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This paper explores the joint optimization problem for trajectory planning and radio resource allocation in unmanned aerial vehicle (UAV) communications with the aim of maximizing data collection. Rather than decomposing the problem into subproblems, as most current approaches do, we express the quantity of data gathered by a UAV-assisted network as a function of both the size of the resource block allocated to all ground devices and their average upload rate. Based on this formula, it can be concluded that the problem of maximizing the average data collection can be reduced to minimizing the flight trajectory if each device communicates with the UAV within the maximum allowable coverage of the UAV. To address this issue, we propose an advanced hierarchical clustering algorithm that divides larger network-scale scenarios into many disjoint subregions to determine the initial hovering positions of the UAV. The non-convex minimization trajectory problem is decomposed into a series of convex optimizations to minimize path segments along the trajectory, based on the traveling salesman problem (TSP). Subsequently, the communication optimization process is modified to assign specific upload times for each device. The effectiveness of the optimization algorithm is demonstrated through extensive simulations, which show its superior performance in terms of average rates of data collection and upload failures.