John Betak

Social integration and mental health in a biracial community

The study investigated the relationship between social integration and mental health, with a particular interest in social integration in a minority group context. The study involved a home interview with 33 white and 30 black women who resided in a residentially integrated working-class community in Austin, Texas. Although blacks reported significantly more psychological symptoms than whites, the race effect was explained by an underlying interaction between race and social integration. Blacks low in social integration showed more symptoms than either white respondents or blacks with a high level of integration. The Race X Social integration interaction is considered in the light of societally based prejudice and the potential social stress associated with minority group status.

Text Mining Analysis of Railroad Accident Investigation Reports

The National Transportation Safety Board in the United States and the Transportation Safety Board of Canada publish reports about major railroad accidents. The text from these accident reports were analyzed using the text mining techniques of probabilistic topic modeling and k-means clustering to identify the recurring themes in major railroad accidents. The output from these analyses indicates that the railroad accidents can be successfully grouped into different topics. The output also suggests that recurring accident types are track defects, wheel defects, grade crossing accidents, and switching accidents. A major difference between the Canadian and U.S. reports is the finding that accidents related to bridges are found to be more prominent in the Canadian reports.© 2016 ASME

Identifying Themes in Railroad Equipment Accidents Using Text Mining and Text Visualization

Developments in text mining now allow useful information to be automatically extracted from text. The Federal Railroad Administration (FRA) publishes a database of railroad equipment accidents. These accident records contain numeric data describing the accident and a text description of the accident. This paper will discuss how latent Dirichlet analysis (LDA), a text-mining algorithm, can be used to identify major recurring accident topics from the text in the FRA reports. Equipment accident reports from 2005 to 2015 were studied. This analysis identified railroad grade crossing accidents with large trucks, shoving accidents, and hump yard accidents as major topics in the accident reports. An alternative method of analyzing the text, text clustering, was also used to study the FRA data. Visualizations of the text also provide useful information about the major types of railroad accidents.

Applying Topic Modeling to Railroad Grade Crossing Accident Report Text

The FRA railroad grade crossing accident database contains text comment fields that may provide additional information about grade crossing accidents. New text mining algorithms provide the potential to automatically extract information from text that can enhance traditional numeric analyses. Topic modeling algorithms are statistical methods that analyze the words of original texts to automatically discover the themes that run through them. A frequently used topic-modeling algorithm is Latent Dirichlet Analysis (LDA). In this paper we will show several examples of how labeled LDA can be applied to the FRA grade crossing data to better understand categories of words and phrases that are associated with various types of grade crossing accidents.© 2015 ASME

A Comparison of LSA and LDA for the Analysis of Railroad Accident Text

Abstract Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation(LDA) were used to identify themes in a database of text about railroad equipment accidents maintained by the Federal Railroad Administration in the United States. These text mining techniques use different mechanisms to identify topics. LDA and LSA identified switching accidents, hump yard accidents and grade crossing accidents as major accident type topics. LSA identified accidents with track maintenance equipment as a topic. Both text mining models identified accidents with tractor-trailer highway trucks as a particular problem at grade crossings. It was found that the use of the two techniques was complementary, with more accident topics identified than with the use of a single method.

Pridit Is a Useful Technique for Detecting Consumer Fraud When No Training Sample Is Available

Marketing researchers and managers often face situations with incomplete information for decision-making. For example, when information needed for classification into strategic customer groups is lacking because of a disclosure social desirability bias. Consumers misbehaving through fraud are unlikely to self-disclose those actions. This is an increasing global problem for services and retailers. Detection of consumers misbehaving can be methodologically more difficult than studying other customer behaviors, since there may be no known observable dependent variable from surveys or observation for training standard statistical models.

Using Data Visualization to Analyze Grade Crossing Accidents

The Federal Railroad Administration grade crossing accident database contains numerous interrelated variables. Understanding of how the variables are interrelated can be enhanced using modern visualization techniques. These techniques can allow managers from railroads and government agencies to find complex variables relationships not usually provided by routine statistical analyses. For this research we have developed several dashboards of linked visualizations using the Weave data visualization software [5]. Our visualizations explore various accident types of concern to the railroad industry including trespassing and pedestrian accidents, passenger train accidents, actions of highway users involved in accidents, and the effect of different types of warning devices on grade crossing accidents. In addition, we are currently developing an advanced visualization system that views the accident data as time varying events occurring over a fixed grade crossings topology. This view allows the application of a recent network data abstraction termed “Graph Cards.” We present initial examples of the advanced system that provides a variety of filtering mechanisms to view statistical distributions and their time varying behavior over the grade crossings topology.Copyright