| dc.contributor.author | Wu, Yan. | en_US |
| dc.date.accessioned | 2014-10-21T12:34:13Z | |
| dc.date.available | 2007 | |
| dc.date.issued | 2007 | en_US |
| dc.identifier.other | AAINR27653 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/54932 | |
| dc.description | Recreational boating is a very popular activity in Canada. Consequently, its associated risk is appreciable. Although the study of recreational boating is a very important element of maritime risk analysis, little spatial information is available on recreational boating movements. However, a better understanding of the patterns in Canada could be important for coastal safety and security, two key issues that motivated this project. | en_US |
| dc.description | For this study, Global Positioning System (GPS) data points were collected for a sample of recreational boating trajectories of four types of boats, namely canoes, kayaks, motorboats and sailboats, and in two environments, coastal and river. The GPS data were then examined to find spatial patterns by establishing and detecting each trajectory's important movement features. Based on these patterns, trajectories of different boat types were simulated to help evaluate recreational boating traffic in the context of a recognized maritime risk of incidents. | en_US |
| dc.description | Aside from the critical steps of assiduous data cleaning for preparation of features extraction, the other indispensable process is dedensification of the data. Using GPS units to gather data results in a large number of points for a single trajectory, but not all of these points are significant for pattern analysis. Dedensifying is the process of removing such unnecessary points while retaining turns. A modified MARIN Douglas-Peucker algorithm was developed to accomplish this purpose. Furthermore, in order to overcome the limitations of setting a single pre-specified tolerance value for such an algorithm, this study advances an objective and context-specific method to select the best dedensified trajectory for any given boat trip. | en_US |
| dc.description | After these preparations, evaluations were conducted showing that eight attributes adequately represent the patterns, and algorithms were developed to calculate them: total distance travelled, mean speed, maximum speed, maximum five percent speed, mean turning angle, coverage index, aspect ratio and furthest distance from shore. Classification of boat types models for both the individual study areas and the combined geographic areas were constructed based on univariate ANOVA tests, multivariate discriminant analysis and other statistics techniques. The classification rates of the linear models, which only retain independent variables with significant discriminating power, exceed 80% accuracy. | en_US |
| dc.description | It was found that one can discriminate and classify between different boat types to varying extents based on these movement patterns' attributes. Moreover, it was also shown that there are some differences in vessel movements between the two areas, but most of the patterns are not dependent on location. | en_US |
| dc.description | This study developed procedures for a novel application: spatial pattern analysis for recreational boating based strictly on GPS trajectory points. The results of this study provide insight into recreational boat movement characteristics and exposure levels to advance the research on risk analysis associated with this activity, improving accident prevention and search and rescue resource planning. Moreover, the results of this research can help detect boat types and abnormal movements based solely on tracking, which may prove useful for coastal security. | en_US |
| dc.description | Thesis (Ph.D.)--Dalhousie University (Canada), 2007. | en_US |
| dc.language | eng | en_US |
| dc.publisher | Dalhousie University | en_US |
| dc.publisher | | en_US |
| dc.subject | Engineering, Industrial. | en_US |
| dc.title | Characterizing recreational boating patterns based on GPS trajectory points. | en_US |
| dc.type | text | en_US |
| dc.contributor.degree | Ph.D. | en_US |
