Application of GIS: Maritime Accident Analysis in Malaysian Waters Using Kernel Density Function

Statistics from the Marine Department in Malaysian Territorial waters has shown an increase in maritime accidents. The data of maritime accidents, including latitude and longitude of the locations, are analysed using Geographical Information System with Kernel Density function. This is to visualise, locate and identify the high-risk location of maritime accidents in Malaysian waters. Using the GIS analysis, the findings suggest that the data of the high-risk maritime location is at Malacca Straits. The results showed that GIS analysis is a useful tool to analyse maritime accidents data and can be used as a guidance for navigators to plan their passage in order to avoid maritime accidents.

Development of a Model for Evaluating the Coverage Area of Transit Center Using Smart Card Data

Since metropolitan cities are broadening as a result of urban sprawl, multimodal transportation systems have been adopted to fulfill the connection between the suburban and urban areas. The transportation system is being revamped around the transit center in the urban area to facilitate access to the downtown area from the suburbs. Studies are being conducted to improve the accessibility of public transportation by using the concept of hub-and-spoke. In this study, we develop a coverage area index (CAI) to assess the impact of a transit center on access to urban areas from the suburbs quantitatively. The concept of network centrality and the kernel density function is used to evaluate the extent of the influence of a transit center. The smart card data in the Seoul metropolitan area are used to analyze the CAI. Six transit centers in the Seoul metropolitan area are investigated to compare the coverage area to the transit center. The bandwidth of the kernel density function is set as 2 km considering the size and influence of each region. We evaluate six transit centers using the CAIs in Seoul compared to the index characteristics with transit accessibility (TA) index from previous studies. The CAI is possible to identify the incompetent centers, alternative routes to solve the problems of overcrowding on the centers, and areas with insufficient supplies of regional transit.

KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords: kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

Are NBA Policies that Promote Long-Term Competitive Balance Effective? What is the Price?

This paper examines competitive balance in the most prominent basketball league in the world: the NBA. Two types of graphs are used. First, long-term competitive balance is studied based on actual positions achieved by the teams on the Regu-lar Season. On the other hand, the competitive balance levels for each season are analyzed using sport betting odds data and through the use of two alternative strategies. In the first approach, density functions for the number of victories for all teams within a season are known, whereas in the second approach, a kernel density function of team winning probability is deter-mined for each season. Thus, a prospective competitive balance analysis is conducted. The study period covers seasons 1993-94 and 2011-12. The results suggest that long-term competitive balance levels are high, as many teams change their classifications. At season level, competitive balance seems to improve along the period studied. However, there are still too many differences between the teams in the same season.