Assessing the effects of bus stop relocation on street robbery Abstract   A number of studies have revealed a correlation between bus stops and crimes, especially street robberies. However, few have looked into the impact of bus stop location changes on the distribution of street robberies. Will newly added bus stops attract more street robberies? Will the removal of existing bus stops reduce street robberies? By assessing the change of street robberies in relation to the spatial change of bus stops of Cincinnati, OH, with the consideration of the controls from socioeconomic characteristics, point of interests (POI) and spatial heterogeneity, this study uses beforeandafter comparisons and the differenceindifferences (DID) analysis in the context of quasiexperiment to answer these questions. This study assesses not only the influences of the relocation of bus stops, but also the influence on street robberies of the time elapsed from the addition or removal of bus stops. Besides the three typical variables representing the presence or absence of the intervention, before or after the intervention and the interaction of the two, we add the time from additionremoval to the DID analysis. Results suggest that, on average, adding bus stops to a new location significantly increases street robberies in the areas surrounding the stops. The longer the time from the addition of a new bus stop, the more the street robberies in its surrounding areas. Removing all bus stops from a location decreases street robberies in the areas nearby; however, this influence is not statistically significant. This suggests that the relationship between street robbery and time from removal may not be linear. There are multiple studies exploring the static relationship between the bus stop and street robbery, but none looked into their dynamic relationship. This study represents the first attempt to do so. Its findings add new evidence to the theories of rational choice, routine activity, crime pattern, and crime displacement.

Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions. The criminal justice system is currently illequipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses. Often due to constraints of caseload and poor record linkage, prior interactions with an individual may not be considered when an individual comes back into the system, let alone in a proactive manner through the application of diversion programs. The Los Angeles City Attorneyu0027s Office recently created a new Recidivism Reduction and Drug Diversion unit (R2D2) tasked with reducing recidivism in this population. Here we describe a collaboration with this new unit as a case study for the incorporation of predictive equity into machine learning based decision making in a resourceconstrained setting. The program seeks to improve outcomes by developing individuallytailored social service interventions (i.e., diversions, conditional plea agreements, stayed sentencing, or other favorable case disposition based on appropriate social service linkage rather than traditional sentencing methods) for individuals likely to experience subsequent interactions with the criminal justice system, a time and resourceintensive undertaking that necessitates an ability to focus resources on individuals most likely to be involved in a future case. Seeking to achieve both efficiency (through predictive accuracy) and equity (improving outcomes in traditionally underserved communities and working to mitigate existing disparities in criminal justice outcomes), we discuss the equity outcomes we seek to achieve, describe the corresponding choice of a metric for measuring predictive fairness in this context, and explore a set of options for balancing equity and efficiency when building and selecting machine learning models in an operational public policy setting.