[1] 邓永录, 梁之舜.随机点过程及其应用[M].北京:科学出版社, 1998.
[2] 杨萍, 侯威, 支蓉.利用空间点过程提取丛集点算法的适用性研究[J].物理学报, 2009, 58(3):2097-2105.
[3] 李长江, 徐有浪, 蒋叙良.论矿床的分形性质[J].浙江地质, 1994(2):25-31.
[4] Pei T, Gong X, Shaw S L, et al. Clustering of temporal event processes[J]. International Journal of Geographical Information Science, 2013, 27(3):484-510.
[5] Illian J, Penttinen A, Stoyan H, et al. Statistical analysis and modelling of spatial point patterns[M]. West Sussex: John Wiley & Sons Ltd, 2008.
[6] Cressie N. Statistics for spatial data[M]. New York: John Wiley & Sons, 1993.
[7] Lloyd M. Mean crowding[J]. The Journal of Animal Ecology, 1967, 36(1):1-30.
[8] Douglas J B. Clustering and aggregation[J]. Sankhyā: The Indian Journal of Statistics, Series B, 1975, 37(4):398-417.
[9] Assuncao R. Testing spatial randomness by means of angles[J]. Biometrics, 1994, 50(2):531-537.
[10] Trifkovi? S, Yamamoto H. Indexing of spatial patterns of trees using a mean of angles[J]. Journal of Forest Research, 2008, 13(2):117-121.
[11] Eberhardt L L. Some developments in distance sampling[J]. Biometrics, 1967, 23(2):207-216.
[12] Johnson R B, Zimmer W J. A more powerful test for dispersion using distance measurements[J]. Ecology, 1985, 66(5):1669-1675.
[13] Prayag V R, Deshmukh S R. Testing randomness of spatial pattern using Eberhardt's index[J]. Environmetrics, 2000, 11(5):571-582.
[14] Lucio P S, Brito N L C. Detecting randomness in spatial point patterns: A "Stat-Geometrical" alternative[J]. Mathematical Geology, 2004, 36(1):79-99.
[15] Mugglestone M A, Renshaw E. Spectral tests of randomness for spatial point patterns[J]. Environmental and Ecological Statistics, 2001, 8(3):237-251.
[16] Ripley B D. Modelling spatial patterns[J]. Journal of the Royal Statistical Society Series B (Methodological), 1977, 39(2):172-212.
[17] Diggle P J, Gates D J, Stibbard A. A nonparametric estimator for pairwise-interaction point processes[J]. Biometrika, 1987, 74(4):763-770.
[18] Schiffers K, Schurr F M, Tielbörger K, et al. Dealing with virtual aggregation——a new index for analyzing heterogeneous point patterns[J]. Ecography, 2008, 31(5):545-555.
[19] Pei T. A non-parameter index for differentiating between heterogeneity and randomness[J]. Mathematical Geosciences, 2011(43): 345-362.
[20] Kulldorff M. A spatial scan statistic[J]. Communications in Statistics-Theory and methods, 1997, 26(6):1481-1496.
[21] Sheikholeslami G, Chatterjee S, Zhang A. Wavecluster: A multi-resolution clustering approach for very large spatial databases[C]. Proceedings of the 24th International Conference on Very Large Data Bases, New York City, 1998, 428-439.
[22] Allard D, Fraley C. Nonparametric maximum likelihood estimation of features in spatial point processes using Voronoitessellation[J]. Journal of the American Statistical Association, 1997, 92(440):1485-1493.
[23] Byers S, Raftery A E. Nearest-neighbor clutter removal for estimating features in spatial point processes[J]. Journal of the American Statistical Association, 1998, 93(442):577-584.
[24] Guo D S, Zhu X, Jin H, et al. Discovering Spatial Patterns in Origin‐Destination Mobility Data[J]. Transactions in GIS, 2012, 16(3):411-429.
[25] Fraley C, Raftery A E. How many clusters? Which clustering method? Answers via model-based cluster analysis[J]. Computer Journal, 1998(41):578-588.
[26] Murtagh F, Starck J L. Pattern clustering based on noise modeling in wavelet space[J]. Pattern Recognition, 1998, 31(7):847-855.
[27] Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, 1996, 226-231.
[28] Ankerst M, Breunig M M, Kriegel H-P, et al. OPTICS: Ordering points to identify the clustering structure[C].//Delis A, Faloutsos C, Ghandeharizadeh S (Eds.). Proc. ACM SIGMOD Int. Conf. on Management of Data, June 1-3, 1999, Philadelphia, PA, USA. ACM Press, 1999, 49-60.
[29] Pei T, Zhu A X, Zhou C H, et al. A new approach to the nearest‐neighbour method to discover cluster features in overlaid spatial point processes[J]. International Journal of Geographical Information Science, 2006, 20(2):153-168.
[30] Pei T, Jasra A, Hand D J, et al. DECODE: A new method for discovering clusters of different densities in spatial data[J]. Data Mining and Knowledge Discovery, 2009, 18(3):337-369.
[31] Pei T, Zhu A X, Zhou C H, et al. Detecting feature from spatial point processes using Collective Nearest Neighbor[J]. Computers, Environment and Urban Systems, 2009, 33(6):435-447.
[32] Kulldorff M. SaTScan user guide for version 9.0. 2011.
[33] Birant D, Kut A. ST-DBSCAN: An algorithm for clustering spatial–temporal data[J]. Data & Knowledge Engineering, 2007, 60(1):208-221.
[34] Pei T, Zhou C H, Zhu A X, et al. Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise[J]. International Journal of Geographical Information Science, 2010, 24(6):925-948.
[35] Pei T, Gong X, Shaw S L, et al. Clustering of temporal event processes[J]. International Journal of Geographical Information Science, 2013, 27(3):484-510.
[36] Alt H, Godau M. Computing the Fréchet distance between two polygonal curves[J]. International Journal of Computational Geometry and Applications, 1995, 5(1):75-91.
[37] Sankoff D, Kruskal J B. Time warps, string edits, and macromolecules: The theory and practice of sequence comparison[M]. Addison-Wesley Publishing Company, 1983.
[38] Crochemore M, Rytter W. Text algorithms[M]. New York, USA: Oxford University Press, 1994.
[39] Alt H, Guibas L. Handbook on computational geometry[M]. Interpolation, and Approximation-A Survey, 1995, 251-265. |