基于地理标记照片的城市旅游意象空间分异研究

城市旅游感知意象与投射意象的匹配程度是城市旅游形象建设与宣传效果的重要评价，景观尺度下旅游感知与投射意象的空间分异特征可以有效评估游客对城市旅游意象的感知偏差，以此把握旅游者的旅游动机与景观偏好，更好地提升旅游地形象建设。本研究以五大世界著名旅游城市为例，以Flickr网站的地理标记照片数据和政府旅游网站宣传的旅游信息为数据源，利用ArcGIS空间分析技术分析城市旅游感知意象与投射意象的要素构成和空间分异特征。结果表明，城市旅游感知与投射意象的差异既体现在要素构成的偏差上，也体现在空间分布特征的差异方面。除东京旅游感知与投射意象要素结构基本一致以外，其他城市均存在一定程度的偏差。局部空间聚集异常值可视化了五大城市旅游感知意象与投射意象的空间分布偏差，相比城市旅游投射意象的空间集聚，城市旅游感知意象的范围更为离散。同时，双变量空间自相关指数分析结果验证了五大城市旅游感知意象与投射意象均呈显著的空间正相关，但是相关程度和依赖度并不高，说明了从感知与投射视角进行旅游意象空间特征分析的重要性，应以需求为导向进行城市旅游景观规划与宣传。结合地理标记照片数据的空间分析方法为城市旅游感知意象与投射意象的空间分异研究提供了普适性的方法，以服务于城市可持续发展和旅游形象建设。

Exploring the spatial differentiation of urban tourism image based on geotagged photos

The matching relationship of urban tourism perception image and projection image is an important evaluation of urban tourism image construction and publicity effect. The spatial differentiation characteristics of tourism perception and projection image at the landscape scale can effectively assess the perception deviation of tourists on urban tourism image, so as to grasp tourists' motives and landscape preferences and enhance the image construction of tourist destinations. This article took the five world famous tourist cities as examples, selected geotagging photo data on Flickr website and tourism information promoted by government tourism websites as data sources, and adopted ArcGIS spatial analysis technology to analyze the composition and spatial differentiation of urban tourism perception and projection image. The research results show that, the differences between urban tourism perception and projected image are reflected not only in the deviation of element composition, but also in the difference in spatial distribution characteristics. Except that the structure of Tokyo tourism perception and projection image is basically the same, there are certain deviations in other cities. The local spatial aggregation outliers visualize the spatial distribution deviation of the tourism perception and projection image in the five cities. Compared with the spatial aggregation of the tourism projection image, the spatial distribution of tourism perception image is more discrete. Secondly, the analysis of the bivariate spatial autocorrelation index (Bivariate Moran's I) verifies that the tourism perception image and projection image of the five cities are significantly spatially positively correlated, but the degree of correlation and dependence is not high. The spatial characteristics of tourism images need to be analyzed from the perspective of perception and projection, and the urban tourism landscape planning and publicity should be combined with market demand. The spatial analysis technology combined with geotagged photo data can be used as a general method for the spatial differentiation of urban tourism perception and projection images, to help the city's sustainable development and tourism image building.