| 显著局部空间同位模式自动探测方法 |
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| 引用本文： | 徐枫,蔡建南,刘启亮,何占军,邓敏.显著局部空间同位模式自动探测方法[J].武汉大学学报(信息科学版),2018,43(10):1538-1545. |
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| 作者姓名： | 徐枫 蔡建南 刘启亮 何占军 邓敏 |
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| 作者单位： | 1.中南大学地理信息系, 湖南 长沙, 410083 |
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| 基金项目： | 国家自然科学基金41730105国家自然科学基金41601410湖南省科技计划项目2015SK2078中南大学研究生自主探索创新项目2017zzts174 |
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| 摘 要： | 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。
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| 关 键 词： | 空间异质性 局部空间同位模式 非参数检验 模式重建 自适应空间聚类 |
| 收稿时间： | 2017-11-26 |
An Automatic Method for Discovering Significant Regional Spatial Colocation Patterns |
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| Institution: | 1.Department of GeoInformatics, Central South University, Changsha 410083, China |
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| Abstract: | Discovery of regional spatial colocation patterns facilities understanding of the spatial dependency of different spatial features at the regional scale. However, two challenges remain:①appropriate thresholds for prevalence measures are difficult to specify without prior knowledge; and ②natural localities of regional spatial colocation patterns with different densities and shapes can hardly be automatically detected. On that account, an automatic method for discovering significant regional spatial colocation patterns is proposed in this paper. First, a nonparametric statistical model is developed to test for significance of spatial colocation patterns. Then, an adaptive spatial clustering method is modified to detect hot spots of each candidate regional spatial colocation pattern that is not identified as a statistically significant spatial colocation pattern at the global scale. At last, all hot spots are iteratively expanded until no larger statistically significant localities can be detected. Comparison between this automatic method and an existing method is carried out with both simulated and ecological datasets. Experiments show that the regional spatial colocation patterns can be effectively detected with less subjectivity and prior knowledge by this automatic method. |
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