城市内部景点间游客流动特征分析. 地理学报, 2014, 69(12):1858-1870.] doi: 10.11821/dlxb201412011
[44]	Huang Shengbing, Lu Taihong.Dimensions of brand personality in China. Nankai Business Review, 2003, 6(1): 4-9. doi: 10.3969/j.issn.1008-3448.2003.01.002
	[黄胜兵, 卢泰宏. 品牌个性维度的本土化研究. 南开管理评论, 2003, 6(1): 4-9.] doi: 10.3969/j.issn.1008-3448.2003.01.002
[45]	Liu Helin.The interpretation and exploitation of urban cultural space: Building the new road of cultural urban. Jiangsu: Southeast University Press, 2010.
	[刘合林. 城市文化空间解读与利用: 构建文化城市的新路径. 江苏: 东南大学出版社, 2010.]
[46]	Zhou Zhihua. Machine Learning.Beijing: Tsinghua University Press, 2016.
	[周志华. 机器学习.北京:清华大学出版社, 2016.]
[47]	Schatter J D, Whitley D, Eshelman L J.Combinations of genetic algorithms and neural networks: A survey of the state of the art. International Workshop on Combinations of Genetic Algorithms and Neural Networks, 1992, 47(47): 4667-4677.
[48]	Quinlan J R.C4.5: Programs for machine learning. San Mateo,CA: Morgan Kaufmann, 1993.
[49]	Lynch K.The image of the city. Cambridge, MA: The MIT Press, 1960.
[50]	Zhang Jie, Lu Shaojing, Jiang Zhijie, et al.The dimensions and characteristics of the perception of Chinese Calligraphic Landscape. Acta Geographica Sinica, 2012, 67(2): 230-238. doi: 10.11821/xb201202009
	[张捷, 卢韶婧, 蒋志杰, 等. 中国书法景观的公众地理知觉特征: 书法景观知觉维度调查. 地理学报, 2012, 67(2): 230-238.] doi: 10.11821/xb201202009

编号	个性指标	编号	个性指标	编号	个性指标	编号	个性指标
P₁	冷漠—热情	P₉	乏味—浪漫	P₁₇	阴柔—阳刚	P₂₅	贫穷—富有
P₂	油滑—敦厚	P₁₀	普通—独特	P₁₈	忧伤—快乐	P₂₆	脆弱—坚韧
P₃	虚伪—真诚	P₁₁	丑陋—美丽	P₁₉	沉闷—活泼	P₂₇	懒惰—勤奋
P₄	排外—包容	P₁₂	古典—现代	P₂₀	保守—开放	P₂₈	依赖性—独立性
P₅	华丽—朴实	P₁₃	年老—年轻	P₂₁	邋遢—整洁	P₂₉	没实力—有实力
P₆	张扬—内敛	P₁₄	本土化—西化	P₂₂	聒噪—安静	P₃₀	不可信赖—值得信赖
P₇	无知—博学	P₁₅	没品位—有品位	P₂₃	浮躁—沉稳	P₃₁	不思进取—积极进取
P₈	低俗—高雅	P₁₆	没魅力—有魅力	P₂₄	闲适—忙碌	P₃₂	无创新精神—有创新精神

编号	特征项	Y	编号	特征项	Y	编号	特征项	Y	编号	特征项	Y
P₁	热情	√	P₉	浪漫	√	P₁₇	阳刚		P₂₅	富有
P₂	敦厚	√	P₁₀	独特	√	P₁₈	快乐	√	P₂₆	坚韧	√
P₃	真诚	√	P₁₁	美丽	√	P₁₉	活泼	√	P₂₇	勤奋
P₄	包容	√	P₁₂	现代	√	P₂₀	开放	√	P₂₈	独立性
P₅	朴实	√	P₁₃	年轻	√	P₂₁	整洁		P₂₉	有实力	√
P₆	内敛	√	P₁₄	西化		P₂₂	安静		P₃₀	值得信赖	√
P₇	博学	√	P₁₅	有品味	√	P₂₃	沉稳	√	P₃₁	积极进取	√
P₈	高雅	√	P₁₆	有魅力	√	P₂₄	忙碌		P₃₂	有创新精神	√
适应度值：Y = 0.0818

编号	因子名	题项名	因子载荷				特征值	累计可解释方差率(%)	克隆巴赫 α系数
编号	因子名	题项名	1				2	3	4
A	态度	包容(A₁)	0.801	0.165	0.043	0.103	9.380	26.220	0.827
		真诚(A₂)	0.800	0.198	0.047	0.213
		朴实(A₃)	0.747	0.177	0.017	0.048
		热情(A₄)	0.716	0.272	0.079	0.127
		内敛(A₅)	0.698	0.223	0.146	0.099
		敦厚(A₆)	0.668	0.044	0.012	0.023
G	气质	美丽(G₁)	0.148	0.722	0.157	0.175	6.699	44.882	0.826
		博学(G₂)	0.338	0.651	0.047	0.130
		高雅(G₃)	0.233	0.615	0.004	0.376
		浪漫(G₄)	0.059	0.581	0.035	0.431
		独特(G₅)	0.242	0.538	0.007	0.100
		有魅力(G₆)	0.371	0.516	0.037	0.385
		有品位(G₇)	0.231	0.479	0.167	0.108
D	性格	活泼(D₁)	0.056	0.002	0.856	0.080	2.842	54.005	0.805
		快乐(D₂)	0.103	0.126	0.788	0.047
		开放(D₃)	0.172	0.009	0.655	0.095
		年轻(D₄)	0.080	0.486	0.610	0.072
		现代(D₅)	0.216	0.395	0.603	0.062
C	能力	沉稳(C₁)	0.109	0.201	0.078	0.831	2.021	61.923	0.738
		坚韧(C₂)	0.190	0.153	0.009	0.785
		值得信赖(C₃)	0.081	0.218	0.164	0.721
		有实力(C₄)	0.143	0.167	0.005	0.663
		积极进取(C₅)	0.156	0.223	0.078	0.602
		有创新精神(C₆)	0.021	0.103	0.147	0.589

序号	特征项	Y₁	Y₂	Y₃	Y₄	序号	特征项	Y₁	Y₂	Y₃	Y₄
L₁	城市主干道	√			√	L₁₆	交通站点	√	√		√
L₂	游憩步行街		√			L₁₇	城市建筑风貌	√			√
L₃	河道沿岸					L₁₈	街头雕塑、小品等			√	√
L₄	传统街巷	√	√	√	√	L₁₉	特色饮食	√			√
L₅	历史遗存区及景区	√	√	√		L₂₀	特色商品和手工艺品		√
L₆	中心商务区和游憩商务区		√	√	√	L₂₁	特色节庆活动	√	√
L₇	百年老校		√	√		L₂₂	城市媒体节目、报刊杂志等
L₈	公共文化场所	√	√		√	L₂₃	当地居民	√	√		√
L₉	公共体育场馆					L₂₄	城市卫生	√	√	√	√
L₁₀	城市广场					L₂₅	城市交通
L₁₁	城市公园及绿地				√	L₂₆	城市治安
L₁₂	山水风光	√	√		√	L₂₇	城市物价
L₁₃	林荫道		√		√	L₂₈	城市基础设施				√
L₁₄	市花市树					L₂₉	城市接待服务	√	√		√
L₁₅	标志性建筑	√	√	√	√
适应度值：Y = 0.0632

编号	因子名	题项名	因子载荷					特征值	累计可解释方差率(%)	克隆巴赫α系数
编号	因子名	题项名	1				2	3	4	5
M	现代空间景观	城市主干道(M₁)	0.760	0.137	0.193	0.224	0.071	7.866	16.733	0.780
		游憩步行街(M₂)	0.449	0.268	0.258	-0.131	0.177
		中心商务区和游憩商务区(M₃)	0.643	0.041	0.228	0.258	0.131
		公共文化场所(M₄)	0.670	0.031	0.157	-0.014	0.282
		城市建筑风貌(M₅)	0.627	0.157	0.113	0.323	0.065
		交通站点(M₆)	0.537	0.236	0.294	-0.150	0.297
		街头雕塑、小品等(M₇)	0.420	0.158	-0.181	0.043	0.099
T	传统空间景观	历史遗存区及景区(T₁)	0.127	0.571	-0.155	0.257	0.237	5.427	32.785	0.807
		传统街巷(T₂)	0.157	0.627	0.113	0.203	0.126
		百年老校(T₃)	0.120	0.675	0.371	-0.040	0.017
		标志性建筑(T₄)	0.067	0.471	0.229	0.140	-0.189
E	生态景观	山水风光(E₁)	0.127	-0.155	0.696	0.257	0.371	3.138	43.241	0.837
		林荫道(E₂)	0.061	0.287	0.664	-0.288	0.166
		城市公园及绿地(E₃)	0.141	0.062	0.589	0.249	0.147
L	生活景观	当地居民(L₁)	0.374	0.156	-0.036	0.629	0.166	2.025	52.336	0.826
		特色商品和手工艺品(L₂)	0.074	0.130	0.090	0.689	0.150
		特色饮食(L₃)	0.193	0.115	0.127	0.457	0.199
		特色节庆活动(L₄)	0.024	0.101	0.130	0.476	0.184
S	社会景观	城市卫生(S₁)	-0.042	0.094	-0.053	-0.039	0.736	1.261	60.456	0.811
		城市基础设施(S₂)	0.166	-0.129	0.030	0.407	0.714
		城市接待服务(S₃)	0.205	-0.081	0.040	0.265	0.640

基于机器学习的南京市旅游地个性及其文化景观表征

Urban tourism destination personality and its cultural landscape representation via machine learning: A case study of Nanjing

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 50

相关文章 7

Metrics

本文评价

景观维度	景观元素	态度信息增益值		气质信息增益值		性格信息增益值		能力信息增益值
景观维度	景观元素	元素	维度	元素	维度	元素	维度	元素	维度
M	M₁	0.336	0.623	0.212	0.893	0.000	0.765	1.391	0.876
	M₂	0.000		0.927		0.221		0.156
	M₃	0.246		0.302		1.520		0.973
	M₄	0.339		0.673		0.000		0.543
	M₅	0.394		0.164		0.000		1.512
	M₆	1.352		0.760		0.271		0.550
	M₇	0.000		0.000		0.837		0.362
T	T₁	0.386	0.329	1.584	0.972	0.582	0.528	0.189	0.149
	T₂	0.344		0.890		0.415		0.382
	T₃	0.000		0.949		0.519		0.245
	T₄	0.382		0.364		0.652		0.321
E	E₁	0.386	0.235	1.105	0.651	0.212	0.211	0.372	0.663
	E₂	0.173		0.785		0.000		0.731
	E₃	0.211		0.276		0.190		0.683
L	L₁	0.962	0.742	1.212	0.733	0.000	0.098	0.373	0.352
	L₂	0.266		0.627		0.000		0.279
	L₃	0.413		0.188		0.133		0.315
	L₄	0.313		1.434		0.119		0.268
S	S₁	0.613	0.569	0.393	0.392	0.332	0.092	0.887	0.841
	S₂	0.000		0.000		0.000		0.682
	S₃	0.831		0.348		0.000		1.172

[1]	刘卫东, 唐志鹏, 夏炎, 韩梦瑶, 姜宛贝. 中国碳强度关键影响因子的机器学习识别及其演进[J]. 地理学报, 2019, 74(12): 2592-2603.
[2]	乔伟峰, 刘彦随, 王亚华, 方斌, 赵酉辰. 21世纪初期南京城市用地类型与用地强度演变关系[J]. 地理学报, 2015, 70(11): 1800-1810.
[3]	靳诚, 徐菁, 黄震方, 曹芳东. 南京城市内部景点间游客流动特征分析[J]. 地理学报, 2014, 69(12): 1858-1870.
[4]	赵荣钦, 黄贤金, 彭补拙. 南京城市系统碳循环与碳平衡分析[J]. 地理学报, 2012, 67(6): 758-770.
[5]	谢顺平, 冯学智, 王结臣, 鲁伟. 基于网络加权Voronoi图分析的南京市商业中心辐射域研究[J]. 地理学报, 2009, 64(12): 1467-1476.
[6]	赵清, 郑国强, 黄巧华. 南京城市森林景观格局特征与空间结构优化[J]. 地理学报, 2007, 62(8): 870-878.
[7]	杨兴柱, 顾朝林 , 王群. 南京市旅游流网络结构构建[J]. 地理学报, 2007, 62(6): 609-620.