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 Online:2017-11-10 Published: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.

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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

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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

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