The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

旅游业的空间分析提供了有关其结构的信息，这对于决策是必要的。在这项工作中，绘制了阿根廷科尔多瓦省各省2001-2014年旅游业的地图。应用有和没有空间限制的多元方法（分别是空间主成分（sPC）分析，MULTISPATI-PCA和主成分分析（PCA）），并比较了它们的性能。与PCA相比，MULTISPATI-PCA在总结旅游活动的组件上产生了更高程度的空间结构化。方法学创新在于生成多维空间数据的统计信息。根据旅游活动在旅游业增加值中的参与程度，将部门分类，使用获得的sPC作为聚类模糊k均值分析的输入。该信息提供了适当定义本地发展策略所必需的元素，因此，对于改进决策很有用。