OR vice: Forecasting and classification techniques for narcotics interdiction

Counternarcotics interdiction efforts have traditionally relied on historically determined sorting criteria or “best guess” to find and classify suspected smuggling traffic. We present a more quantitative approach which incorporates customized database applications, graphics software and statistical modeling techniques to develop forecasting and classification models. Preliminary results show that statistical methodology can improve interdiction rates and reduce forecast error. The idea of predictive modeling is thus gaining support in the counterdrug community. The problem is divided into sea, air and land forecasting, only part of which will be addressed here. The maritime problem is solved using multiple regression in lieu of multivariate time series. This model predicts illegal boat counts by behavior and geographic region. We developed support software to present the forecasts and to automate the process of performing periodic model updates. During the period, the model was in use at. Coast Guard Headquarters. Because of deterrence provided by improved intervention, the vessel seizure rate declined from 1 every 36 hours to 1 every 6 months. Due in part to the success of the sea model, the maritime movement of marijuana has ceased to be a major threat. The air problem is more complex, and required us to locally design data collection and display software. Intelligence analysts are using a customized relational database application with a map overlay to perform visual pattern recognition of smuggling routes. We are solving the modeling portion of the air problem using multiple regression for regional forecasts of traffic density, and discriminant analysis to develop tactical models that classify “good guys” and “bad guys”. The air models are still under development, but we discuss some modeling considerations and preliminary results. The land problem is even more difficult, and data collection is still in progress.     

Unemployment and Property Crime: Not a Simple Relationship

A bstract . While previous studies on the unemployment-crime hypothesis have yielded equivocal results, here nonsignificant Pearson correlation coefficients were found for theft rates and the percentage of unemployed men and unemployed women for 20 nations. This raises additional cross-cultural questions about the validity both of strain theories that predict positive correlations, and competing theories that predict negative correlations. Implications for research on the unemployment-crime hypothesis are discussed.     

Ensuring quality in higher education through student fees1

The article questions the myth of inequality and elitism on the grounds of tuition fees in higher education. It argues that the key to a successful transformation in higher education lies in the ability to provide ‘high quality products,’ driven by independent charges or fees and an equitable loan finance system. Unless these conditions are met, the author predicts an overcrowded university system and falling academic standards.