Fuzzy Indices with Applications to Human Trafficking

John N. Mordeson, Aimee Schwab-McCoy (), Sunil Mathew () and M. Binu ()
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John N. Mordeson: Department of Mathematics, Creighton University, Omaha, Nebraska 68178, USA
Aimee Schwab-McCoy: Department of Mathematics, Creighton University, Omaha, Nebraska 68178, USA
Sunil Mathew: #x2020;Department of Mathematics, National Institute of Technology, Calicut 673601, Kerala, India
M. Binu: #x2020;Department of Mathematics, National Institute of Technology, Calicut 673601, Kerala, India

New Mathematics and Natural Computation (NMNC), 2019, vol. 15, issue 03, 411-425

Abstract: We develop indices that can be used to measure the susceptibility of a route to human trafficking. The susceptibility is based on data providing a measure of a country’s vulnerability for and its government response to human trafficking. The data was normalized and the Pearson correlation coefficient was used to determine the correlation between five types of government response and five types of vulnerability. It was determined that comparing government response values with vulnerability values yielded a negative correlation. Four routes through the Americas to the United States were examined. The indices of two of the measures agree on all four routes.

Keywords: Fuzzy graphs; human trafficking; government response; vulnerability; trafficking routes (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S1793005719500236

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