Do Street View Imagery and Public Participation GIS align: Comparative Analysis of Urban Attractiveness

TL;DR

This study compares Street View Imagery and Public Participation GIS in Helsinki to assess how well visual data aligns with residents' perceptions of urban attractiveness, revealing partial agreement and highlighting the importance of experiential factors.

Contribution

It provides a comparative analysis of SVI and PPGIS data, demonstrating their partial alignment and emphasizing the need for integrated approaches in urban perception studies.

Findings

Agreement between SVI and PPGIS ranged from 27% to 77% depending on criteria.

Non-visual factors like noise and traffic significantly affect perception mismatches.

SVI cannot fully capture experiential aspects influencing urban attractiveness.

Abstract

As digital tools increasingly shape spatial planning practices, understanding how different data sources reflect human experiences of urban environments is essential. Street View Imagery (SVI) and Public Participation GIS (PPGIS) represent two prominent approaches for capturing place-based perceptions that can support urban planning decisions, yet their comparability remains underexplored. This study investigates the alignment between SVI-based perceived attractiveness and residents' reported experiences gathered via a city-wide PPGIS survey in Helsinki, Finland. Using participant-rated SVI data and semantic image segmentation, we trained a machine learning model to predict perceived attractiveness based on visual features. We compared these predictions to PPGIS-identified locations marked as attractive or unattractive, calculating agreement using two sets of strict and moderate criteria. Our findings reveal only partial alignment between the two datasets. While agreement (with a moderate threshold) reached 67% for attractive and 77% for unattractive places, agreement (with a strict threshold) dropped to 27% and 29%, respectively. By analysing a range of contextual variables, including noise, traffic, population presence, and land use, we found that non-visual cues significantly contributed to mismatches. The model failed to account for experiential dimensions such as activity levels and environmental stressors that shape perceptions but are not visible in images. These results suggest that while SVI offers a scalable and visual proxy for urban perception, it cannot fully substitute the experiential richness captured through PPGIS. We argue that both methods are valuable but serve different purposes; therefore, a more integrated approach is needed to holistically capture how people perceive urban environments.