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首页> 外文期刊>Accident Analysis and Prevention >A visual approach for defining the spatial relationships among crashes, crimes, and alcohol retailers: Applying the color mixing theorem to define the colocation pattern of multiple variables
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A visual approach for defining the spatial relationships among crashes, crimes, and alcohol retailers: Applying the color mixing theorem to define the colocation pattern of multiple variables

机译：一种视觉方法，用于定义崩溃，犯罪和酒精零售商之间的空间关系：应用颜色混合定理来定义多个变量的分配模式

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摘要

In traffic safety studies, the few scholars who have focused on analyzing disaggregated data obtained results that have been either difficult to explain or demonstrate because they did not provide clear visual maps or utilize statistical tests to quantify the spatial relationships. In order to increase the use of such disaggregated spatial methods for use in traffic safety studies, the current study documents the application of a new RGB (red, green, blue) model which combines the color additive theorem and the kernel density map (KDE) to define crash colocation patterns and the coincidence spaces of related variables.This study contributes to the literature in three major ways: (1) a new RGB model was established and applied in the field of traffic safety; (2) the variable dimensions were expanded from two to three; and, (3) the dimension of uncertainty was also included. When the new RGB model was utilized with data collected in College Station, Texas, the results indicated that the new colocation map is able to clearly and accurately define colocation hotspots of crashes, crimes, and alcohol retailers. As expected, these hotspots are located in areas with many bars, the largest strip malls and busiest intersections. The intensity maps have provided results consistent with the above colocation maps. However, the uncertainty map does not show a relatively higher level of certainty regarding the location of hotspots as we expected because the input of each variable was not related to the highest kernel value. Therefore, future scholars should focus on the colocation and intensity maps while using the uncertainty map as a reference for individual event risk evaluation only.
机译：在交通安全研究，谁也重点分析了分列数据的少数学者获得已要么难以解释或证明，因为他们没有提供清晰的视觉地图或利用统计测试，以量化的空间关系的结果。为了增加在交通安全研究使用使用这种分类空间的方法，目前的研究文件，新的RGB（红，绿，蓝）模型，结合了色素添加剂定理和核密度地图的应用程序（KDE）定义崩溃托管模式和相关variables.This研究有助于的巧合空间文献在三个主要方面：（1）成立，并在交通安全领域应用新的RGB模式; （2）可变尺寸从两到三个扩大;并且，也包括（3）不确定的尺寸。当新的RGB模型用在College Station，德克萨斯州收集的数据所使用的，结果表明，新的托管地图是能够清楚地和准确地定义崩溃，犯罪，和醇零售商托管热点。正如预期的那样，这些热点都位于拥有许多酒吧，最大的商业街和最繁忙的十字路口的地方。强度图已提供的结果与上述托管映射一致。然而，不确定性图并没有显示确定性就如我们所预期，因为每个变量的输入是不相关的最高核心价值热点的位置相对较高的水平。因此，在使用不确定性图作为仅有个别事件风险评估基准日后的学者应把重点放在托管和强度图。

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