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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (11): 121-130    DOI: 10.11925/infotech.2096-3467.2020.0606
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Analyzing Evolution of City Tourism Portraits with Multi-Dimensional Features and LDA Model
Ye Guanghui(),Xu Tong,Bi Chongwu,Li Xinyue
School of Information Management, Central China Normal University, Wuhan 430079, China
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Abstract  

[Objective] This paper explores the characteristics of a city portrait’s evolution based on visitor’s cognitive data with time attributes. [Methods] First, we chose the urban tourism industry as our research subject. Then, we developed a method using the LDA model and multi-dimensional theme description framework for the city. Finally, we revealed the changing trends of the city portraits from three perspectives: the theme development process, as well as the theme evolution trends in the first and second feature dimensions. [Results] We examined our new model with Hong Kong and found its urban tourism portraits showed no significant periodic changes. However, the tourists’ perceptions on Hong Kong always had the primary and secondary dimensions. Sightseeing, transportation and entertainment were the main factors of tourists’ perceptions on Hong Kong. Specifically, sightseeing was the most important one during the entire process, entertainment was mainly in the early and late stages, and transportation was more likely at the middle stage. We also found each topic node in the evolutionary path had a stable iconic. [Limitations] We need to evaluate our method with other cities. [Conclusions] Our research will benefit urban planning and policy implementation.
Key words： City Profile      Evolution Analysis      Feature Dimension      LDA Model     
Received: 24 June 2020      Published: 04 December 2020
ZTFLH:  TP391  
Corresponding Authors: Ye Guanghui     E-mail: 3879-4081@163.com
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Guanghui Ye
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Xinyue Li

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Ye Guanghui,Xu Tong,Bi Chongwu,Li Xinyue. Analyzing Evolution of City Tourism Portraits with Multi-Dimensional Features and LDA Model. Data Analysis and Knowledge Discovery, 2020, 4(11): 121-130.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0606     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I11/121

The Research Framework of Urban Portrait Subjects in the View of the Public
作者 特征维度选取
凯文·林奇
（Kevin Lynch）[10]		 道路;边缘;区域;节点;标志物
李茜等[11] 旅游氛围（气候、价格、市民素质……）;旅游传播（旅游口号、历史文化……）;旅游基础设施（交通方式、城市环境……）;旅游资源（景点内涵、资源丰富……）;旅游服务（景点讲解、纪念品）
陆易[12] 游客基本特征（兴趣偏好、地点偏好……）;认知形象（吃、住、行……）;情感感知（资源环境、景观特色……）;总体感知（满意度、重游意愿）
马天等[13] 地域条件（天气、空气质量……）;文化（人文素质、民风……）;社会环境（市容市貌、治安条件……）;旅游吸引物（自然风光、历史遗迹）;旅游配套设施与服务（住宿、食物……）
张文等[14] 游客基本特征（旅游次数、停留天数……）;认知形象（自然与人文景点、旅游设施与服务……）;情感形象（愉快、激动）;重游意向
Gallarza等[15] 景观环境;自然状况;文化吸引;夜间娱乐;购物设施;信息指引;体育设施……
陈建明[16] 规划布局;特色建筑;生态环境;经济发展服务接待;气候条件;历史文化;通讯设施;旅游咨询;景色优美;节庆活动;旅游商品……
韦晨[17] 旅游吸引物（自然景观、风味美食……）;旅游设施与服务（市内交通、购物……）;旅游环境（价格水平、卫生状况……）
Relevant Research Around the Perception of Tourist Destinations, Urban Tourism Themes, etc.
一级指标 二级指标 特征示例词
旅游娱乐 A 特色美食 A1 甜品、茶餐厅、生蚝、牛腩
购物商场 A2 面膜、海港城、青衣城、护肤品
节事活动 A3 庆典、万圣节、舞火龙、新春
旅游游览 B 自然风光 B1 大屿山、海滨、麦理浩径、日落
人文景观 B2 渔村、教堂、博物馆、香港大学
现代城市景观 B3 中环半山扶梯、旺角、尖沙咀
主题乐园 B4 迪士尼乐园、海洋公园
旅游环境 C 气候环境 C1 小雨、天气、热、太阳
生态环境 C2 牛、清新、松叶、小溪、野猪
景区秩序 C3 排队、工作人员、服务态度
旅游服务 D 酒店 D1 住宿、半岛酒店、旅舍、民宿
旅行社 D2 旅行团、导游
旅游交通 E 本地交通 E1 天星小龙、缆车、地铁
城市对外交通 E2 飞机、高铁、航空公司、机场
城市氛围 F 城市治安 F1 社区服务、政府、交通警察
市容市貌 F2 干净、著名、繁华
市民素质 F3 居民、素质、本地人
其他 G 通行证、行程安排等 通行证、港币、流量
Description System of City Portrait
6]
">
The Process of LDA Model Generation[6]
字符 含义
Tit 时间窗口t下的主题
Tit+1 时间窗口t+1下的主题
Pk(Tit) Tit对应的主题-特征矩阵中的概率值
Pk(Tit+1) Tit+1对应的主题-特征矩阵中的概率值
The Meaning of Variables in the Topic Similarity Calculation Formula
时间窗口 游记数量 时间窗口 游记数量
2019.1 163 2019.7 233
2019.2 444 2019.8 93
2019.3 469 2019.9 77
2019.4 440 2019.10 89
2019.5 444 2019.11 40
2019.6 402 2019.12 49
The Amount of Urban Tourism Facet Data in Different Time Windows
Topic 1 概率 所属维度 Topic 2 概率 所属维度 Topic 3 概率 所属维度 Topic 4 概率 所属维度 Topic 5 概率 所属维度
香港海洋公园 0.013 B4 沙茶面 0.003 A1 油麻地 0.002 B3 大澳 0.019 B2 酒店 0.019 D1
酒店 0.005 D1 酒店 0.009 D1 旅舍 0.002 B1 挪亚方舟 0.010 B4 迪斯尼 0.010 B4
味道 0.004 A1 机场 0.008 E2 麦理浩 0.008 B1 餐厅 0.008 A1 山顶 0.008 B3
维多利亚港 0.003 B3 码头 0.008 E1 码头 0.006 E1 当代艺术中心 0.008 B4 地铁 0.008 E1
海洋公园 0.003 B4 维多利亚港 0.006 B3 装备 0.006 G 城市规划 0.007 F2 港币 0.007 G
甜品 0.003 A1 长洲岛 0.006 B1 西贡 0.004 B1 粥 0.007 A1 中环 0.007 B3
拉面 0.003 A1 旺角 0.005 B2 水库 0.004 B2 小朋友 0.007 G 机场 0.007 E2
The Dimension Division of Each Topic Feature Word
主题 旅游娱乐 旅游游览 旅游环境 旅游服务 旅游交通 城市氛围 其他
Topic 1 0.056 0.046 0.003 0.005 0.010 0.001 0.010
Topic 2 0.031 0.061 0.013 0.010 0.031 0.000 0.012
Topic 3 0.007 0.053 0.019 0.002 0.018 0.001 0.005
Topic 4 0.048 0.101 0.015 0.000 0.042 0.013 0.022
Topic 5 0.035 0.111 0.004 0.028 0.064 0.002 0.023
The Cumulative Probability of Each Topic in Different Dimensions
Pre-Theme Theme Sim Theme Next-Theme Sim
1-topic1 2-topic6 0.959 5 2-topic1 3-topic10 0.967 6
1-topic2 2-topic3 0.954 6 2-topic2 3-topic10 0.986 7
1-topic3 2-topic3 0.986 1 2-topic3 3-topic23 0.981 5
1-topic4 2-topic3 0.951 7 2-topic4 3-topic26 0.987 3
1-topic5 2-topic4 0.981 0 2-topic5 3-topic11 0.981 8
2-topic6 3-topic4 0.997 0
2-topic7 3-topic1 0.986 8
Average 0.966 5 Average 0.984 1
The Calculation of Subject Relationship (Partial)
The Type of Association Between Topics
The Core Evolution Route of Urban Tourism Portrait (Partial)
所属阶段 主题 旅游娱乐 旅游游览 旅游环境 旅游服务 旅游交通 城市氛围 其他
1 2-topic2 0.030 0.100 0.018 0.001 0.010 0.002 0.006
2 3-topic10 0.045 0.207 0.053 0.000 0.040 0.004 0.033
3-topic8 0.008 0.196 0.077 0.000 0.055 0.000 0.028
3-topic11 0.014 0.175 0.004 0.000 0.006 0.014 0.011
3-topic21 0.027 0.136 0.008 0.004 0.038 0.004 0.001
3-topic2 0.037 0.064 0.021 0.012 0.031 0.005 0.030
3-topic7 0.038 0.083 0.017 0.022 0.025 0.002 0.018
3-topic16 0.041 0.084 0.011 0.035 0.01 0.000 0.026
3 4-topic5 0.017 0.127 0.032 0.005 0.028 0.001 0.012
4-topic8 0.035 0.137 0.017 0.033 0.044 0.002 0.053
4 6-topic5 0.015 0.130 0.005 0.019 0.02 0.000 0.029
The Probability Statistics of the First Dimension of the Theme Under the First Core Theme Evolution Path
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