Towards geographically robust statistically significant regional colocation pattern detection
Towards geographically robust statistically significant regional colocation pattern detection
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DOI：
10.1145/3557989.3566158
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发表时间：
2022-11
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影响因子：
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通讯作者：
Subhankar Ghosh;Jayant Gupta;Arun Sharma;Shuai An;S. Shekhar
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文献类型：
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作者：
Subhankar Ghosh;Jayant Gupta;Arun Sharma;Shuai An;S. Shekhar
给定一组S空间特征类型，其特征实例，研究区域和邻居关系，目标是找到配对，使得C是区域rg中统计上显著的区域共定位模式。例如，驯鹿咖啡和星巴克在明尼阿波利斯有很大的共同点，但目前不在达拉斯。这个问题在包括生态学、经济学和社会学在内的各种领域都有应用。该问题是计算上的挑战，由于指数数量的区域托管模式和候选区域。目前关于区域共址模式检测的文献还没有解决可能导致虚假（机会）模式实例的统计显著性。在本文中，我们提出了一种新的技术，挖掘统计上显着的区域托管模式。我们的方法基于地理上定义的边界（例如，县），而不像以前的工作采用聚类或规则多边形来枚举候选区域。为了减少虚假模式，我们通过在相应区域内使用多个Monte Carlo模拟对观察到的数据点进行建模来执行统计显著性测试。使用Safegraph POI数据集，本文提供了一个案例研究，在明尼苏达州的零售机构验证所提出的想法。本文还利用博弈论和区域经济学对所发现的模式进行了详细的解释。
Given a set S of spatial feature-types, its feature-instances, a study area, and a neighbor relationship, the goal is to find pairs such that C is a statistically significant regional colocation pattern in region rg. For example Caribou Coffee and Starbucks are significantly co-located in Minneapolis but not in Dallas at present. This problem has applications in a wide variety of domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. The current literature on regional colocation pattern detection has not addressed statistical significance which can result in spurious (chance) pattern instances. In this paper, we propose a novel technique for mining statistically significant regional colocation patterns. Our approach determines regions based on geographically defined boundaries (e.g., counties) unlike previous works which employed clustering, or regular polygons to enumerate candidate regions. To reduce spurious patterns, we perform a statistical significance test by modeling the observed data points with multiple Monte Carlo simulations within the corresponding regions. Using Safegraph POI dataset, this paper provides a case study on retail establishments in Minnesota for validation of proposed ideas. The paper also provides a detailed interpretation of discovered patterns using game theory and regional economics.