Graffiti is an inseparable element of most large cities. It is of critical value to recognize whether it is an artistry product or a distortion sign. This study develops a larger graffiti dataset containing a variety of graffiti types and annotated boundary boxes. We use this data to obtain a robust graffiti detection model. Compared with existing methods on the task, the proposed model achieves superior results. As a case study, the created model is evaluated on a vast number of street view images to localize graffiti incidence in the city of São Paulo, Brazil. We also validated our model using the case study data, and, again, the method achieved outstanding performance. The robustness of the technique enabled further analysis of the geographical distribution of graffiti. Considering graffiti as a spatial element of the city, we investigated its relation with crime occurrences. Relatively high correlation values were obtained between graffiti and crimes against pedestrians. Finally, this work raises many questions, such as the understanding of how these relationships change across the city according to the types of graffiti.