Aggressive behaviors are violent actions or disputes that one individual effectuates over another in which physical harm might happen and occurs in a social environment. These criminal events have negative consequences for public health and citizen's security, especially in Latin American cities. Predictive crime aims to use analytical techniques on crime databases to identify potential criminal activity. Most research focuses on other types of crime, such as homicide and crime against property. However, there is little research to describe predictive patterns for aggressive behavior at the city scale. This paper studies possible sessional patterns of aggressive behavior crime and its relationship with temporal dynamics shared across different city areas in Bogotá (Colombia), a Latin American city severely affected by this phenomenon. For this, we propose a Spatio-temporal analysis strategy based on predictability, a grounded information theory measure of sessionality, and independent component analysis. Using this approach, we studied more than three million registers reported to the city emergency line from 2014 to 2018 related to aggressive behaviors. Our results show that many city areas exhibit high sessionality values and share multiple temporal dynamics in 8 of 19 regions (localities). Notably, most of these areas present both patterns in 7 of 19 regions. Remarkably, these patterns emerged in regions that account for the 71% of aggressive behavior reports. These results agree with modern crime theories that consider Spatio-temporal dynamics, such as routine activity theory, suggesting that the citizen's routines may generate particular social dynamics which significantly influence aggressive behavior.