Detecting spatio-temporal outliers in crowdsourced bathymetry data

GEOCROWD 2013 - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

Volumn , Issue , 2013, Pages 55-62

  Sedaghat, Leela ^a   Hersey, John ^b   McGuire, Michael P ^a  

^a TOWSON UNIVERSITY   (United States)

^b SURVICE Engineering   (United States)

Author keywords

crowdsourced bathymetry data; outlier detection; spatio temporal data mining; submerged marine debris

Indexed keywords

BATHYMETRY; DATA MINING; DEBRIS; MAPS; MARINE ENGINEERING;

CROWDSOURCED BATHYMETRY DATA; DEPTH MEASUREMENTS; LOCAL OUTLIER FACTOR; MARINE DEBRIS; OUTLIER DETECTION; SPATIAL AUTOCORRELATIONS; SPATIO-TEMPORAL DATA MINING; VOLUNTEERED GEOGRAPHIC INFORMATION;

STATISTICS;

EID: 84889681648     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2534732.2534739     Document Type: Conference Paper

References (26)

1. J. Howe. 2006. Crowdsourcing: A definition. Retrieved June 2, 2006 from http://crowdsourcing.typepad.com/cs/2006/06/crowdsourcing-a.html.
2. D. Brabham. Crowdsourcing as a model for problem solving: An introduction and cases. Convergence: The International Journal of Research into New Media Technologies, 14(1): 75-90, 2008.
3. M. Norden, P. Cooper, and J. Hersey. "Crowdsourced Bathymetry: One Solution for Addressing Nautical Chart Data Deficiencies." US Hydro 2013.
4. ARGUS - Service Engineering. Retrieved August 28, 2013 from http://argus.survice.com/.
5. C. J. Moore. Synthetic polymers in the marine environment: a rapidly increasing, long-term threat. Environmental Research, 108(2): 131-139, 2008.
6. D. Birant and A. Kut. Spatio-temporal outlier detection in large databases. In Proceedings of the 28th International Conference on Information Technology Interfaces, 179-184, 2006.
7. M. Kulldorff. A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6): 1481-1496, 1997.
8. D. Agarwal, A. McGregor, J. Phillips, S. Venkatasubramanian, and Z. Zhu. Spatial scan statistics: approximations and performance study. In Proceedings of the 12th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 24-33, 2006.
9. Y. Zhang, N.A.S. Hamm, N. Meratnia, A. Stein, M. van de Voort, and P.J.M. Havinga. Statistics-based outlier detection for wireless sensor networks. International Journal of Geographical Information Science, 26(8): 1373-1392, 2012.
10. S. Shekhar, C.-T. Lu, and P. Zhang. Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 371-376, 2001.
11. M.P. McGuire, V. Janeja, and A. Gangopadhyay. Spatiotemporal neighborhood discovery for sensor data. in Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., and A.R. Ganguly, eds. Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers, Lecture Notes in Computer Science. Springer, Berlin, 2010, 203.
12. V.P. Janeja, N. Adam, V. Atluri, and J.S. Vaidya. Spatial neighborhood based anomaly detection in sensor datasets. Data Mining and Knowledge Discovery, 20(2): 221-258, 2010.
13. Y. Kou, C.-T. Lu, and R.F. Dos Santos. Spatial outlier detection: A graph-based approach. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, 281-288, 2007.
14. M.P. McGuire, V. Janeja, and A. Gangopadhyay. Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets. Data Mining and Knowledge Discovery, 2013. (In Press) Published Online: DOI:10.1007/s10618-013- 0324-z.
15. S. Mehta, S. Parthasarathy, and R. Machiraju. On trajectory representation for scientific features. In Proceedings of the 6th IEEE International Conference on Data Mining, 997-1001, 2006.
16. R. Hiller, B. Calder, P. Hogarth, and L. Gee. Adapting CUBE for Phase Measuring Bathymetric Sonars, International Conference on High-Resolution Survey in Shallow Water (HRSSW), Plymouth, Devon, UK, Sep 12 - Sep 15. 2005
17. E. M. Knorr and R.T. Ng. Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24th International Conference on Very Large Data Bases, 392-403, 1988.
18. M.M. Breunig, H.P. Kriegel, R.T. Ng, and J. Sander. LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93-104, 2000.
19. M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 226-231, 1996.
20. Z. He, X. Xu, and S. Deng. Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10): 1641-1650, 2003.
21. J. K. Ord and A. Getis. Testing for local spatial autocorrelation in the presence of global autocorrelation. Journal of Regional Science, 41(3): 411-432, 2001.
22. W. Tobler. Computation of the correspondence of geographical patterns. Papers and Proceedings of the Regional Science Association, 15(1): 131-139, 1965.
23. J. Odland. 1988. Scientific Geography Series, Vol. 9: Spatial Autocorrelation. Sage Publications, Newbury Park, CA.
24. L. Anselin. Local indicators of spatial association-LISA. Geographical analysis, 27(2): 93-115, 1995.
25. S. Openshaw. The modifiable areal unit problem. Concepts and Techniques in Modern Geography (CATMOG), 38: 41, 1984.
26. S. Openshaw. Ecological fallacies and the analysis of area census data. Environment and Planning A, 16: 17-31, 1984.