Firearms

The recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity of machine learning (ML) processes, systems are being developed and optimized to assist personnel in highly dangerous situations. In addition to saving innocent lives, supporting the capture of the responsible criminals is part of the AI algorithm that can be hosted in acoustic gunshot detection systems (AGDSs). Although there has been some speculation that these AGDSs produce a higher false positive rate (FPR) than reported in their specifications, optimizing the dataset used for the model’s training and testing will enhance its performance.
This dissertation proposes a new gunshot-like sound database that can be incorporated into a dataset for improved training and testing of a ML gunshot detection model. Reduction of the sample bias (that is, a bias in ML caused by an incomplete database) is achievable. The Mel frequency cepstral coefficient (MFCC) feature extraction process was utilized in this research. The uniform manifold and projection (UMAP) algorithm revealed that the MFCCs of this newly created database were the closest sounds to a gunshot sound, as compared to other gunshot-like sounds reported in literature. The UMAP algorithm reinforced the outcome derived from the calculation of the distances of the centroids of various gunshot-like sounds in MFCCs’ clusters. Further research was conducted into the feature reduction aspect of the gunshot detection ML model. Reducing a feature set to a minimum, while also maintaining a high accuracy rate, is a key parameter of a highly efficient model. Therefore, it is necessary for field deployed ML applications to be computationally light weight and highly efficient. Building on the discoveries of this research can lead to the development of highly efficient gunshot detection models.

Recently, empirical attention has been directed toward understanding public opinion about gun control laws. Despite this focus, three gaps are evident in extant scholarship. First, few current examinations have relied on recently collected, national data to explore predictors of public attitudes. Second, relatively little work systematically investigates whether type of weapon bans (e.g. handgun versus semi-automatic weapon) affects public support for a given gun control initiative. Third, and importantly, the general focus in prediction support for gun control measures has been on social and demographic factors. Little is known from a theoretical perspective about how other variables - such as knowledge of Constitutinal issues or perceptions of the U.S. Supreme Court - affect public attitudes toward gun control. Using national poll data collected in 2011 by Time magazine, this study addresses these research gaps by estimating several logistic regression analyses. Research and policy implications are discussed.

Within the last several years there has been a movement, ostensible spear-headed by pro-gun lobbyists, to remove the "duty to retreat" requirement placed on individuals confronted with deadly threats. Florida first passed stand-your-ground legislation in 2005, and has since been followed by at least 12 other states. Policy advocates claim that such a legal change should decrease crime, as a crime victims will no longer be legally encumbered by the duty to retreat. This study examines the reason why states adopted such legislation and the relationship between this legislation and crime. I find that stand-your-ground legislation is associated primarily with southern states and republican governors, and that such legislation does not significantly affect either violent crime or property crime rates in large U.S. cities.

In 1987, Florida passed a Right to Carry Law, allowing citizens to purchase a concealed weapons license. Bill proponents believe that an armed citizenry will deter crime. This study examines the relationship between gun control legislation and violent crime in Florida. By using multiple regression analysis, I conclude that gun control legislation has a significant effect on homicide rates, and the presence of national economic conditions is associated with violent crime in Florida.