Detecting feature from spatial point processes using Collective Nearest Neighbor

Computers, Environment and Urban Systems

Volumn 33, Issue 6, 2009, Pages 435-447

  Pei, Tao ^a   Zhu, A Xing ^a,b   Zhou, Chenghu ^a   Li, Baolin ^a   Qin, Chengzhi ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Classification entropy; EM algorithm; Feature; Noise; Point pattern; Shared nearest neighbor; Spatial scan method

Indexed keywords

EM ALGORITHMS; POINT PATTERN; POINT PATTERNS; SCAN METHODS; SHARED NEAREST NEIGHBORS;

EARTHQUAKES; POISSON DISTRIBUTION;

ENTROPY;

ALGORITHM; IMAGE CLASSIFICATION; NEAREST NEIGHBOR ANALYSIS; PATTERN RECOGNITION; SPATIAL DATA;

EID: 70449699684     PISSN: 01989715     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compenvurbsys.2009.08.001     Document Type: Article

References (51)

1. Agrawal R., Gehrke J., Gunopulos D., and Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. Proceedings of 1998 ACM-SIGMOD international conference on management of data (1998), ACM Press, New York 94-105
2. Ankerst M., Breunig M.M., Kriegel H.-P., and Sander J. OPTICS: Ordering points to identify the clustering structure. Proceedings of ACM-SIGMOD'99 international conference on management data (1999), Philadelphia, USA 46-60
3. Boots B., and Getis A. Point pattern analysis (1988), Sage Publications, Beverly Hills, CA
4. Byers S.D., and Raftery A.E. Nearest-neighbor clutter removal for estimating features in spatial point processes. Journal of the American Statistical Association 93 (1998) 577-584
5. Celeux G., and Govaert G. A classification EM algorithm and two stochastic versions. Computational Statistics and Data Analysis 14 (1992) 315-332
6. Chainey S., and Ratcliffe J. GIS and crime mapping (mastering GIS: Technol, applications & mgmnt) (2005), John Wiley & Sons, Inc., New York
7. Chen Y., Liu J., and Ge H.K. Pattern characteristics of foreshock sequences. Pure and Applied Geophysics 155 (1999) 395-408
8. China Seismograph Network Data Management Center. (2009). China Seismograph Network (CSN) Catalog: Accessed 05.01.09.
9. Cressie N.A.C. Statistics for spatial data. 1st ed. (1991), John Wiley & Sons, Inc., New York
10. Dasgupta A., and Raftery A.E. Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association 93 (1998) 294-302
11. Ertoz, L., Steinbach, M., & Kumar, V. (2002). A new shared nearest neighbor clustering algorithm and its applications, In Proceeding of Workshop on Clustering High Dimensional Data and its Applications (pp. 105-115), Arlington, Va., USA.
12. Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceeding of 2nd International Conference on Knowledge Discovery and Data Mining (pp. 226-231), Portland, OR.
13. Feng H., and Huang D.Y. A catalogue of earthquake in western China (1970-1975, M ≥ 1) (1980), Seismological Press, Beijing (in Chinese)
14. Feng H., and Huang D.Y. Earthquake catalogue in west China (1976-1979, M ≥ 1) (1989), Seismological Press, Beijing (in Chinese)
15. Fraley C., and Raftery A.E. Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST. Journal of Classification 20 (2003) 263-286
16. Fraley C., and Raftery A.E. How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal 41 (1998) 578-588
17. Gaudart J., Poudiougou B., Dicko A., Ranque S., Toure O., Sagara I., et al. Space-time clustering of childhood malaria at the household level: A dynamic cohort in a Mali village. BMC Public Health 6 (2008) 1-13
18. Hodge V.J., and Austin J. A survey of outlier detection methodologies. Artificial Intelligence Review 22 (2004) 85-126
19. Illian J., Penttinen A., Stoyan H., and Stoyan D. Statistical analysis and modeling of spatial point patterns (2008), John Wiley & Sons Ltd., West Sussex (Section 1.5)
20. Kulldorff M., and Nagarwalla N. Spatial disease clusters: Detection and inference. Statistics in Medicine 14 (1995) 799-810
21. Kulldorff M. A spatial scan statistic. Communications in Statistics - Theory and Methods 26 (1997) 1481-1496
22. Kulldorff M., Heffernan R., Hartman J., Assuncao R., and Mostashari F. A space-time permutation scan statistic for disease outbreak detection. Plos Medicine 2 (2005) 216-224
23. Lawson B.A. Statistical methods in spatial epidemiology. 1st ed. (2001), John Wiley & Sons, New York
24. Lin C.Y., and Chang C.C. A new density-based scheme for clustering based on genetic algorithm. Fundamenta Informaticae 68 (2005) 315-331
25. Lucio P.S., and Brito N.L.C. Detecting randomness in spatial point patterns: A 'stat-geometrical' alternative. Mathematical Geology 36 (2004) 79-99
26. Jarvis R.A., and Patrick E.A. Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers C-22 (1973) 1025-1034
27. Jiao Y.B., Wu K.T., and Yang M.D. Assessment about capability and quality of our country earthquake observation network. Earthquake Research in China 6 (1990) 1-7 (in Chinese)
28. Matsu'ura R.S., and Karakama I. A point-process analysis of the Matsushiro earthquake swarm sequence. The effect of water on earthquake occurrence. Pure and Applied Geophysics 162 (2005) 1319-1345
29. Moon T.K. The expectation-maximization algorithm. IEEE Signal Processing Magazine 13 (1996) 47-60
30. Nagesh, H., Goil, S., & Choudhary, A. (1999). Mafia: Efficient and scalable subspace clustering for very large data sets, Technical report TR #9906-010, Northwestern University, 1999, Accessed January 2009.
31. Openshaw S. Using a geographical analysis machine to detect the presence of spatial clusters and the location of clusters in synthetic data. In: Alexander F.E., and Boyle P. (Eds). Methods for investigating localised clustering of disease IARC scientific publication no 135 (1996), Lyon, France 68-87
32. Openshaw S., Charlton M., Wymer C., and Craft A.W. A mark I geographical analysis machine for the automated analysis of point data sets. International Journal of Geographical Information Systems 1 (1987) 335-358
33. Openshaw S., Charlton M., Craft A.W., and Birth J.M. An investigation of leukaemia clusters by the use of a geographical analysis machine. Lancet I (1988) 272-273
34. Pei T., Yang M., Zhang J.S., Zhou C.H., Luo J.C., and Li Q.L. Multi-scale expression of spatial activity anomalies of earthquakes and its indicative significance on the space and time attributes of strong earthquakes. Acta Seismologica Sinica 3 (2003) 292-303
35. Pei T., Zhu A.X., Zhou C.H., Li B.L., and Qin C.Z. A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes. International Journal of Geographical Information Science 20 (2006) 153-168
36. Pei T., Zhu A.X., Zhou C.H., Li B.L., and Qin C.Z. Delineation of support domain of feature in the presence of noise. Computers and Geosciences 33 (2007) 952-965
37. Reasenberg P.A. Foreshock occurrence rates before large earthquakes worldwide. Pure and Applied Geophysics 155 (1999) 355-379
38. Ripepe M., Piccinini D., and Chiaraluce L. Foreshock sequence of September 26th, 1997 Umbria-Marche earthquakes. Journal of Seismology 4 (2000) 387-399
39. Ripley B.D. Spatial point pattern analysis in ecology. In: Legendre P., and Legendre L. (Eds). Developments in numerical ecology. NATO AS1 Series Vol. G14 (1987), Springer-Verlag, Berlin 407-429
40. Rogerson P.A. A statistical method for the detection of geographic clustering. Geographical Analysis 33 (2001) 215-227
41. Roy S., and Bhattacharyya D.K. An approach to find embedded clusters using density based techniques. Lecture Notes in Computer Science 3816 (2005) 523-535
42. Skellam J.G. Studies in statistical ecology, I: Spatial pattern. Biometrika 39 (1952) 346-362
43. Umino N., Okada T., and Hasegawa A. Foreshock and aftershock sequence of the 1998 M ≥ 5.0 Sendai, northeastern Japan, earthquake and its implications for earthquake nucleation. Bulletin of the Seismological Society of America 92 (2002) 2465-2477
44. Wu K.T., Jiao Y.B., Lu P.L., and Wang Z.D. Panorama of seismic sequence (1990), University Press, Beijing (in Chinese)
45. Wyss M., and Toya Y. Is background seismicity produced at a stationary Poissonian rate. Bulletin of the Seismological Society of America 90 (2000) 1174-1187
46. Wang, W., Yang, J., & Muntz, R. (1997). STING: A statistical information grid approach to spatial data mining. In Proceeding of the 23rd international conference on very large data bases (VLDB'97) (pp. 186-195), Athens, Greece, August.
47. Yan P., and Clayton M.K. A cluster model for space-time disease counts. Statistics in Medicine 25 (2006) 867-881
48. Yamada I., and Thill J.-C. Local indicators of network-constrained clusters in spatial point patterns. Geographical Analysis 39 (2007) 268-292
49. Yang T.Y., and Lee J.C. Bayesian nearest-neighbor analysis via record value statistics and nonhomogeneous spatial Poisson processes. Computational Statistics and Data Analysis 51 (2007) 4438-4449
50. Zhang Z.C. Earthquake cases in China (1976-1980) (1990), Seismological Press, Beijing (in Chinese)
51. Zhuang J.C., Chang C.P., Ogata Y., and Chen Y.L. A study on the background and clustering seismicity in the Taiwan region by using point process models. Journal of Geophysical Research - Solid Earth 110 (2005) 10.1029/2004JB003157 B05S18