(a) Risk profile tools give time-and location-aware assessment of public safety using law enforcement data.(b) The mobile system can be used anywhere with cellular network coverage to visualize and analyze law enforcement data. Here, the system is being used as the user walks down a street. Abstract-The advent of modern smartphones and handheld devices has given analysts, decision-makers, and even the general public the ability to rapidly ingest data and translate it into actionable information on-the-go. In this paper, we explore the design and use of a mobile visual analytics toolkit for public safety data that equips law enforcement agencies and citizens with effective situation awareness and risk assessment tools. Our system provides users with a suite of interactive tools that allow them to perform analysis and detect trends, patterns and anomalies among criminal, traffic and civil (CTC) incidents. The system also provides interactive risk assessment tools that allow users to identify regions of potential high risk and determine the risk at any user-specfied location and time. Our system has been designed for the iPhone/iPad environment and is currently being used and evaluated by a consortium of law enforcement agencies. We report their use of the system and some initial feedback.

In this article, we present a visual analytics system, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.

We present a visual analytics system SemanticPrism, which aims to analyze large-scale high-dimensional datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: geo-temporal, time series curve, and pixel visualization. With each perspective, we use semantic zooming to present more detailed information.