识别能向过往车辆交通提供最大视觉曝光度的地点，是城市分析中的核心问题，其应用涵盖城市设计、导航、基于位置的服务以及街面设施的布设。传统选址方法通常依赖静态交通流量统计或主观评估。本研究提出一种数据驱动的方法，通过分析大规模城市环境中的网联车辆轨迹数据，客观量化地点可见性。我们基于插值所得的车辆轨迹，为每个车辆位置构建前向投影的可见区域，以建模动态驾驶员视场；再结合从OpenStreetMap提取的建筑顶点位置，量化沿道路分布的数千个潜在兴趣点的累积视觉曝光量（即“可见性计数”）。本研究的核心技术贡献在于：在建筑顶点上构建BallTree空间索引，从而支持高效（时间复杂度为O(logN)）的半径查询，以判定哪些顶点落入数百万轨迹点所对应的视域圆内——该方法显著优于暴力几何检查。分析揭示两项关键发现：1）可见性高度集中，可识别出若干“视觉热点”，其曝光量远高于平均水平；2）各顶点的聚合可见性计数服从对数正态分布。

Identifying locations that offer maximum visual exposure to passing vehicular traffic is a core problem in urban analytics, with applications spanning urban design, navigation, location-based services, and the placement of street-level assets. Traditional site selection methods often rely on static traffic counts or subjective assessments. This research introduces a data-driven methodology to objectively quantify location visibility by analyzing large-scale connected vehicle trajectory data within urban environments. We model the dynamic driver field-of-view using a forward-projected visibility area for each vehicle position derived from interpolated trajectories. By integrating this with building vertex locations extracted from OpenStreetMap, we quantify the cumulative visual exposure, or ``visibility count'', for thousands of potential points of interest along roadways. The core technical contribution involves the construction of a BallTree spatial index over building vertices. This enables highly efficient (O(logN) complexity) radius queries to determine which vertices fall within the viewing circles of millions of trajectory points across numerous trips, significantly outperforming brute-force geometric checks. Analysis reveals two key findings: 1) Visibility is highly concentrated, identifying distinct 'visual hotspots' receiving disproportionately high exposure compared to average locations. 2) The aggregated visibility counts across vertices conform to a Log-Normal distribution.