Urban crime exhibits spatio - temporal clustering patterns known as “near repeat” phenomena. This study extends the spatial transmission network framework by He et al. (2020) to analyze day-night temporal variations in crime patterns. Using crime data from Morelia, Mexico, we introduce an eight-state taxonomy for characterizing spatial zones and transition matrices that capture temporal reconfigurations. We propose a temporal reconfiguration index (IR) to quantify urban crime pattern variability. Results demonstrate non-random crime distribution with zones acting as sources, sinks, or thoroughfares, with different crime types exhibiting varying temporal stability. Hierarchical clustering identifies distinct zone categories with specific temporal behaviors. This framework provides tools for understanding temporal crime dynamics and suggests the potential for comparative urban analysis.