中国典型城市旅游气候舒适度及其与客流量相关性分析

发布时间：2018-06-28 12:32

本文选题：典型城市 + 气候舒适度　；参考：《陕西师范大学》2012年博士论文

【摘要】：气候与旅游是近年来国内外旅游研究的一个热点问题,国内外旅游研究者在旅游气候资源评价与开发、气候变化对旅游资源的影响、气候变化对旅游需求的影响、气候变化对目的地客流量接待及旅游流的影响等方面进行了深入的研究。旅游气候舒适度是气候与旅游研究的一个重要内容,然而相关研究多为现状的分析和评价,没有从一个较长时间尺度上考察气候舒适度的变化及其规律,且就气候论“气候”,较少将气候舒适度与客流量年内变化联系起来,探讨气候舒适度与客流量年内变化的关系。本文以典型城市为研究对象,在系统收集相关数据资料的基础上,主要完成了城市气候舒适度的现状评价及30年来城市旅游气候舒适度的变化分析、游客对气候变化的感知及其对出游行为影响的调查研究、气候舒适度与客流量、游客网络关注度时空相关关系分析、气候舒适度变化对客流量的影响分析等几个方面的研究,主要结论如下： (1)西安市居民年内气候变化感知与实际年内气候变化状况基本一致。西安市居民年内气候变化感知与出游季节偏好具有较强的一致性,气候最舒适的季节是居民最愿意出游的季节,最不舒适的季节是居民最不愿意出游的季节,说明气候舒适性是影响游客出游时间选择的重要因素。气候是影响居民旅游目的地选择的重要因素,其它条件相同的情况下,居民偏好于气候舒适的旅游目的地。气候是影响居民旅游项目选择的重要因素,不同季节居民对旅游项目的偏好不同,春季和秋季居民对不同旅游项目的偏好较为一致,有相同的变化趋势,且偏好程度相差不大,夏季和冬季居民对不同旅游项目的偏好波动较大。 (2)纬度和海拔高度是影响城市旅游气候舒适度的重要因素。城市年综合气候舒适指数随纬度的降低先升高后降低,说明城市年气候舒适度随纬度的降低先升高后降低,长江流域城市年综合气候舒适指数随海拔高度的降低呈一下降趋势,城市年气候舒适度随海拔高度的降低呈下降趋势。随着纬度的降低,1-2月和12月气候舒适度升高,3-5月和10-11月气候舒适度先升高后降低,6-9月气候舒适度降低。依据城市综合气候舒适指数的年内变化,可以将46个城市划分为倒“V”形、倒“U”形、“M”形和宽“U”形4种类型。依据城市旅游气候舒适期的年内分布,可以将46个城市划分为夏适型、冬适型、春秋适型和四季型4种类型。 (3)气候舒适度是影响游客网络关注度时空变化的重要因素。收集30个旅游城市游客网络关注度,分析其年内时空变化状况,按游客网络关注度年内变化,将30个城市划分为13种类型。在游客网络关注度月指数与气候舒适度指数比较的基础上,采用虚拟变量的回归分析方法,建立相关模型,分析气候舒适度与游客网络关注度的相关关系,长春、北京、西宁和海口游客网络关注度月指数的气候弹性系数分别为0.542、0.46、1.182和0.8,长白山、八达岭长城、周庄古镇、张家界武陵源游客网络关注度月指数的气候弹性系数分别为0.333、0.632、0.438、0.324。气候舒适度是影响游客网络关注度空间分布的重要因素,综合气候舒适指数每变化1个单位,游客网络关注度将增加(或减少)0.641万次。 (4)气候舒适度是影响客流量时空变化的重要因素。收集2005-2007年26个城市各月入境客流量,分析其年内时空变化状况,将其年内变化分为4种类型。在客流量月指数与气候舒适度指数比较的基础上,采用虚拟变量的回归分析方法,建立了4个城市入境旅游客流量月指数模拟模型,哈尔滨、大连、北京、海口入境游客月指数气候弹性系数分别为0.666、0.372、0.625、1.227。同时收集了4个城市2005-2007年各月国内客流量以及4个景区的客流量数据,对国内客流量与气候舒适度年内变化的相关关系进行了分析,北京、海口、张家界和昆明国内游客月指数气候弹性系数分别为0.1221、1.069、0.401、0.763,九寨沟、青城山、都江堰和广汉三星堆游客月指数气候弹性系数分别为2.337、0.831、0.421、0.816。利用旅游资源丰度、经济发展水平、综合气候舒适指数3个因素,建立其与入境及国内客流量地域分布的统计关系,综合气候舒适度指数每变化1个单位,入境客流量将增加(或减少)2.17万人,国内客流量将增加(或减少)30.72万人。 (5)全球气候变化使城市气候舒适度发生了改变。30年来旅游气候舒适度的变化主要受地理纬度的影响,重庆以北绝大部分城市年综合气候舒适指数上升,旅游气候舒适度升高,重庆以南绝大部分城市年综合气候舒适指数下降,旅游气候舒适度降低。纬度较高的城市温湿指数、风寒指数和综合气候舒适指数变化幅度相对较大,纬度较低的城市变化幅度相对较小。春季纬度较高的城市气候舒适度上升,纬度较低的城市气候舒适度有所下降；夏季绝大多数城市气候舒适度降低,且随纬度的降低城市气候舒适度下降幅度有所减小；秋季绝大部分城市气候舒适度下降,且随纬度的降低气候舒适度的下降幅度在增大；冬季除济南外,南昌及其以北城市气候舒适度均上升,且随纬度的降低气候舒适度上升的幅度在减小。30年来45个城市年综合气候舒适指数共上升了26.8,促进了我国旅游业的发展,但随着全球的进一步升温,年综合气候舒适指数下降的城市将进一步增多,下降的幅度将进一步增大,对旅游业的促进将逐渐转变为抑制。极端天气气候对旅游业有重大的影响,2008年雪灾对旅游业的影响,其游客损失量与客流量基数(本底值)成正比,游客损失率与2008年本底值(基数)成反比,损失量和损失率两者均与受灾程度存在一定的成正比关系。 (6)气候舒适度变化对目的地客流量接待产生了影响。利用综合气候舒适指数及40个城市的相关气候和旅游客流量数据,构建国内外旅游气候模型,分析气候舒适度变化对旅游业的影响。结果显示综合气候舒适指数每变化1个单位我国入境及国内旅游客流量将增加或减少1.852万人次和35.263万人次,重庆以北绝大部分城市年接待客流量增加,重庆以南绝大部分城市年接待客流量减少,30年来40城市国内及入境旅游客流量分别增加540.7万人次和28.4万人次。本文的主要创新点有： (1)以温湿指数、风寒指数和着衣指数为基础,采用专家打分和层次分析法确定各分指数的权重,建立了一个新的旅游气候舒适性综合评价模型,该模型不仅能直接反映客流量的年内月变化,而且还有可加和等特征。 (2)构建气候舒适度与客流量及游客网络关注度时空相关模型,揭示旅游气候舒适度弹性。传统的研究多以气候论“气候”,较少将气候舒适度与客流量年内变化联系起来,探讨气候舒适度与客流量年内变化的关系；本文系统收集城市客流量及游客网络关注度数据,分析其时空变化规律,并将其与气候舒适度时空变化规律相对比,构建气候舒适度与客流量及游客网络关注度时空相关模型,揭示旅游气候舒适度弹性。 (3)本文揭示了极端天气气候对旅游业影响的动力机制,并借助本底趋势线理论,分析了2008年雪灾对旅游业的影响,发现2008年雪灾对旅游业的影响,其游客损失量与客流量基数(本底值)成正比,游客损失率与2008年本底值(基数)成反比,损失量和损失率两者均与受灾程度存在一定的成正比关系。 (4)构建旅游气候模型,估算气候变化对旅游业的影响。国内外有关气候变化对游客旅游需求和目的地客流量接待影响的研究多以其中一种或几种气候要素为变量,通过建立相关模型定量分析气候变化对旅游业的影响,这种研究方法存在较大的缺陷,因为大多数气候要素对旅游业的影响均存在“过犹不及”的现象。本文以综合气候舒适指数为变量,构建旅游气候模型,从气候舒适度视角定量分析了气候变化对旅游业的影响。
[Abstract]:Climate and tourism are a hot issue of tourism research at home and abroad in recent years. The evaluation and development of tourism climate resources, the impact of climate change on tourism resources, the impact of climate change on tourism demand, the impact of climate change on tourist flow and the impact of tourist flow are deeply studied. The comfort degree of tourism climate is an important content of climate and tourism research. However, the related research is mostly the analysis and evaluation of the present situation. It does not examine the changes and laws of climate comfort from a long time scale, and the climate theory is "climate", and the climate comfort is associated with the changes in the annual passenger flow, and the climate comfort is discussed. In this paper, based on the collection of relevant data, this paper mainly completed the assessment of the status of urban climate comfort and the analysis of the changes in the climate comfort of urban tourism in the past 30 years, and the research on the perception of climate change and its impact on travel behavior on the basis of the system collection of relevant data. The main conclusions are as follows: the climate comfort and the passenger flow, the spatio-temporal correlation analysis of the tourist network attention, the impact analysis of the climate comfort change on the passenger flow, and so on.
(1) the climate change perception of the residents in Xi'an is basically the same as that in the actual year. The climate change perception of the residents in Xi'an has a strong consistency with the travel season preference. The most comfortable season in the climate is the most likely season for the residents to travel, and the most discomfort season is the most reluctant season for the residents to travel. Climate is an important factor affecting the choice of tourists' travel time. Climate is an important factor affecting the choice of tourist destinations. Under the same conditions, the residents prefer the comfortable tourist destinations. Climate is an important factor affecting the selection of residents' tourism projects, and the preference of residents in different seasons is different. In spring and autumn, the preference of residents in spring and autumn is more consistent, with the same trend of change, and the difference of preference degree is small. The preference of residents in summer and winter on different tourism items fluctuates greatly.
(2) latitude and altitude are the important factors that affect the comfort of urban tourism climate. The urban annual comprehensive climate comfort index rises first and then decreases with the decrease of latitude. It indicates that the annual climate comfort of the city increases first and then decreases with the decrease of latitude, and the urban annual climate comfort index of the Yangtze River Basin decreases with the decrease of altitude. The climate comfort of the city decreased with the decrease of altitude. As the latitude decreased, the climate comfort increased in 1-2 and December, the climate comfort increased first and then decreased in the 3-5 and 10-11 months, and the Climate Comfortableness decreased in 6-9 months. According to the annual change of the urban comprehensive climate comfort index, the 46 cities could be divided into "inverted" "V" shape. There are 4 types of "U" shape, "M" shape and wide "U" form. According to the annual distribution of urban tourism climate comfort period, 46 cities can be divided into 4 types of summer adaptation, winter adaptation, spring and autumn adaptation and four seasons.
(3) the climate comfort is an important factor affecting the spatial and temporal changes of tourists' network attention. To collect the network attention of 30 tourist cities, analyze the changes in time and space during the year, and divide the 30 cities into 13 types according to the changes of the visitors' network attention in the year. The basis of the comparison between the monthly index of tourist network and the climate comfort index is based on the comparison between the tourist network and the climate comfort index. On the basis of the regression analysis of virtual variables, a correlation model is established to analyze the relationship between climate comfort and tourist network attention. The climatic elasticity coefficient of the tourist network index of Changchun, Beijing, Xining and Haikou is 0.542,0.46,1.182 and 0.8, respectively, Changbai Mountain, the Badaling Great Wall, Zhouzhuang, Zhangjiajie Wulingyuan. The climate elasticity coefficient of the network concern month index (0.333,0.632,0.438,0.324.) is an important factor affecting the spatial distribution of tourists' network attention, and 1 units of the comprehensive climate comfort index will increase (or decrease) by 6 thousand and 410 times.
(4) the climate comfort is an important factor affecting the spatio-temporal change of passenger flow. Collecting and analyzing the changes in the year and space of each month in 26 cities of 2005-2007 years, and dividing the changes into 4 types. On the basis of the comparison of the monthly volume of passenger flow index and the climate comfort index, the regression analysis method of virtual variables is adopted to establish 4. Monthly index simulation model of inbound tourist flow in a city, Harbin, Dalian, Beijing and Haikou, the monthly index of the inbound tourist index is 0.666,0.372,0.625,1.227., and the domestic passenger flow of 4 cities for 2005-2007 years and the passenger flow data of 4 scenic spots are collected at the same time, and the domestic passenger flow and climate comfort are changed in the year. The correlation relationship was analyzed. The monthly index climate elasticity coefficient of Beijing, Haikou, Zhangjiajie and Kunming was 0.1221,1.069,0.401,0.763, and the monthly index of tourist index of tourists in Jiuzhaigou, Qingchengshan, Dujiangyan and Guanghan was 2.337,0.831,0.421,0.816. using tourism resources abundance, economic development level and comprehensive gas. The 3 factors of the weather comfort index set up a statistical relationship with the regional distribution of inbound and domestic passenger flow. The integrated climate comfort index will increase (or decrease) by 21 thousand and 700 people with 1 units per change, and the domestic passenger flow will increase (or decrease) by 307 thousand and 200 people.
(5) global climate change has changed the comfort degree of urban climate in.30 years. The changes in the comfort degree of tourism climate are mainly influenced by the geographical latitude. The comprehensive climate comfort index of most cities in the north of Chongqing rises, the comfort of tourism climates, and the annual comprehensive climate comfort index of Chongqing in the south of the south of Chongqing is reduced, and the tourism climate is reduced. The comfort degree is lower. The temperature and humidity index of the city with high latitude, the wind chill index and the comprehensive climate comfort index are relatively large, the cities with lower latitude are relatively small. The urban climate comfort of higher latitudes in spring is rising, the comfort degree of the cities with lower latitudes is reduced, and the climate comfort of most cities in summer is more comfortable. The decline in urban climate comfort decreased with the decrease of latitude, and the climate comfort decreased in most cities in autumn, and the decrease of climate comfort decreased with latitude. In addition to Ji'nan, the climate comfort of Nanchang and its north cities rose and the climate comfort increased with latitude. The overall climate comfort index of the 45 cities in the year of.30 has risen by 26.8, which has promoted the development of tourism in China. However, with the further warming of the world, the city will be further increased, the decline will be further increased, and the promotion of tourism will be gradually reduced to inhibition. The air climate has a significant impact on tourism. In 2008, the effect of snow disaster on tourism is proportional to the base value of passenger flow (the base value). The loss rate of tourists is inversely proportional to the base value of 2008 (the base), and both the loss and the loss rate are in direct proportion to the degree of disaster.
(6) the climate comfort changes have an impact on the destination passenger flow reception. Using the comprehensive climate comfort index and the related climate and tourist traffic data of 40 cities, the domestic and foreign tourism climate model is constructed, and the impact of climate comfort changes on tourism is analyzed. The results show that the comprehensive climate comfort index has 1 units per change in China. The flow of tourists and domestic tourists will increase or decrease by 18 thousand and 520 and 352 thousand and 630 passengers. The annual passenger flow of most cities in the north of Chongqing is increased, and the annual passenger flow in most cities in the south of Chongqing is reduced. In the 30 years, the domestic and inbound tourist flows in 40 cities have increased by 5 million 407 thousand and 284 thousand respectively.
The main innovations of this article are:
(1) on the basis of temperature and humidity index, wind cold index and clothing index, the weight of each index is determined by expert scoring and analytic hierarchy process. A new comprehensive evaluation model of tourism climate comfort is established. This model can not only directly reflect the monthly change of passenger flow, but also can be added and so on.
(2) to construct the spatio-temporal correlation model of climate comfort and passenger flow and tourist network attention, to reveal the elasticity of tourism climate comfort. Passenger traffic and tourist network attention data, analysis of its temporal and spatial variation law, and compare it with climate comfort time and space change law, build climate comfort and passenger flow and tourist network concern time and space correlation model, to reveal the flexibility of tourism climate comfort.
(3) this paper reveals the dynamic mechanism of the impact of extreme weather and climate on tourism, and analyses the impact of snow disaster on Tourism in 2008 with the help of the bottom trend line theory, and finds the effect of snow disaster on Tourism in 2008. The loss of tourists is inversely proportional to the Cheng Zheng ratio of passenger flow base (base value), the loss rate of tourists and the base value of 2008 (base number). Both loss and loss rate are directly proportional to the degree of disaster.
(4) to construct a tourist climate model to estimate the impact of climate change on tourism. The research on the impact of climate change on tourist demand and destination passenger flow reception at home and abroad is mainly based on one or several climatic factors as variables, and the quantitative analysis of the impact of climate change on tourism is made by establishing a related model. In this paper, the impact of climate change on tourism is quantitatively analyzed from the perspective of climate comfort.
【学位授予单位】：陕西师范大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：F592.7;P4;F224

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