Analyzing Effectiveness Of Gang Interventions Using Koopman Operator Theory

Koopman operator theory, applied via numerical techniques such as dynamic mode decomposition (DMD) and autoencoders, has recently emerged as an interesting mathematical framework for understanding how complex, high-dimensional dynamical systems evolve. In this paper, we apply several DMD and autoencoder algorithms to a dataset of gang involvement and activity to assess the effectiveness City of Los Angeles Mayor's Office of Gang Reduction and Youth Development's (GRYD) Intervention Family Case Management Program. We compare various subsets of the data to explore differences in sub-populations. We then control for different covariates in our analysis of dynamical changes in population characteristics over time. Statistically significant results suggest the efficacy of the GRYD FCM Program.