Violent Personal Crimes on Campus: The Influence of Size, Setting, and Police Presence

Statement of the Problem Violent campus crime, in particular rape and aggravated assault, has become more pronounced in the last several decades. The judicial and legislative branches have responded through court decisions and legal enactments that require most universities to collect and report campus crime data. Many concerns have arisen as to the effectiveness and implications associated with such mandates. Some believe current laws do little to improve campus safety. Some suggested solutions to address campus crime have involved safety initiatives/programs and potential staffing increases to their campus police force. Research Questions Are universities a reflection of surrounding community levels on violent crime and police staffing? Are violent on campus crimes and police staffing impacted by university location and enrollment? Does a relationship exist between police staffing and violent levels of personal crime? Methodology This study used a population of all Kentucky state-sponsored universities and their surrounding communities. Study measures involved demographic characteristics, police presence and violent personal crime. The data were analyzed using SPSS descriptively, in terms of frequency and measures of central tendency, and inferentially to test hypotheses. Independent t-tests were used to test differences in dependent variables (police presence and prevalence of crime) between campus and community. Differences in dependent variables by institutional setting were computed using a series of one-way analysis of variance (ANOVA). Findings Steady growth in both enrollment and community population existed from 2002-2005. However, campus growth outpaced surrounding community growth. The staffing level for campus police was less than their host community counterparts, but proved statistically significant in only two study years. In spite of comparatively smaller campus police presence, the risk of aggravated assault was higher for community residents than students on campus. However, the prevalence of forcible rape did not differ between campuses and community for any of the years observed. While campus police officer to student ratios declined as campus enrollment increased, there were no statistically significant correlations between the prevalence of forcible rape and university enrollment. Limitations on the study include: the study’s small sample, which impacts statistical testing unless strong correlations are identified; the lack of statistical significance for prevalence of forcible rape by both university and host community size should be interpreted cautiously because of the small cell sizes within each level; data reporting limitations, where discrepancies were identified and addressed; and other factors such as misreporting/underreporting also served as limitations but can not be fully properly measured. Recommendations The following recommendations are suggested: 1) further research needs to be conducted to measure factors not captured in this study, 2) an increase in campus police may result in potential on campus assault reduction, but have little impact reducing on campus rape –meaning additional and comparative program evaluations should be undertaken to measure the effectiveness of other campus safety initiatives, 3) to make campuses safer and to reduce institutional liability – administrators must continue to develop and test different programs rather than rely on campus mandated reporting requirements to satisfy their duties in this area

Efficient Multi-Robot Coverage of a Known Environment

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

A Profile of the 2004 Western Washington University Graduating Class

Profiles graduates from 2004, including gpas, honors, college of graduation, degree granted, etc

An Autonomous Surface Vehicle for Long Term Operations

Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto

The First-Year Experience (FYE) Program Report: Course Offerings in Fall, 2006

1) The FYE seminars are stand-alone seminars, not linked to a GUR course as are the FIGs. 2) While the FIGs are taught by faculty and qualified staff, the FYE seminars are taught only be full-time faculty. 3) FYE seminars are offered in the fall, winter, and spring quarters, whereas the FIGs are available only in the fall of each year. 4) The FYE program does not have a mission statement that brings together a unified purpose for all the FYE seminars. Rather, each seminar is more closely aligned with disciplinary and / or departmental goals

The Freshmen Interest Group (FIGs) Program Report: Fall, 2006

INTRODUCTION In the fall, 2006, the Freshman Interest Group (FIGs) Program entered its eighth interation. To use assessment terminology, the program has passed through its beginning and emergent stages and has entered maturity. Its mission has been crafted, student learning outcomes (SLO\u27s) have been identified, and its identity has solidified. (Please see Appendix One to read the FIGs mission statement, course criteria, student learning outcomes, and other FIGs-related documents.) Yet while maturity has wrought a degree of self-confidence, it has not brought complacency.The program remains one of the most assessed academic programs on campus. Yearly, FIGs administrators and instructors pore over results of surveys and quantitative data, searching for ways to improve both the program and its assessment. Findings from the fall, 2006, program offerings will be analyzed no differently. As is the case every year, in the spring preceding the fall course offerings, the FIGs seminar survey was retooled to more exacting standards. Most importantly, the survey included questions designed to explore the effectiveness of the seminars to attain goals based on a reconsidered set of revised student learning outcomes.In addition to the seminar survey, a new web-based survey was administered to three groups: FIGs students, Freshmen Year Experience (FYE) students, and a control group of freshmen who did not participate in either program. And, of course, quantitative data from the Data Warehouse was extracted and analyzed. Findings from all these sources are included in this report

A Profile of the 2007 Western Washington Unviersity Graduating Class

Summary and analysis of statistics for the 2007 Western graduating class

A Profile of the 2006 Western Washington University Graduating Class

Summary and analysis of statistics for the 2006 Western graduating class

A Profile of the 2003 Western Washington University Graduating Class

Profiles graduates from 2003, including GPAs, honors, college of graduation, degree granted, etc