Web-based Visual Analytics for Social Media Data

Social media data provides valuable information about different events, trends and happenings around the world. Visual data analysis tasks for social media data have large computational and storage space requirements. Due to these restrictions, subdivision of data analysis tools into several layers such as Data, Business Logic or Algorithms, and Presentation Layer is often necessary to make them accessible for variety of clients. On server side, social media data analysis algorithms can be implemented and published in the form of web services. Visual Interface can then be implemented in the form of thin clients that call these web services for data querying, exploration, and analysis tasks. In our work, we have implemented a web-based visual analytics tool for social media data analysis. Initially, we extended our existing desktop-based Twitter data analysis application named “ScatterBlog” to create web services based API that provides access to all the data analysis algorithms. In the second phase, we are creating web based visual interface consuming these web services. Some major components of the visual interface include map view, content lens view, abnormal event detection view, Tweets summary view and filtering / visual query module. The tool can then be used by parties from various fields of interest, requiring only a browser to perform social media data analysis tasks

09251 Abstracts Collection -- Scientific Visualization

From 06-14-2009 to 06-19-2009, the Dagstuhl Seminar 09251 ``Scientific Visualization \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, over 50 international participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

Classification and Visualization of Crime-Related Tweets

Millions of Twitter posts per day can provide an insight to law enforcement officials for improved situational awareness. In this paper, we propose a natural-language-processing (NLP) pipeline towards classification and visualization of crime-related tweets. The work is divided into two parts. First, we collect crime-related tweets by classification. Unlike written text, social media like Twitter includes substantial non-standard tokens or semantics. So we focus on exploring the underlying semantic features of crime-related tweets, including parts-of-speech properties and intention verbs. Then we use these features to train a classification model via Support Vector Machine. The second part is to utilize visual analytics approaches on collected tweets to analyze and explore crime incidents. We integrate the NLP pipeline with Social Media Analytics Reporting Toolkit (SMART) to improve the accuracy of crime-related tweets identification in SMART. This paper can also be utilized to improve crime prediction for law enforcement personnel

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

City-level Geolocation of Tweets for Real-time Visual Analytics

Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We provide computational evaluations to demonstrate the superiority and utility of our geolocation prediction model within an interactive system.Comment: 4 pages, 2 tables, 1 figure, SIGSPATIAL GeoAI Worksho

Visually Analyzing the Impacts of Essential Air Service Funding Decisions

Essential Air Service (EAS) is a U.S. government subsidy program which ensures maintenance of commercial airline services in small deregulated communities. The program’s budget currently is around $250 million annually, which is used as subsidy for airlines to maintain a minimal level of scheduled air service in relatively smaller airports. It is evident that 2% of the FAA budget is being spent to maintain air service in smaller communities, but there is not enough evidence to prove that all the current decisions made by Congress about EAS are advantageous. To understand these decisions, 15 years of data produced by the US Department of Transportation and Bureau of Transportation Statistics needs to be analyzed using an exploratory approach. The goal of our paper is to collect the EAS subsidy data produced by the US Department of Transportation and Bureau of Transportation Statistics and develop a multi-year and multi-location visual analytics tool which uses graphs and user-interaction to make it easier for decision makers to understand and analyze the data. We want to use this visual analytics tool to analyze the EAS funding decisions and determine its impact upon the funded airports, based on changes in factors like per passenger subsidy trends, total number of arriving and departing flights and total amount of freight being transported