Characterization of mobility patterns and collective behavior through the analytical processing of real-world complex networks

Cities are complex systems of transportation and social activity; their structure can be used to model urban street networks i.e. complex network that represents the geometry of a city allowing analytical activities for data-driven decision-making. The geometry of a city holds intrinsic information that can support activities related to the analysis of the urban scenario; of higher importance is the use of such information to enhance the quality of life of its inhabitants and/or to understand the dynamics of an urban center. Several of these analytical processes lacks in-depth methodologies to analyze crime patterns and ill-designed urban structures, which can provide for public safety and urban design. Consequently, it is our goal to provide means for the structural and topological analysis of highly criminal regions of cities represented as complex networks, and for the identification of urban planning inconsistencies that point to regions that lack access from/to points of interest in a city. In this regard, we devised a set of algebraic and algorithmic procedures that are capable of revealing patterns and provide for data comprehension. More specifically, we introduced pre-processing techniques to transform georeferenced electronic maps into graph representations of cities; we used metric-based and epidemic processes to understand the dynamics of cities in what refers to criminality; finally, we introduced a novel set of formalisms and operations based on set theory to identify design flaws concerning access in urban centers. Our results refer to approaches to preprocess and prepare maps in the form of urban street networks; to the analyses of crimes based on their spatial disposition; to the development of a model to describe criminal activities; and, to the advance of a concept based on critical problems in the urban design.