LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering cluster-based local outliers
Pattern Recognition Letters
Spatial scan statistics: approximations and performance study
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On Trajectory Representation for Scientific Features
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Spatial Outlier Detection: A Graph-Based Approach
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Spatial neighborhood based anomaly detection in sensor datasets
Data Mining and Knowledge Discovery
Spatiotemporal neighborhood discovery for sensor data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Statistics-based outlier detection for wireless sensor networks
International Journal of Geographical Information Science
| Hi-index | 0.00 |
The widespread availability of Internet access and location-acquisition technologies, such as the global positioning system (GPS), has given rise to the growing phenomenon of Volunteered Geographic Information (VGI). Our work presents the use of VGI in bathymetry and hydrographic surveying and demonstrates that crowdsourced bathymetry data (CSB) can yield valuable knowledge for the maritime community. In this study, CSB data collected from 2012 to 2013 within the Baltimore Inner Harbor was used to locate anomalous depth measurements that could indicate the presence of submerged debris. To this end, we explored two approaches for detecting spatio-temporal outliers in the CSB data. In the first approach, we combined Local Outlier Factor and DBSCAN in an ensemble method to find spatio-temporal clusters of anomalous measurements that could indicate the presence of submerged debris. In the second approach, we calculated a measure of local spatial autocorrelation over time to identify "hotspots" or specific areas that consistently have low depth measurements compared to their immediate neighbors (i.e. "low-high" outliers). Results from both approaches revealed locations within the Fort McHenry Channel whose depth measurements may be indicative of the presence of submerged marine debris and, as such, may pose a threat to the safety of mariners operating in that region. Our results indicate that CSB data can not only help to improve the safety of mariners, but also serve to alert authorities in a timely manner that channel maintenance, a re-survey, and/or changes to the nautical chart may be needed.