A5020345491- Crime Patterns and Interventions
- Second Language Acquisition and Learning
- Data-Driven Disease Surveillance
- Linguistics, Language Diversity, and Identity
- Gambling Behavior and Treatments
- Evacuation and Crowd Dynamics
- Music and Audio Processing
- Musicology and Musical Analysis
- Crime, Illicit Activities, and Governance
- Anomaly Detection Techniques and Applications
- Video Analysis and Summarization
- Human Mobility and Location-Based Analysis
- Music Technology and Sound Studies
- Translation Studies and Practices
- Wildlife Conservation and Criminology Analyses
University of Salzburg
2017-2018
Population at risk of crime varies due to the characteristics a population as well generator and attractor places where is located. This establishes different opportunities for crimes. However, there are very few efforts modeling structures that derive spatiotemporal models allow accurate assessment exposure crime. study develops depict spatial distribution people who have heightened burglaries robberies. The data used in include: Census source existing population, Twitter geo-located data,...
Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence matches has consistently uncovered differences in spatial crime patterns, particularly areas around stadia. At same time, social media data mining shows a high volume created during events. This study seeks to build these two streams by exploring relationship between events and nearby Twitter activity stadium, estimating possible tweets for explaining presence or absence...
Research shows that public events can be violence attractors or generators. Recent studies focus on the effect of crowd spatial crime analysis, using environmental criminology theories as a background. Also, social media analyses show possible influences in movements. This study displays and correlates patterns crimes tweets around two stadiums Manchester, United Kingdom. Moreover, text analysis is used for extracting violent tweets. For this data are: aggregated monthly analyzed 2km...