基于旅游“六要素”理论，应用最近邻指数、核密度分析、双变量空间自相关、Ripley’s K函数等空间分析方法，对2024年4月获取的天山北坡城市群旅游要素兴趣点（POI）数据的空间分布及关联特征进行了分析，并基于地理探测器探讨了其影响因素。结果表明：（1） 各旅游要素均呈显著集聚的空间分布特征，空间集聚程度由高到低依次为：“食”>“购”>“住”>“行”>“娱”>“游”。（2） 各旅游要素空间连续性较弱，呈现出“一核、一轴、多中心”的空间分布格局；县域尺度下的各旅游要素空间关联性整体较弱，“行”要素与其他要素空间关联性较强，而“游”要素与其他要素空间关联性较弱，旅游要素空间分布格局有待优化。（3） 旅游“六要素”的整体空间集聚尺度特征值为33.83 km。其中，“游”要素的空间集聚尺度特征值最大（42.95 km），而“住”要素的最小（18.48 km）。（4） 各影响因子之间的交互作用对旅游要素空间格局的影响要显著高于单一因子，研究表明其受到经济发展水平、基础设施及人口等多维因素的综合影响。其中，GDP、夜间灯光指数、A级景区数量、人口密度、第三产业比重等因子对旅游要素空间格局的影响最为显著。

This study employs the theory of the “six elements” of tourism and utilizes spatial analysis methods, including nearest neighbor index, kernel density analysis, bivariate spatial autocorrelation, and Ripley’s K-function, to examine the spatial distribution and correlation characteristics of point of interest data related to tourism elements in the urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang of China based on data collected in April 2024. In addition, we explore the influencing factors using a geographical detector. The results show the following. (1) The spatial distribution characteristics of each tourism element exhibit significant concentration, with the degree of spatial agglomeration ranking from high to low as follows: “food”>“shopping”>“accommodation”>“transportation”>“entertainment”>“tourism”. (2) Each tourism element demonstrates weak spatial continuity, resulting in a distribution pattern characterized by “one core, one axis, and multiple centers”. At the county level, the spatial correlation among tourism elements is generally weak; however, a strong correlation exists between the “transportation” element and other elements, whereas the “tourism” element exhibits weak correlations, indicating a need for optimization in the spatial distribution of tourism elements. (3) The characteristic value of the overall spatial agglomeration scale of the “six elements” of tourism is 33.83 km. Among the different elements, the “tourism” factor shows the largest spatial agglomeration scale eigenvalue (42.95 km), whereas the “accommodation” factor has the smallest (18.48 km). (4) The influence of the interaction between each factor on the spatial pattern of tourism elements is significantly greater than that of any single factor. This research highlights the effects of multi-dimensional factors, including economic development level, infrastructure, and population on the spatial pattern of tourism elements, with GDP, night light index, number of A-level scenic spots, population density, and the proportion of the tertiary industry having the most significant effects.