Discovering Spatial Mixture Patterns of Interest
Discovering Spatial Mixture Patterns of Interest
复制标题
发现感兴趣的空间混合模式
DOI：
10.1145/3397536.3422217
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Shekhar, Shashi
中科院分区：
文献类型：
--
作者：
Xie, Yiqun;Bao, Han;Li, Yan;Shekhar, Shashi
给定k种类型的N个地理定位点样本的集合，我们的目标是检测感兴趣的空间混合模式，这些模式是研究区域中具有不同类型点的显著高或低混合的子区域。空间混合模式在许多社会领域都有重要的应用，包括智慧城市和社区的弹性、生物多样性、公平、商业智能等。这个问题具有挑战性，因为候选模式的排序和选择可能非常容易受到自然随机性的影响，而现实世界的数据通常由各种混合模式组成。在相关工作中，多项扫描统计量由于其“无方向性”和对数据中混合模式组成的高灵敏度，不支持高或低混合的识别。生物多样性研究中的物种丰富度指标具有方向性，但对空间随机效应非常敏感。为了弥补这一差距，我们首先提出了一个空间混合指数来提供候选模式之间的稳健排名。然后，我们提出了一种基于基线算法的双水平蒙特卡罗估计方法用于空间混合模式检测。最后，我们提出了一种精确算法和一种分布启发的序列约简启发式算法来加速基线方法。合成数据和实际数据的实验结果表明，该方法能够以较高的精度检测混合模式，加速方法在保持高解质量的同时大大降低了计算成本。
Given a collection of N geo-located point samples of k types, we aim to detect spatial mixture patterns of interest, which are sub-regions of the study area that have significantly high or low mixture of points of different types. Spatial mixture patterns have important applications in many societal domains, including resilience of smart cities and communities, biodiversity, equity, business intelligence, etc. The problem is challenging because ranking and selection of candidate patterns can be highly susceptible to the effect of natural randomness, and real-world data often consists of various mixture patterns. In related work, the multi-nomial scan statistic does not support identification of high or low mixture due to its "directionless" nature and high sensitivity to the composition of mixture patterns in data. While species richness indices in biodiversity research allow specification of directions, the measures are very sensitive to spatial randomness effects. To bridge the gap, we first propose a spatial mixture index to provide robust ranking among candidate patterns. Then, we present a dual-level Monte-Carlo estimation method with a baseline algorithm for spatial mixture pattern detection. Finally, we propose both an exact algorithm and a distribution-inspired sequence-reduction heuristic to accelerate the baseline approach. Experiment results with both synthetic and real-world data show that the proposed approaches can detect mixture patterns with high accuracy, and the acceleration methods can greatly reduce computational cost while maintaining high solution quality.
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DOI：
10.1109/icdm.2018.00183
发表时间：
2018-11
期刊：
2018 IEEE International Conference on Data Mining (ICDM)
影响因子：
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作者：
Yiqun Xie;Han Bao;S. Shekhar;Joseph K. Knight
通讯作者：
Yiqun Xie;Han Bao;S. Shekhar;Joseph K. Knight
DOI：
10.1145/3340964.3340968
发表时间：
2019-08
期刊：
Proceedings of the 16th International Symposium on Spatial and Temporal Databases
影响因子：
--
作者：
Yiqun Xie;S. Shekhar
通讯作者：
Yiqun Xie;S. Shekhar
影响因子：
2
作者：
Jung, Inkyung;Kulldorff, Martin;Klassen, Ann C.
通讯作者：
Klassen, Ann C.
通过智能空间技术彻底改变树木管理
DOI：
10.1145/3347146.3359066
发表时间：
2019
期刊：
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
影响因子：
--
作者：
Xie, Yiqun;Shekhar, Shashi;Feiock, Richard;Knight, Joseph
通讯作者：
Knight, Joseph