# Global urban visual perception varies across demographics and personalities

arXiv: https://arxiv.org/abs/2505.12758 · HTML: https://mlsanity.com/paper/2505.12758/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 75 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Generative agents in the streets: Exploring the use of Large Language Models (LLMs) in collecting urban perceptions](https://mlsanity.com/paper/2312.13126/) (2023)
- [Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data](https://mlsanity.com/paper/2208.03078/) (2022)
- [A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images](https://mlsanity.com/paper/2311.17354/) (2023)

## Built on by (most cited first, 10 of 10)

- [It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities](https://mlsanity.com/paper/2512.17186/) (2025)
- [UrbanAlign: Post-hoc Semantic Calibration for VLM-Human Preference Alignment](https://mlsanity.com/paper/2602.19442/) (2026)
- [Culturally uneven urban perception in large language models](https://mlsanity.com/paper/2604.20048/) (2026)
- [Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications](https://mlsanity.com/paper/2601.06056/) (2025)
- [URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres](https://mlsanity.com/paper/2602.10124/) (2026)
- [How Many Visual Levers Drive Urban Perception? Interventional Counterfactuals via Multiple Localised Edits](https://mlsanity.com/paper/2604.22103/) (2026)
- [City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery](https://mlsanity.com/paper/2606.15198/) (2026)
- [Sidewalk Moments: Are Richer Representations Always More Human-Aligned? Evidence from City-Walk Videos](https://mlsanity.com/paper/2607.20903/) (2026)
- [Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning](https://mlsanity.com/paper/2608.06934/) (2026)
- [Cross-View Urban Sensing: Mapping Subjective Streetscape Perception via AlphaEarth Embeddings and Urban Context](https://mlsanity.com/paper/2608.16310/) (2026)

## Abstract (arXiv)

Understanding people's preferences is crucial for urban planning, yet current approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. We conducted a largescale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics -- including gender, age, income, education, race and ethnicity, and personality traits -- shape perceptions among 1,000 participants with balanced demographics from five countries and 45 nationalities. This dataset, Street Perception Evaluation Considering Socioeconomics (SPECS), reveals demographic- and personality-based differences across six traditional indicators -- safe, lively, wealthy, beautiful, boring, depressing -- and four new ones -- live nearby, walk, cycle, green. Location-based sentiments further shape these preferences. Machine learning models trained on existing global datasets tend to overestimate positive indicators and underestimate negative ones compared to human responses, underscoring the need for local context. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.