What question did this study set out to answer?

The study aims to analyze how perceived street safety varies across different populations and relates to street characteristics and crash incidences.

February 12, 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences0 citationsOpen Access

Modeling Perceived Street Safety from Street View Imagery: Global and Local Perspectives on Street Space Features and Crash Incidences

JMJanine A. MendozaUniversity of the Philippines Diliman KSKim Paolo L. SateraUniversity of the Philippines Diliman KVKarl Adrian P. VergaraUniversity of the Philippines Diliman

Key Points

The study aims to analyze how perceived street safety varies across different populations and relates to street characteristics and crash incidences.
Utilized Street View Imagery for analysis of street features.
Developed PSPNet-based semantic segmentation for feature extraction.
Created group-specific multiple linear regression models to predict safety perceptions.
Employed SHAPley Additive exPlanations to analyze feature influence.
Compared global perspectives using Place Pulse with local views from Quezon City.
Natural elements like trees and open spaces positively influenced perceived safety.
Features such as vehicles and dense buildings were associated with decreased safety perception.
Local models showed much higher predictive accuracy (R² up to 0.55) than global models (R² ≈ 0.12).
A perception–risk gap was observed, where locals viewed crash-prone areas as safer.
Findings highlight the need for localized, context-sensitive urban design.

Abstract

Abstract. Road safety remains a global issue, with traffic crashes causing approximately 1.19 million deaths annually. While global datasets have been used to assess safety perception, limited studies examine how local perceptions vary and relate to street features and crash incidences. This study addresses that gap by modeling perceived street safety using Street View Imagery (SVI), comparing global (Place Pulse) and local (Quezon City, Philippines) perspectives. Models were developed to analyze how different populations perceive safety in relation to street space characteristics, and how these perceptions align spatially with crash incidences. A PSPNet-based semantic segmentation model extracted 28 features from SVIs which were modeled with perceptions from diverse groups defined by gender, road user role, and geographic context. Group-specific multiple linear regression models were developed to predict safety perception, and SHAPley Additive exPlanations (SHAP) interpreted feature influence. Results revealed natural and open-space elements like trees, sky, and sidewalk increased perceived safety, while vehicles, walls, and dense buildings reduced it. Local models outperformed global (R² up to 0.55 vs. ≈ 0.12), highlighting the value of localized, group-specific modeling. However, a perception–risk gap emerged: local respondents perceived crash-prone areas as safer than non-crash zones, whereas global perception aligned better with actual crash data. These findings emphasize that familiarity and environmental normalization can shape safety perception independently of real-world risk. The study highlights the limitations of relying solely on global data and advocates for equity-oriented urban design, prioritizing safe, inclusive, and context-aware public spaces that reflect lived realities of diverse communities.

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