Rapid Urban Visibility Hotspots: Quantifying Building Vertex Visibility from Connected Vehicle Trajectories using Spatial Indexing

TL;DR

This paper presents a scalable, data-driven approach to identify urban visual hotspots by analyzing connected vehicle trajectories and building data, revealing concentrated visibility zones and their statistical distribution.

Contribution

It introduces an efficient spatial indexing method using BallTree for large-scale visibility analysis from vehicle trajectories, a novel approach for urban visibility hotspot detection.

Findings

Visibility is highly concentrated in specific hotspots.

Visibility counts follow a Log-Normal distribution.

The method significantly outperforms brute-force geometric checks.

Abstract

Effective placement of Out-of-Home advertising and street furniture requires accurate identification of locations offering maximum visual exposure to target audiences, particularly vehicular traffic. 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 (sourced from Compass IoT) 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 near roadways. The analysis reveals that visibility is highly concentrated, identifying specific ``visual hotspots'' that receive disproportionately high exposure compared to average locations. 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.