A Visualization Method for Mining Colocation Patterns Constrained by a Road Network
A Visualization Method for Mining Colocation Patterns Constrained by a Road Network
复制标题
路网约束下挖掘托管模式的可视化方法
DOI：
10.1109/access.2020.2980168
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Hu Wenqing
中科院分区：
文献类型：
--
作者：
Zhou Mengjie;Ai Tinghua;Zhou Guohua;Hu Wenqing
托管挖掘对于理解地理现象之间发生的相互作用或依赖关系很有用。大多数托管挖掘方法都是基于平面空间的。然而，在城市空间中，许多与人类相关的活动受到道路网络的限制。平面定位挖掘方法不适合研究城市空间中的相关地理现象。在本文中，我们提出了一种可视化方法来发现受道路网络约束的代管模式。该方法由两个主要部分组成：网络核密度估计和网络定位规则图构建。在位置规则地图构建组件中，基于混色的思想对空间网络地理现象之间的空间交互进行建模。我们使用具有不同空间模式、不同样本量和不同路网事件之间最大距离的模拟数据集来测试我们的方法。实验结果表明，该方法对不同情况下的并置模式挖掘是有效的。我们还改变了网络托管规则图的分辨率，结果表明分辨率对结果影响很小。在案例研究中，我们应用我们的方法来探索芝加哥环路地区和邻近北区的犯罪与城市设施之间的空间关联。
Colocation mining is useful for understanding the interactions or dependencies that occur among geographic phenomena. Most colocation mining methods are based on planar space. However, in urban spaces, many human-related activities are constrained by a road network. Planar colocation mining methods are not suitable for studying the concerning geographic phenomena in an urban space. In this paper, we propose a visualization method to discover colocation patterns constrained by a road network. The method consists of two major components: network kernel density estimation and network colocation rule map construction. In the colocation rule map construction component, spatial interactions among spatial network geographic phenomena are modeled based on the idea of color mixing. We use simulated datasets with different spatial patterns, different sample sizes, and different maximum distances between road network events to test our method. The results show that our method is effective for mining colocation patterns in different situations. We also change the resolution of the network colocation rule maps, and the results show that the resolution has little influence on the results. In the case study, we apply our method to explore the spatial association between crimes and city facilities in the Loop and the Near North Side districts of Chicago.
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DOI：
10.1145/2598153.2600039
发表时间：
2014-05
期刊：
Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces
影响因子：
--
作者：
S. Gama;D. Gonçalves
通讯作者：
S. Gama;D. Gonçalves
DOI：
10.1016/j.compenvurbsys.2008.05.001
发表时间：
2008-09-01
影响因子：
6.8
作者：
Xia, Zhixiao;Yan, Jun
通讯作者：
Yan, Jun
影响因子：
8.9
作者：
Sajib Barua;J. Sander
通讯作者：
Sajib Barua;J. Sander
影响因子：
6.1
作者：
Wenhao Yu;T. Ai;Shiwei Shao
通讯作者：
Wenhao Yu;T. Ai;Shiwei Shao
影响因子：
5.9
作者：
Anderson, Tessa K.
通讯作者：
Anderson, Tessa K.