Spatial hotspot detection using polygon propagation
Spatial hotspot detection using polygon propagation
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DOI：
10.1080/17538947.2018.1485754
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发表时间：
2018-06
影响因子：
5.1
通讯作者：
Satya Katragadda;Jian Chen;Shaaban Abbady
中科院分区：
文献类型：
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作者：
Satya Katragadda;Jian Chen;Shaaban Abbady
摘要空间扫描统计是在流行病学、公共卫生、天文学和犯罪学等真实的应用中检测高活动或热点的最重要的模型之一。传统的扫描统计使用圆形等规则形状来检测高活动区域;相同的模型被扩展到日食以改进模型。最近的工作确定不规则形状的热点与地理边界的数据，在地理边界内的人口信息是可用的。由于采用了更好的绘图技术，与以前为之开发模型的汇总数据相比，将个案映射到经纬度变得更加容易。提出了一种不含地理边界信息的点数据空间热点检测方法。我们的算法检测热点作为一个多边形由一组三角形计算的多边形传播算法。该算法的时间复杂度与观测值的数量呈非线性关系，这对于较大的数据集来说并不能很好地扩展。为了改进模型，我们还引入了我们算法的MapReduce版本，以识别较大数据集的热点。
ABSTRACT Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology, public health, astronomy and criminology applications on geographic data. Traditional scan statistic uses regular shapes like circles to detect areas of high activity; the same model was extended to eclipses to improve the model. More recent works identify irregular shaped hotspots for data with geographical boundaries, where information about population within the geographical boundaries is available. With the introduction of better mapping technology, mapping individual cases to latitude and longitude became easier compared to aggregated data for which the previous models were developed. We propose an approach of spatial hotspot detection for point data set with no geographical boundary information. Our algorithm detects hotspots as a polygon made up of a set of triangles that are computed by a Polygon Propagation algorithm. The time complexity of the algorithm is non-linear to the number of observations, which does not scale well for larger datasets. To improve the model, we also introduce a MapReduce version of our algorithm to identify hotspots for larger datasets.