|
|
- 2018
显著局部空间同位模式自动探测方法
|
Abstract:
局部空间同位模式挖掘旨在揭示多类地理事件在异质环境下的共生共存规律。已有的方法一方面需要模式筛选的频繁度阈值参数，另一方面需要区域探测的划分参数或聚类参数，参数的不合理设置会导致挖掘结果不可靠甚至出现错误。因此，提出了一种显著局部空间同位模式自动探测方法。首先，基于空间统计思想，采用非参数模式重建方法对空间同位模式进行显著性判别，将全局非显著空间同位模式作为进一步局部探测的候选模式；然后，借助自适应空间聚类方法提取每个候选模式的热点区域；最后，通过不断生长并测试每个热点区域，界定显著局部空间同位模式的有效边界，即空间影响域。通过实验与比较发现，该方法能够客观且有效判别空间同位模式的显著性，并且自适应地提取局部同位模式的空间分布结构，降低了现有方法参数设置的主观性
| [1] | Qian F, Chiew K, He Q, et al. Mining Regional Colocation Patterns with KNNG[J]. Journal of Intelligent Information Systems, 2014, 42(3):485-505 |
| [2] | Mohan P, Shekhar S, Shine J A, et al. A Neighborhood Graph Based Approach to Regional Colocation Pattern Discovery:A Summary of Results[C]. The 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, USA, 2011 |
| [3] | Wang S, Huang Y, Wang X S. Regional Colocations of Arbitrary Shapes[C]. International Symposium on Spatial and Temporal Databases, Munich, Germany, 2013 |
| [4] | Illian J, Penttinen A, Stoyan H, et al. Statistical Analysis and Modelling of Spatial Point Patterns[J].Technometrics, 2008, 47(4):516-517 |
| [5] | Gelfand A E. Handbook of Spatial Statistics[M]. UK:CRC Press, 2010 |
| [6] | Openshaw S. Geographical Data Mining:Key Design Issues[C]. Proceedings of GeoComputation, Virginia, USA, 1999 |
| [7] | Barua S, Sander J. Mining Statistically Sound Colocation Patterns at Multiple Distances[C]. The 26th International Conference on Scientific and Statistical Database Management, Aalborg, Denmark, 2014 |
| [8] | Liu Qiliang, Deng Min, Shi Yan, et al. A Novel Spatial Clustering Method Based on Multi-Constraints[J]. Acta Geodaetica et Cartographica Si-nica, 2011, 40(4):509-516(刘启亮, 邓敏, 石岩, 等. 一种基于多约束的空间聚类方法[J]. 测绘学报, 2011,40(4):509-516) |
| [9] | Barua S, Sander J. Mining Statistically Significant Colocation and Segregation Patterns[J].IEEE Transactions on Knowledge & Data Engineering, 2014, 26(5):1185-1199 |
| [10] | Keddy P A. Wetland Ecology:Principles and Conservation[M]. UK:Cambridge University Press, 2010 |
| [11] | Sha Zongyao,Li Xiaolei. Algorithm of Mining Spatial Association Data Under Spatially Heterogeneous Environment[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12):1480-1484(沙宗尧, 李晓雷. 异质环境下的空间关联规则挖掘[J]. 武汉大学学报·信息科学版,2009, 34(12):1480-1484) |
| [12] | Yoo J S, Shekhar S. A Joinless Approach for Mi-ning Spatial Colocation Patterns[J].IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10):1323-1337 |
| [13] | Celik M, Kang J M, Shekhar S. Zonal Colocation Pattern Discovery with Dynamic Parameters[C]. The 7th IEEE International Conference on Data Mining, Omaha, NE, USA, 2007 |
| [14] | Neyman J, Scott E L. Statistical Approach to Problems of Cosmology[J].Journal of the Royal Statistical Society, 1958, 20(1):143 |
| [15] | Lotwick H W, Silverman B W. Methods for Analysing Spatial Processes of Several Types of Points[J].Journal of the Royal Statistical Society. Series B (Methodological), 1982, 44(3):406-413 |
| [16] | Diggle P J. Statistical Analysis of Spatial Point Patterns[M]. London:Edward Arnold Publishers, 2003 |
| [17] | Ripley B D. The Second Order Analysis of Stationary Point Processes[J]. Journal of Applied Probability, 1976,13(2):255-266 |
| [18] | Zimmer K D, Hanson M A, Butler M G. Interspecies Relationships, Community Structure, and Factors Influencing Abundance of Submerged Macrophytes in Prairie Wetlands[J].Wetlands, 2003, 23(4):717-728 |
| [19] | Shekhar S, Huang Y.Discovering Spatial Colocation Patterns:A Summary of Results[C]. International Symposium on Spatial and Temporal Databases, Redondo Beach, USA, 2001 |
| [20] | Yoo J S, Shekhar S, Smith J, et al. A Partial Join Approach for Mining Colocation Patterns[C]. The 12th Annual ACM International Workshop on Geographic Information Systems, Washington D C, USA, 2004 |
| [21] | Goodchild M F. The Fundamental Laws of GIScience[R]. University Consortium for Geographic Information Science, University of California, Santa Barbara, 2003 |
| [22] | Shekhar S, Evans M R, Kang J M, et al. Identi-fying Patterns in Spatial Information:A Survey of Methods[J]. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery, 2011, 1(3):193-214 |
| [23] | Xiao X, Xie X, Luo Q, et al. Density Based Colocation Pattern Discovery[C]. The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Irvine, CA, USA, 2008 |
| [24] | Ding W, Eick C F, Yuan X, et al. A Framework for Regional Association Rule Mining and Scoping in Spatial Datasets[J].Geoinformatica, 2011, 15(1):128 |
| [25] | Wiegand T, Moloney K A. Handbook of Spatial Point Pattern Analysis in Ecology[M]. UK:CRC Press, 2013 |
| [26] | Wiegand T, He F, Hubbell S P. A Systematic Comparison of Summary Characteristics for Quantifying Point Patterns in Ecology[J].Ecography, 2013, 36(1):92-103 |
| [27] | Yoo J S, Bow M. Mining Spatial Colocation Patterns:A Different Framework[J].Data Mining and Knowledge Discovery, 2012, 24(1):159-194 |
| [28] | Besag J, Diggle P J. Simple Monte Carlo Tests for Spatial Patterns[J].Journal of the Royal Statistical Society:Series C (Applied Statistics), 1977, 26(3):327-333 |
| [29] | Eick C F, Parmar R, Ding W, et al. Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets[C]. The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Irvine, California, 2008 |
