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Social and Behavioral Sciences Commons™
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Articles 1 - 9 of 9
Full-Text Articles in Social and Behavioral Sciences
Opencrimemapping.Org: An Online Tool For Visualizing Crime, Michael Crowder, Lauren Darr, Gerardo Garza, Brent Allen
Opencrimemapping.Org: An Online Tool For Visualizing Crime, Michael Crowder, Lauren Darr, Gerardo Garza, Brent Allen
SMU Data Science Review
In this paper we present a method for creating geographic visualizations of criminal incidents using open data and open-source software. The motivation for this method is to provide law enforcement agencies (LEAs) and interested citizens an affordable and relatively easy way to start analyzing geospatial data. The National Incident Based Reporting System (NIBRS) is a national standard for LEA incident reporting going into effect for all 18,000 U.S. LEAs in 2021. This project uses the Dallas Police Department's publicly available, NIBRS-style, incident data to develop a geovisual analysis tool called opencrimemapping.org.
Enhancing Trust In The Cryptocurrency Marketplace: A Reputation Scoring Approach, Dan Freeman, Tim Mcwilliams, Sudip Bhattacharyya, Craig Hall, Pablo Peillard
Enhancing Trust In The Cryptocurrency Marketplace: A Reputation Scoring Approach, Dan Freeman, Tim Mcwilliams, Sudip Bhattacharyya, Craig Hall, Pablo Peillard
SMU Data Science Review
Trust is paramount for the effective operation of any monetary system. While the distributed architecture of blockchain technology on which cryptocurrencies operate has many benefits, the anonymity of users on the blockchain has provided criminal users an opportunity to hide both their identities and illicit activities. In this paper, we present a scoring mechanism for cryptocurrency users where the scores represent users’ trustworthiness as safe or risky transactors in the cryptocurrency community. In order to distinguish law-abiding users from potential threats in the Bitcoin marketplace, we analyze historical thefts to profile transactions, classify them into risky and non-risky categories using …
Identifying Areas For Change: A Case Study On North Carolina State Public School Performance, Olivia Leeson, Kelly Bean, Jacob Drew
Identifying Areas For Change: A Case Study On North Carolina State Public School Performance, Olivia Leeson, Kelly Bean, Jacob Drew
SMU Data Science Review
In this paper, we present a framework to identify school-level factors within North Carolina public school administration’s control that have a positive impact on school performance. Public school administrators struggle to improve the academic performance of their schools, as the most influential factors determining overall school performance are outside of their scope of influence. We consider the current circumstances responsible for poor performance in North Carolina public schools and their implications for future academic improvement. Our framework utilizes an extreme gradient boosting model to predict school performance scores using only school-level features that administrators can impact. By varying the inputs, …
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …
Consumer Welfare And Price Discrimination: A Fine Line, Marie Wallmark, Eyal Greenberg, Dan Engels
Consumer Welfare And Price Discrimination: A Fine Line, Marie Wallmark, Eyal Greenberg, Dan Engels
SMU Data Science Review
Traditionally, it was not feasible for businesses to determine the maximum price the buyer was willing to pay, but with the availability of big data and the deployment of sophisticated algorithms, with a great degree of precision businesses can ascertain the maximum willingness price. Some forms of price discrimination are prohibited under the Robinson-Patman Act of Antitrust (1890), provided demographic characteristics such as race and gender are the determining factors. The problem with this interpretation is that sellers are not transparent about what factors are taken into consideration when determining price. Current laws are either limited in their interpretation or …
Goalie Analytics: Statistical Evaluation Of Context-Specific Goalie Performance Measures In The National Hockey League, Marc Naples, Logan Gage, Amy Nussbaum
Goalie Analytics: Statistical Evaluation Of Context-Specific Goalie Performance Measures In The National Hockey League, Marc Naples, Logan Gage, Amy Nussbaum
SMU Data Science Review
In this paper, we attempt to improve upon the classic formulation of save percentage in the NHL by controlling the context of the shots and use alternative measures than save percentage. In particular, we find save percentage to be both a weakly repeatable skill and predictor of future performance, and we seek other goalie performance calculations that are more robust. To do so, we use three primary tests to test intra-season consistency, intra-season predictability, and inter-season consistency, and extend the analysis to disentangle team effects on goalie statistics. We find that there are multiple ways to improve upon classic save …
On Identifying Factors Affecting Ethical Practices In Data Science Domains, Yanqin Wang, Earl Shaw, Brian Kruse, Mehdi Ghods
On Identifying Factors Affecting Ethical Practices In Data Science Domains, Yanqin Wang, Earl Shaw, Brian Kruse, Mehdi Ghods
SMU Data Science Review
In data science domains, ethics and ethical approaches are important to minimize adverse effects that may arise in data collection, analysis, and storage. What factors are influential for ethical practices in data science? In this research study, we designed a survey to capture an assessment of ethical concerns and practices from those currently active in the field by soliciting the attitudes/feelings of data science students and practitioners via the questionnaire. We analyzed the extent of their attitudes and identified factors contributing to the difference.
Comparative Study: Reducing Cost To Manage Accessibility With Existing Data, Claire Chu, Bill Kerneckel, Eric C. Larson, Nathan Mowat, Christopher Woodard
Comparative Study: Reducing Cost To Manage Accessibility With Existing Data, Claire Chu, Bill Kerneckel, Eric C. Larson, Nathan Mowat, Christopher Woodard
SMU Data Science Review
“Project Sidewalk” is an existing research effort that focuses on mapping accessibility issues for handicapped persons to efficiently plan wheelchair and mobile scooter friendly routes around Washington D.C. As supporters of this project, we utilized the data “Project Sidewalk” collected and used it to confirm predictions about where problem sidewalks exist based on real estate and crime data. We present a study that identifies correlations found between accessibility data and crime and housing statistics in the Washington D.C. metropolitan area. We identify the key reasons for increased accessibility and the issues with the current infrastructure management system. After a thorough …
Predicting How U.S. Counties Will Vote In Presidential Elections Through Analysis Of Socio-Economic Factors, Voting Heuristics, And Party Platforms, Joseph Stoffa, Randall Lisbona, Christopher Farrar, Mike Martos
Predicting How U.S. Counties Will Vote In Presidential Elections Through Analysis Of Socio-Economic Factors, Voting Heuristics, And Party Platforms, Joseph Stoffa, Randall Lisbona, Christopher Farrar, Mike Martos
SMU Data Science Review
In this paper, it is proposed that voters, devoid of any pressing concerns that could be addressed at the federal level, will tend to vote by their ideology for their preferred party. However, given pressing concerns, they will vote for whichever party can address these concerns despite party affiliation. This hypothesis is extended to the county level by assuming counties can be defined as the aggregate of their voting residence and as such their behavior can be predicted by considering their past voting history, socioeconomic makeup, and party platform.