Regional aspects of Miami crime fiction

This thesis argues that forces of literary regionalism and postmodern culture are behind the explosion of crime fiction being written in and about South Florida by a growing number of resident authors. ^ Research included four methods of investigation: (1) A critical reading of many of the novels that make up the sub-genre. (2) A study of the theories of regionalism, postmodernism and the genre of the crime fiction. (3) Interviews with a number of the authors and a prominent Miami book seller. (4) Sociological studies of Miami in terms of historical events and their cultural significance. ^ Today's South Florida crime fiction authors cast their narratives in the old genre of the detective novel where characters are delineated according to traditional definitions of good and evil. What makes South Florida crime fiction different from traditional detective fiction is its interest in the exotic, postmodern culture and setting of South Florida. There is a unique cultural diversity of the city due to the geographical location of Miami in relationship to Latin America and the Caribbean, and the political forces at work in the region. South Florida's sub-tropical climate, fragile ecosystem, and elements of frontier life in a cosmopolitan city work to support Miami crime fiction. ^

Perceptions of French and Creole Among First-Generation Adult Haitian English Language Learners

Context: Due to a unique combination of factors, outdoor athletes in the Southeastern United States are at high risk of lightning deaths and injuries. Lightning detection methods are available to minimize lightning strike victims. Objective: Becoming aware of the risk factors that predispose athletes to lightning strikes and determining the most reliable detection method against hazardous weather will enable Certified Athletic Trainers to develop protocols that protect athletes from injury. Data Sources: A comprehensive literature review of Medline and Pubmed using key words: lightning, lightning risk factors, lightning safety, lightning detection, and athletic trainers and lightning was completed. Data Synthesis: Factors predisposing athletes to lighting death or injury include: time of year, time of day, the athlete’s age, geographical location, physical location, sex, perspiration level, and lack of education and preparedness by athletes and staff. Although handheld lightning detectors have become widely accessible to detect lightning strikes, their performance has not been independently or objectively confirmed. There is evidence that these detectors inaccurately detect strike locations by recording false strikes and not recording actual strikes. Conclusions: Lightning education and preparation are two factors that can be controlled. Measures need to be taken by Certified Athletic Trainers to ensure the safety of athletes during outdoor athletics. It is critical for athletic trainers and supervising staff members to become fully aware of the risks of lightning strikes in order to most effectively protect everyone under their supervision. Even though lightning detectors have been manufactured in an attempt to minimize death and injuries due to lightning strikes, none of the detectors have been proven to be 100% effective. Educating coaches, athletes, and parents on the risks of lightning and the detection methods available, while implementing an emergency action plan for lightning safety, is crucial to ensure the well being of the student-athlete population.

Regional aspects of Miami crime fiction

This thesis argues that forces of literary regionalism and postmodern culture are behind the explosion of crime fiction being written in and about South Florida by a growing number of resident authors. Research included four methods of investigation: 1. A critical reading of many of the novels that make up the sub-genre. 2. A study of the theories of regionalism, postmodernism and the genre of the crime fiction. 3. Interviews with a number of the authors and a prominent Miami book seller. 4. Sociological studies of Miami in terms of historical events and their cultural significance. Today's South Florida crime fiction authors cast their narratives in the old genre of the detective novel where characters are delineated according to traditional definitions of good and evil. Evil characters threaten established order. What makes South Florida crime fiction different from traditional detective fiction is its interest in the exotic, postmodern culture and setting of South Florida. Like the region, the villains are exotic and the order that they threaten is postmodern. There is less of an interest in attributing a larger social meaning to the heroes. Rather, there is an ontological interest in the playing out of good against evil in an almost mythical setting that magnifies economic, environmental and racial issues. There is a unique cultural diversity of the city due to the geographical location of Miami in relationship to Latin America and the Caribbean, and the political forces at work in the region. South Florida's subtropical climate, fragile ecosystem, and elements of frontier life in a cosmopolitan city work to support Miami crime fiction. The setting personifies the unpredictability and pastiche of a postmodern world and may call for a new definition for literature that relies on non-traditional regional characteristics.

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.