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In 2015, the Bureau of Justice Assistance's (BJA) selected the team of National Shooting Sports Foundation (NSSF) and APCO Worldwide to distribute firearm locks and conduct safety education activities under its Firearm Locks Distribution and Safe Storage Program (FLD/SSP). The National Institute of Justice funded the RAND Corporation to conduct an process and implementation evaluation of the FLD/SSP. NSSF titled their effort Project ChildSafe (PCS) Communities, a community-focused effort that was developed using the framework of their national Project ChildSafe initiative. The main research objectives were to monitor and document the PCS Communities program's design; assess areas of success and strengths in the implementation process, including the effectiveness of partnerships; identify challenges to implementation of the program with fidelity to the planned design.

In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes.

The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.