Spatiotemporal Crime Analysis

There has been a rise in the use of visual analytic techniques to create interactive predictive environments in a range of different applications. These tools help the user sift through massive amounts of data, presenting most useful results in a visual context and enabling the person to rapidly form proactive strategies. In this paper, we present one such visual analytic environment that uses historical crime data to predict future occurrences of crimes, both geographically and temporally. Due to the complexity of this analysis, it is necessary to find an appropriate statistical method for correlative analysis of spatiotemporal data, as well as design an interface to present these results to the user in a timely fashion. In our approach, we make use of the Dynamic Covariance Kernel Density Estimation (DCKDE) method to visualize the data in a geospatial context. The results are represented as a heat map showing the areas with a higher probability of crime. In the temporal context, a modified Seasonal Trend decomposition based on Loess (STL) is used to decompose time series signals in order to isolate trends that are used to predict the number of crime occurrences in pre-defined areas for a given time interval. These techniques were applied to Tippecanoe County to make predictions for the next time step. We evaluated the results of our prediction technique against observed data. We note that our methods are applicable to any situation where incidents may have a local spatial correlation

VAST2015 Challenge Two: Event Analysis from Communication Data

Social Media is a very good example of a large communication network. Typically, most data generated by social media are embedded with spatiotemporal stamps which hold crucial information than can help law enforcement agencies analyze the intensity of a calamity or chaos. Currently, not much research is done in designing a visual analytics system that incorporates clustering methods to analyze communication patterns. This research seeks to develop an analysis tool that represents such diverse data sets in user-friendly visual forms, to provide insights into the data that will improve the efficiency of event analysis. To analyze this data we have employed a community detection algorithm that will help us group people together who exhibit similar behavior. To visualize these clusters and the relationships between each cluster we have used a force-directed graph which will help law enforcement officials interpret communication patterns and discover suspicious ones. Each cluster in the graph is colored distinctly and a list is also provided to display the people arranged in descending order of their communication frequencies with other people in the same cluster. This visualization allows users to find the most influential people in a group/cluster. The tool designed has been used to analyze the VAST 2015 Mini-Challenge 2 Data Set in order to detect some suspicious groups of individuals. Although this tool has been currently designed to analyze the VAST 2015 datasets, it can easily be modified to visualize other data sets such as twitter or any other similar social media source

Implementation of a Speech Recognition Algorithm to Facilitate Verbal Commands for Visual Analytics Law Enforcement Toolkit

The VALET (Visual Analytics Law Enforcement Toolkit) system allows the user to visualize and predict crime hotspots and analyze crime data. Police officers have difficulty in using VALET in a mobile situation, since the system allows only conventional input interfaces (keyboard and mouse). This research focuses on introducing a new input interface to VALET in the form of speech recognition, which allows the user to interact with the software without losing functionality. First an Application Program Interface (API) that was compatible with the VALET system was found and initial code scripts to test its functionality were written. Next, the code scripts were integrated with the VALET and additional code was written to execute the commands given by the user. Lastly, more functionality was added by including a button and keywords to toggle speech recognition on/off, and a panel to display visual feedback to the user. The results from the research showed that it was easier to give simple commands by voice rather than typing them out. It helped the user with having a new way to interact with the system that was accurate but also convenient when on the move. The speech recognition was able to recognize the correct commands with a high rate of success. The implementation of the speech recognition function was able to help the police departments in interacting with the system effectively when conventional methods were not an option

Route Packing: Geospatially-Accurate Visualization of Route Networks

We present route packing}, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance

A Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal Data

With the increase in community-contributed data availability, citizens and analysts are interested in identifying patterns, trends and correlation within these datasets. Various levels of aggregation are often applied to interpret such large data schemes. Identifying the proper scales of aggregation is a non-trivial task in this exploratory data analysis process. In this paper, we present an integrated visual analytics environment that facilitates the exploration of multivariate categorical spatiotemporal data at multiple spatial scales of aggregation, focusing on citizen-contributed data. We propose a compact visual correlation representation by embedding various statistical measures across different spatial regions to enable users to explore correlations between multiple data categories across different spatial scales. The system provides several scale-sensitive spatial partitioning strategies to examine the sensitivity of correlations at varying spatial extents. To demonstrate the capabilities of our system, we provide several usage scenarios from various domains including citizen-contributed social media (soundscape ecology) data

Assisted decision making using multivariate spatiotemporal data through the application of visual analytics

The increasing availability of digital data provide both opportunities and challenges to analysts and decision makers. Data can be utilized to extract actionable information in a time constrained environment for making effective decisions. In order to better facilitate the exploration of such datasets, advanced tool sets are required that allow users to interact and provide insight into their data. In this work, we present a suite of visual analytic tools for spatiotemporal data exploration and analysis that enable analysts to make effective decisions and generate and test hypotheses pertaining to their datasets. These tools provide analysts with the ability to discover patterns and trends, allowing them to look for correlations and explore possible predictive links from among their datasets. Our research also applies a visual analytics approach for risk-based decision making scenarios in the spatiotemporal domain in order to assist users in understanding the implications of implementing different strategies. We also provide a visual analytics method for exploring correlations among multivariate datasets at different spatiotemporal scales, and present results that explore the benefits and harm of utilizing aggregate statistics. Finally, we present our proactive and predictive spatiotemporal visual analytics environment that enables decision makers to utilize their domain expertise at natural problem scales in order to help alleviate the cognitive overload caused due to the complexity of analysis process

Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective.

BACKGROUND:Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS:We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject. RESULTS:We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year. CONCLUSIONS:Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime

Visual analytics for investigative analysis of hoax distress calls using social media

A hoax distress call is a serious concern for the U.S. Coast Guard. Hoax calls not only put the Coast Guard rescue personnel in potentially dangerous situations, but also waste valuable assets that should be used for real emergency situations. However, conventional approaches do not provide enough information for investigating hoax calls and callers. As social media has played a pervasive role in the way people communicate, such data opens new opportunities and solutions to a wide range of challenges. In this paper, we present social media visual analytics solutions for supporting the investigation for hoax distress calls. We not only provide a set of comprehensive keyword collections, but also resolve the lack of social media data for the investigation. Our framework allows investigators to identify suspicious Twitter users and provide a visual analytics environment designed to examine geo-tagged tweets and Instagram messages in the context of hoax distress calls

Bristle Maps: A Multivariate Abstraction Technique for Geovisualization

Abstract—We present Bristle Maps, a novel method for the aggregation, abstraction, and stylization of spatio-temporal data that enables multi-attribute visualization, exploration, and analysis. This visualization technique supports the display of multidimensional data by providing users with a multi-parameter encoding scheme within a single visual encoding paradigm. Given a set of geographically located spatio-temporal events, we approximate the data as a continuous function using kernel density estimation. The density estimation encodes the probability that an event will occur within the space over a given temporal aggregation. These probability values, for one or more set of events, are then encoded into a bristle map. A bristle map consists of a series of straight lines that extend from, and are connected to, linear map elements such as roads, train, subway lines, etc. These lines vary in length, density, color, orientation, and transparency—creating the multivariate attribute encoding scheme where event magnitude, change, and uncertainty can be mapped as various bristle parameters. This approach increases the amount of information displayed in a single plot and allows for unique designs for various information schemes. We show the application of our bristle map encoding scheme using categorical spatio-temporal police reports. Our examples demonstrate the use of our technique for visualizing data magnitude, variable comparisons, and a variety of multivariate attribute combinations. To evaluate the effectiveness of our bristle map, we have conducted quantitative and qualitative evaluations in which we compare our bristle map to conventional geovisualization techniques. Our results show that bristle maps are competitive in completion time and accuracy of tasks with various levels of complexity