Traditionally, urban traveling patterns have been obtained through origin-destination surveys. This method presents drawbacks such as high costs, limited representativeness of the surveyed population, and low spatial and temporal resolution of the results obtained. This study proposes deploying historical data on mobile device geolocalization to depict population displacement patterns with high spatial and temporal resolution levels. As an illustrative example, the traveling patterns were derived for a megacity in Latin America (the metropolitan area of Monterrey, Mexico) with a database of 0.7 million users being monitored during three months. The solutions formulated to tackle the challenges posed by this proposed method are described, as well as the use of the information gathered to obtain dynamic origin-destination matrices, quantify the average number of daily trips and kilometers traveled per inhabitant, attain population density per hour, and to identify the destinations attractor of most trips. We also suggest using this information to assess the impact of massive events such as concerts and sports gatherings on city mobility and air pollution.

The study of the perception of security helps to understand the feeling of citizens in the face of risk and the magnitude of its consequences, to understand the fear of crime. Traditionally the measure of this kind of subjective perception was made through surveys with low costly-effective performance, to a small sample of the population in specific time periods. To solve those inconveniences, this work analyzes take advantage of the amount of data and the real-time monitoring allowed by the social networks to quantifies the Perception of the security in Bogota. The quantification is made through different methods that involve ruled-based and supervised learning approaches for the sentiment analysis of the data coming from Spanish text from Twitter.

Aggressive behaviors are triggers of personal injuries and homicides in modern cities. Their origin is multicausal, but some phenomena, such as agglomerations and alcohol use, exacerbate these events. Understanding how the occurrence of aggressive behaviors is spatially and temporally distributed in cities will potentially allow making better governmental policies to mitigate them. Traditionally, the occurrence of aggressive behaviors in cities results from an aggregation of the reporting count of this kind of event over some range of time. Recent work suggests that this counting pattern can be obtained from a combination of independent sources of aggressive behavior occurrence. This paper explores the existence of underlying shared sources of activity in the occurrence of aggressive behaviors in the city of Bogotá, Colombia, by using source decomposition. Our results suggest that these behaviors are related to consistent and reproducible independent sources of the activity through different spatial scales in the city Bogotá.

Understanding of crime patterns is paramount important for citizen's security planning. In particular, the comprehension of the Spatio-temporal dynamics related to aggressive behaviors is fundamental for deploying policial resources and devising mitigation actions. Currently, a significant number of approaches to find patterns, characterize dynamics, and predict crime have been proposed in state of the art. However, the operation of these approaches is strongly adapted to the specific conditions of a city. In this paper, we propose a novel approach to finding spatio-temporal crime patterns in the city of Bogotá (Colombia), particularly aggressive behaviors reported to the emergency line. We characterize aggressive behaviors through rhythm and tempo based on the theory of routine activity. Finally, we show that the dynamics of aggressive behaviors in the city are shared by several spatial units in which specific strategies can be applied to mitigate it.