“Violent crime fell 3 percent in Philadelphia in 2010” – this title from the Philadelphia Inquirer depicts Philadelphia’s reported decline in crime in the late 2000s and 2010s. However, is this claim exactly what it appears to be? In their paper, “Crime in Philadelphia: Bayesian Clustering and Particle Optimization,” Balocchi, Deshpande, George, and Jensen use Bayesian hierarchical modeling and clustering to identify more nuanced patterns in temporal trends and baseline levels of crime in Philadelphia.
A statistical toolbox in some ways is like an endless buffet. There are tons of statistical methods out there, ranging from linear models to statistical tests to neural networks. In addition, with increasing amounts of data, new applications from other fields, and increased computational power, methods are constantly being created or improved upon. Having so many possibilities, of course, has its perks. But researchers inevitably must face this daunting question: what method do you choose and why?