# Predicting city safety perception based on visual image content

## TL;DR

This paper introduces a machine learning approach that uses image processing to identify urban environment patterns influencing citizens' safety perception, aiming to provide objective insights into a subjective topic.

## Contribution

It presents a novel method combining image processing and machine learning to predict safety perception based on visual urban environment features.

## Key findings

- High accuracy detection of urban safety-influencing patterns
- Identification of common visual cues affecting safety perception
- Potential for urban planning and safety improvement

## Abstract

Safety perception measurement has been a subject of interest in many cities of the world. This is due to its social relevance, and to its effect on some local economic activities. Even though people safety perception is a subjective topic, sometimes it is possible to find out common patterns given a restricted geographical and sociocultural context. This paper presents an approach that makes use of image processing and machine learning techniques to detect with high accuracy urban environment patterns that could affect citizen's safety perception.