全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

自组织双重空间聚类算法的城市扩张结构分析应用

DOI: 10.3724/SP.J.1047.2015.00638, PP. 638-643

Keywords: 自组织特征映射,城市扩张,景观扩张指数,空间组团,双重空间聚类

Full-Text   Cite this paper   Add to My Lib

Abstract:

双重空间聚类是能顾及空间连续性和属性相似性的空间数据分析,而常规空间聚类算法难以同时顾及2方面的约束条件。本文采用自组织双重空间聚类算法,对城市扩张结构分析进行了研究。通过改造自组织特征映射的最佳匹配神经元搜索的算法机制,在空间域和属性域进行迭代聚类搜索,实现了自组织双重空间聚类。以武汉市扩张斑块的位置信息和扩张程度指数为输入数据,使用自组织双重空间聚类算法,实现了城市扩张动态结构的识别。自组织双重空间聚类算法使得聚类结果,既在空间域上连续,又在属性域上相近,算法过程具有自组织性,减少了人为影响。

References

[1]  Zhang B, Yin W J, Xie M, et al. Geo-spatial clustering with non-spatial attributes and geographic non-overlapping constraint: A penalized spatial distance measure[C]. Proceedings of PAKDD'07, 2007:1072-1079.
[2]  Wang X, Hamilton H J. DBRS: A density-based spatial clustering method with random sampling[C]. Proceedings of the 7th PAKDD, Seoul, Korea, 2003:563-575.
[3]  Zhou J G, Guan J H, Li P X. DCAD: A dual clustering algorithm for distributed spatial databases[J]. Geo-spatial Information Science, 2007,10(2):137-144.
[4]  Henriques R, Bacao F, Lobo V. Exploratory geospatial data analysis using the GeoSOM suite[J]. Computers, Environment and Urban Systems, 2012,36:218-232.
[5]  李光强,邓敏,程涛.一种基于双重距离的空间聚类方法[J].测绘学报,2008,37(4):484-488.
[6]  周翠竹,朱建军,石岩.一种基于双重距离约束的多层次空间聚类方法[J].测绘科学,2014,39(10):98-101.
[7]  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.
[8]  Cao F, Ge Y, Wang J F. Optimal discretization for geographical detectors-based risk assessment[J]. GIScience & Remote Sensing, 2013,50(1):78-92.
[9]  Wang J F, Li X H, Christakos G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China[J]. International Journal of Geographical Information Science, 2010,24(1):107-127.
[10]  Jiao L M, Liu Y L, Zou B. Self-organizing dual clustering considering spatial analysis and hybrid distance measures[J]. Science in China-Earth Science, 2011,54(8):1268-1278.
[11]  Liu X P, Li X, Chen Y M, et al. A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data[J]. Landscape Ecology, 2010,25:671-682.
[12]  Lin C R, Liu K H, Chen M S. Dual clustering: Integrating data clustering over optimization and constraint domains[J]. IEEE Trans. Knowledge and Data Engineering, 2005,17(5):628-637.
[13]  Jiao L M, Liu Y L. Research on self-organizing clustering of spatial points[C]. Proceedings of SPIE Vol.6751, the 15th International Conference on Geoinformatics, Nanjing, 2007:5.
[14]  Chawla S, Shekhar S. Modeling spatial dependencies for mining geospatial data: An introduction[A]. In: Miller H J, Han J W (eds.). Geographic Data Mining and Knowledge discovery (GKD)[M]. Boca Raton, FL: Taylor and Francis, 2001.
[15]  Han J, Kamber M, Tung A K H. Spatial clustering methods in data mining: A survey[A]. In: H. Miller H J, Han J W (eds.). Geographic Data Mining and Knowledge Discovery[M]. Boca Raton, FL: Taylor and Francis, 2001.
[16]  Halkidi M, Batistakis Y, Vazirgiannis M. On Clustering Validation Techniques[J]. Journal of Intelligent Information Systems, 2001,17(2-3):107-145.
[17]  Tai C H, Dai B R, Chen M S. Incremental clustering in geography and optimization spaces[C]. Proceedings of PAKDD'07, 2007:272-283.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133