GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions

Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that LASSO dominates least squares, ridge and Liu estimators over a significant portion of the parameter space for large dimension. Secondly, a simulation study was conducted to compare performance of ridge, Liu and two parameter biased estimator by their mean squared error criterion. I found that two parameter biased estimator performs better than its corresponding ridge regression estimator. Overall, Liu estimator performs better than both ridge and two parameter biased estimator.

Hotel Remodeling Impact: Guest Satisfaction at a Family Resort Hotel

A case study of a family resort hotel demonstrated empirical relationships between guest satisfaction and their perception of the hotel's physical appearance, staff attitude, and the guests' age group. The 333 self-administered surveys also provided information about the guests' travel behavior and their experience at the hotel. The predictive regression model confined that the hotel was in need of remodeling, and that potential renovation projects will ultimately result in increased guest satisfaction.

Variables affecting the success of older applicants for employment in a public organization: An age -cued cognitive model of administrative decision-making

The dissertation takes a multivariate approach to answer the question of how applicant age, after controlling for other variables, affects employment success in a public organization. In addition to applicant age, there are five other categories of variables examined: organization/applicant variables describing the relationship of the applicant to the organization; organization/position variables describing the target position as it relates to the organization; episodic variables such as applicant age relative to the ages of competing applicants; economic variables relating to the salary needs of older applicants; and cognitive variables that may affect the decision maker's evaluation of the applicant. ^ An exploratory phase of research employs archival data from approximately 500 decisions made in the past three years to hire or promote applicants for positions in one public health administration organization. A logit regression model is employed to examine the probability that the variables modify the effect of applicant age on employment success. A confirmatory phase of the dissertation is a controlled experiment in which hiring decision makers from the same public organization perform a simulated hiring decision exercise to evaluate hypothetical applicants of similar qualifications but of different ages. The responses of the decision makers to a series of bipolar adjective scales add support to the cognitive component of the theoretical model of the hiring decision. A final section contains information gathered from interviews with key informants. ^ Applicant age has tended to have a curvilinear relationship with employment success. For some positions, the mean age of the applicants most likely to succeed varies with the values of the five groups of moderating variables. The research contributes not only to the practice of public personnel administration, but is useful in examining larger public policy issues associated with an aging workforce. ^

Effect of Hydrologic Restoration on the Habitat of The Cape Sable Seaside Sparrow, Annual Report of 2004-2005

The major activities in Year 3 on ‘Effect of hydrologic restoration on the habitat of the Cape Sable seaside sparrow (CSSS)’ included presentations, field work, data analysis, and report preparation. During this period, we made 4 presentations, two at the CSSS – fire planning workshops at Everglades National Park (ENP), one at the Society of Wetland Scientists’ meeting in Charleston, SC, and a fourth at the Marl Prairie/CSSS performance measure workshop at ENP. We started field work in the third week of January and continued till June 3, 2005. Early in the field season, we completed vegetation surveys along two transects, B and C (~15.1 km). During April and May, vegetation sampling was completed at 199 census sites, bringing to 608 the total number of CSSS census sites with quantitative vegetation data. We updated data sets from all three years, 2003-05, and analyzed them using cluster analysis and ordination as in previous two years. However, instead of weighted averaging, we used weighted-averaging partial least square regression (WA-PLS) model, as this method is considered an improvement over WA for inferring values of environmental variables from biological species composition. We also validated the predictive power of the WA-PLS regression model by applying it to a sub-set of 100 census sites for which hydroperiods were “known” from two sources, i.e., from elevations calculated from concurrent water depth measurements onsite and at nearby water level recorders, and from USGS digital elevation data. Additionally, we collected biomass samples at 88 census sites, and determined live and dead aboveground plant biomass. Using vegetation structure and biomass data from those sites, we developed a regression model that we used to predict aboveground biomass at all transects and census sites. Finally, biomass data was analyzed in relation to hydroperiod and fire frequency.