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

• URL: http://arxiv.org/abs/2511.05570v1
• Date: Tue, 04 Nov 2025 12:40:12 GMT
• Title: Do Street View Imagery and Public Participation GIS align: Comparative Analysis of Urban Attractiveness
• Authors: Milad Malekzadeh, Elias Willberg, Jussi Torkko, Silviya Korpilo, Kamyar Hasanzadeh, Olle Järv, Tuuli Toivonen,
• Abstract summary: Street View Imagery (SVI) and Public Participation GIS (PPGIS) represent two prominent approaches for capturing place-based perceptions.<n>This study investigates the alignment between SVI-based perceived attractiveness and residents' reported experiences gathered via a city-wide PPGIS survey in Helsinki, Finland.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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