Crime Mapping through Geo-Spatial Social Media Activity

The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction

AN OPEN DOOR MAY TEMPT A SAINT – DATA ANALYTICS FOR SPATIAL CRIMINOLOGY

The vast amounts of data that are generated and collected in today’s world bear immense potential for businesses and authorities. Innovative companies already adopt novel analytics methods driven by competition and the urge of constantly gaining new insights into business operations, customer preferences, and strategic decision making. Nonetheless, local authorities have been slow to embrace the opportunities enabled by data analytics. In this paper, we demonstrate and discuss how latent structures unveil valuable information on an aspect of public life and communities we all face: criminal activity. On city-scale, we analyze the spatial correspondence of recorded crime to its physical environment, the public presence, and the demographical structure in its vicinity. Our results show that Big Data in fact is able to identify and quantify the main spatial drivers of criminal activity. At the same time, we are able to maintain interpretability by design, which ultimately allows deep informational insights

Taming Uncertainty in Big Data - Evidence from Social Media in Urban Areas

While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions

INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH

Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes

Cost-Aware On-Demand Resource Provisioning in Clouds

Cloud computing has been gaining increased popularity among companies for a couple of years. Low upfront costs and the enormous elasticity make it an attractive alternative to in-house IT solutions. However, most companies still tend to overprovision their IT infrastructures induced by the buffering behavior to avoid violations of service level agreements. The introduction of dynamic scaling in IT infrastructures promises enormous cost-saving potential. We identify factors of influence for cost-aware operation of an on-demand provisioned system and propose a novel provisioning model that attempts to optimally scale the cloud at any time. Following the design science paradigm we formulize the elements of our simulation framework and carry out extensive simulations under fine-grained real world settings. Compared to common operation strategies, our approach delivers superior performance results. Finally, we derive managerial implications based on the cloud customer’s preference between cost-awareness and SLA compliance

The Article 123 of Criminal law delimitation problems of Criminal law paragraph 3 of Article 125

This paper describes the software and hardware system developed by the University of Freiburg team of search and rescue robots for the RoboCup Res- cue 2010 competition. This system is an extension to the software that finished in first place the 2005 and 2006 autonomy challenge, focusing on two key areas: autonomous navigation and manipulation. Our team, consisting mainly of students, originates from the former CS Freiburg team (RoboCupSoccer), the ResQ Freiburg team (RoboCupRescue Simulation), and RescueRobots Freiburg teams ’05 and ’06

RoboCupRescue 2010 - Robot League Team RescueRobots Freiburg (Germany)

This paper describes the software and hardware system developed by the University of Freiburg team of search and rescue robots for the RoboCup Res- cue 2010 competition. This system is an extension to the software that finished in first place the 2005 and 2006 autonomy challenge, focusing on two key areas: autonomous navigation and manipulation. Our team, consisting mainly of students, originates from the former CS Freiburg team (RoboCupSoccer), the ResQ Freiburg team (RoboCupRescue Simulation), and RescueRobots Freiburg teams ’05 and ’06