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A case study for Beijing point of interest data using group linked Cox process

机译：北京兴趣点与综合COX流程的案例研究

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摘要

We develop in this article a group linked Cox process model for analyzing point of interest (POI) data. We focus on a Beijing POI dataset, which contains more than 22 thousand POIs in Beijing urban area. These POIs have been divided into many small categories (e.g., restaurants, movie theaters, hospitals, universities and subway stations) by the digital map maker (e.g., Baidu Map). Empirical analysis provides substantial evidence that POIs across different categories could be highly correlated so that those small categories can be further grouped. To this end, we develop here a group linked Cox process model. Specifically, within each group, we model POI locations by a standard Cox process so that the POI clustering effect can be well described. Furthermore, the idea of bivariate linked Cox process is borrowed and further extended to its multivariate counterpart. Consequently, a more significant number of POI categories can be accommodated within each group. To estimate the model, a minimum contrast type method is developed, and an automatically grouping method is provided. Simulation studies are conducted to validate the proposed methodology. At last, we apply our method to the aforementioned real dataset, and a total of 4 groups are uncovered. This leads to the discovery of some urban-planning-related features.
机译：我们在本文中开发了一个组链接Cox过程模型，用于分析兴趣点（POI）数据。我们专注于北京POI数据集，北京市区含有超过22000杆。这些POI已被数字地图制造商（例如，百度地图）分为许多小类别（例如，餐馆，电影院，医院，大学和地铁站）。实证分析提供了大量证据，即不同类别的POI可能会高度相关，以便可以进一步分组这些少数类。为此，我们在这里开发一个组链接Cox过程模型。具体而言，在每个组内，我们通过标准COX过程模拟POI位置，使得POI聚类效果可以很好地描述。此外，借用了双相结合的COX过程的想法，并进一步扩展到其多变量对应物。因此，可以在每个组内容纳更大量的POI类别。为了估计模型，开发了最小对比度方法，并且提供了自动分组方法。进行仿真研究以验证提出的方法。最后，我们将我们的方法应用于上述实时数据集，并且还发现了共4组。这导致发现某些与城市规划相关的功能。

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