This research focuses on two fundamental aspects of hot spot policing that have been widely neglected by previous scholarly research. These aspects include the adequate concentration of crime at a smaller geographical unit to be considered a crime hot spot, and the cost-benefit implication of focusing limited police resources on such a smaller place in an effort to prevent criminal activities. Substantial limitations in call-t- service data from police departments raise concern on the purported concentration of crime at places that warrant such strategy in the first place. We will examine data from the Cincinnati Police Department and propose guidelines on adopting a threshold when designating places as crime hot spots, using time and cost-benefit analysis as key determinants.
This paper looks at the opportunities and challenges of implementing blockchain technology across the medical sector and provides a clear view which can enable blockchain for more extents. After a notable research on underlying blockchain technology which offers distributed governance, immutable audit trail, provenance of data, robustness and privacy, we contrasted blockchain innovations and identified prominent applications of it in historically decentralized healthcare sectors. As the healthcare industry faces many challenges like unauthorised data sharing, lack of data transparency, ransomware, data breaches and cyber crimes, blockchain is one of the best ways to enhance data sharing and to mitigate prominent cyber crimes. By proper designing of a decentralized and immutable blockchain network where the data is dispersed among credentialed social insurance experts guarantees that cybercriminals cannot touch single patient’s confidential data, which facilitates encryption or cryptography of personal data where no patient’s emergency data is at extreme hazard. Blockchain trust-worthy cloud is one of the most powerful and secure ways of storing high confidential data. After analysing Blockchain implementations and identifying its potential in healthcare, we conclude with several promising directions for future research.
Shooting crime is a serious public health problem in the US. The analysis of any historical crime data reveals that crime is non-randomly distributed in time and space. Based on this notion, hot spots policing has gained its momentum to effectively predict future crime locations. Recent studies; however, pointed out that traditional hot spots policing occasionally predict rare crimes such as homicides and shootings due to their less frequent recurring counts in a given place and time (specifically for shorter time periods such as weeks and months). Given this context, we developed a new shooting prediction system (SHOPS) to explore whether recent dynamic/mobility activity patterns of known violent individuals increase the prediction of short-term fatal and non-fatal shootings compared to the traditional hot spots policing. Findings suggest that SHOPS predicts fatal and non-fatal shooting locations more precisely by identifying fewer hotspot locations. Policy implications of the study were discussed in the conclusion section.