A Machine Learning-Based Approach to Analyze and Visualize Time-Series Sentencing Data

Analyzing time-series sentencing data presents many challenges. The data have many dimensions and change with time. This makes it difficult to identify patterns and discuss their similarities over time. This work proposes a machine learning approach to associate patterns with clusters. This allows a representation of sentencing data regarding trajectories in the appropriate (time, cluster) space. We propose to use the Hamming distance of trajectories to measure the similarity of sentencing data across districts. For any offense, we can define the average Hamming distance that has a simple interpretation as the average period when sentencing patterns are different. We introduce simple statistical measures on trajectories to show similarities and changes in sentencing behavior over time. We illustrate our approach by analyzing sentencing data for narcotics and retail theft.