Athens-Polis: Machine Learning in Urban Graffiti
Key Words
This project explores the correlation between street graffiti and spatial cognitive indicators in the first district of Athens by building machine learning data sets and training models, providing a new paradigm for the study of the impact of street features on urban space.
Machine Learning, Urban perception, Urban Data, Graffiti, Street View Image
Date
April 2024
Research Background
Recent urban studies increasingly use machine learning to understand how physical features influence human perception, yet the role of graffiti remains underexplored. While traditionally associated with disorder, graffiti also reflects cultural expression and local identity, particularly in cities like Athens where it is deeply embedded in the urban fabric. Building on prior work that links urban elements to perceptual indicators such as safety and liveliness, this study investigates how different types of graffiti affect spatial cognition. By analyzing over 30,000 street view images from Athens' First District, we aim to quantify the relationship between graffiti and six cognitive indices, offering new insights into the visual dynamics of urban space.
Data and Methods
Technical Pipeline
This study constructs a machine learning pipeline that processes over 30,000 Google Street View images from Athens’ First District. Graffiti types are classified into four categories using a ResNet-50 image classification model, while six perceptual indicators—such as safety, beauty, and liveliness—are predicted using models trained on the Place Pulse dataset. The classified outputs are then spatially analyzed through ANOVA, Kruskal-Wallis tests, and Geographically Weighted Regression (GWR) to examine correlations between graffiti presence and urban perception across different areas of the city.
Google Street View Images of Athens' 1st District
4 types of Graffiti
