Spatial-temporal evolution characteristics and influence mechanism of Xinjiang A-grade tourist attractions based on geo-detector

doi:10.7523/j.ucas.2021.0050

Abstract

Abstract: Based on the spatial and temporal perspective of geography and the data of Xinjiang A-grade tourist attractions in three time nodes (2011,2015,2019), this paper uses spatial analysis methods such as nearest neighbor index, standard deviation ellipse, and kernel density analysis to analyze the spatial-temporal evolution characteristics of tourist attractions. Geo-detector and buffer analysis are used to identify the factors affecting spatial heterogeneity and the influence mechanism. The results indicate that the number of Xinjiang A-grade tourist attractions increases significantly during the study period. Besides, the spatial distribution characteristics of A-grade tourist attractions show a cluster state with the degree of cluster from weakening to increasing. The spatial distribution of the A-grade tourist attractions shows a "NE-SW" direction, the spatial distribution center of tourist attractions moves roughly to the southwest; The cluster density and scope of A-grade tourist attractions have expanded, and the density of tourist attractions has significant regional differences. The spatial pattern has gradually shifted from "one pole and multiple points with Urumqi-Chang as the core" to "The number of tourist attractions in each region increased more evenly". The natural environment, tourism resources, socio-economic environment and policy environment all have a significant impact on the spatial-temporal evolution characteristics of Xinjiang A-grade tourist attractions. Moreover, the influence intensity of socio-economic environment and policy environment has been increasing. The effect of natural tourism resources on the spatial differentiation of scenic spots is greater than that of human tourism resources, while the impact of natural environment has weakened.

Key words: A-grade tourist attractions, spatial-temporal evolution characteristics, influence mechanism, geo-detector, Xinjiang

CLC Number:

F592.7

WANG Tian, YANG Zhaoping, HAN Fang, PAN Xumei, WANG Zhi, CHEN Xiaodong. Spatial-temporal evolution characteristics and influence mechanism of Xinjiang A-grade tourist attractions based on geo-detector[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(3): 322-332.

References

[1] 郭来喜,吴必虎,刘锋,等.中国旅游资源分类系统与类型评价[J].地理学报,2000,55(3):294-301.
[2] 卞显红.长江三角洲国家AAAA级旅游区空间结构[J].经济地理,2007,27(1):157-160.
[3] 谢志华,吴必虎.中国资源型景区旅游空间结构研究[J].地理科学,2008,28(6):748-753.
[4] 朱竑,陈晓亮.中国A级旅游景区空间分布结构研究[J].地理科学,2008,28(5):607-615.
[5] Santana-Jiménez Y, Hernández J M. Estimating the effect of overcrowding on tourist attraction:the case of Canary Islands[J]. Tourism Management,2011,32(2):415-425.
[6] Chen Y S, Wu S T. An exploration of actor-network theory and social affordance for the development of a tourist attraction:a case study of a Jimmy-related theme park, Taiwan[J]. Tourism Management, 2021, 82:104206.
[7] Alegre J, Garau J. Tourist satisfaction and dissatisfaction[J]. Annals of Tourism Research, 2010, 37(1):52-73.
[8] Nowacki M M. Quality of visitor attractions, satisfaction, benefits and behavioural intentions of visitors:verification of a model[J]. International Journal of Tourism Research, 2009, 11(3):297-309.
[9] Thompson M. Farmers' markets and tourism:Identifying tensions that arise from balancing dual roles as community events and tourist attractions[J]. Journal of Hospitality and Tourism Management, 2020, 45:1-9.
[10] Wang X, Li X, Zhen F, et al. How smart is your tourist attraction? measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach[J]. Tourism Management, 2016, 54:309-320.
[11] Han S S, Liu C M, Chen K Y, et al. A tourist attraction recommendation model fusing spatial, temporal, and visual embeddings for flickr-geotagged photos[J]. ISPRS International Journal of Geo-Information, 2021, 10(1):20.
[12] Wang T, Wu P, Ge Q S, et al. Ticket prices and revenue levels of tourist attractions in China:spatial differentiation between prefectural units[J]. Tourism Management, 2021, 83:104214.
[13] 陈楷俊,陈菁,林卓宏,等.广东省A级旅游景区空间结构及其影响因素研究[J].西南师范大学学报(自然科学版),2020,45(10):63-71.
[14] 钟林生,曾瑜皙,肖练练,等.呼伦贝尔农垦旅游资源空间特征及其发展模式[J].科学通报,2018,63(17):1740-1751.
[15] 席建超,葛全胜,成升魁,等.旅游资源群:概念特征、空间结构、开发潜力研究:以全国汉地佛教寺院旅游资源为例[J].资源科学,2004,26(1):91-98,2.
[16] 张永平,吴健生,黄秀兰,等.海峡西岸经济区旅游景区(点)空间结构分析[J].资源科学,2011,33(9):1799-1805.
[17] 李鹏,虞虎,王英杰.中国3A级以上旅游景区空间集聚特征研究[J].地理科学,2018,38(11):1883-1891.
[18] 敬峰瑞,孙虎,袁超.成都市旅游资源吸引力空间结构特征[J].资源科学,2017,39(2):303-313.
[19] 丁蕾,吴小根,马劲松,等.南京市水体旅游资源空间结构分析[J].经济地理,2015,35(12):209-215.
[20] 王洪桥,袁家冬,孟祥君.东北地区A级旅游景区空间分布特征及影响因素[J].地理科学,2017,37(6):895-903.
[21] 陆保一,张恩伟,明庆忠,等.云南省A级旅游景区空间演化特征及其驱动机制[J].山地学报,2019,37(6):879-890.
[22] 唐承财,孙孟瑶,万紫微.京津冀城市群高等级景区分布特征及影响因素[J].经济地理,2019,39(10):204-213.
[23] 李武陵,袁小玉.丝绸之路新疆段人文旅游资源景观空间研究[J].干旱区资源与环境,2018,32(9):203-208.
[24] 张佳运,高敏华,刘海军.新疆A级旅游景区空间结构演变分析[J].水土保持研究,2016,23(4):138-142.
[25] 乔家君,朱乾坤,辛向阳.黄河流域农区贫困特征及其影响因素[J].资源科学,2020,42(1):184-196.
[26] Clark P J, Evans F C. Distance to nearest neighbor as a measure of spatial relationships in populations[J]. Ecology, 1954, 35(4):445-453.
[27] 张正峰,张阿曼.中国县域乡村发展类型及时空变化[J].资源科学,2020,42(2):207-216.
[28] 方叶林,黄震方,陈文娣,等.2001-2010年安徽省县域经济空间演化[J].地理科学进展,2013,32(5):831-839.
[29] 赖长强,巫细波.基于GIS方法的特大型城市酒店空间布局特征研究:基于广州酒店POI数据分析[J].现代城市研究,2019,34(8):66-74.
[30] 张茂茂,张雪松,何炬,等.基于村域尺度的鄂北地区耕地时空变化特征分析:以湖北省随县厉山镇为例[J].中国农业大学学报,2019,24(9):179-189.
[31] 李强,郑新奇,晁怡.大数据支持的武汉市功能识别与分布特征研究[J].测绘科学,2020,45(5):119-125.
[32] 王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
[33] 李佳洺,陆大道,徐成东,等.胡焕庸线两侧人口的空间分异性及其变化[J].地理学报,2017,72(1):148-160.
[34] 关伟,许淑婷,郭岫垚.黄河流域能源综合效率的时空演变与驱动因素[J].资源科学,2020,42(1):150-158.
[35] 杨智威,陈颖彪,吴志峰,等.粤港澳大湾区城市热岛空间格局及影响因子多元建模[J].资源科学,2019,41(6):1154-1166.
[36] Wang X G, Xi J C, Yang D Y, et al. Spatial differentiation of rural touristization and its determinants in China:a geo-detector-based case study of Yesanpo scenic area[J]. Journal of Resources and Ecology,2016,7(6):464-471.
[37] 宁志中,王婷,杨雪春.2001年以来中国旅游景区时空格局演变与景区群形成[J].地理研究,2020,39(7):1654-1666.