一种用于城市结构检测的概率嵌入聚类方法
A Probabilistic Embedding Clustering Method for Urban Structure Detection
摘要：
城市结构探测是城市地理学的一项基本任务。聚类是城市空间结构、城市功能区划等模式识别的核心技术，在大数据时代，记录人类行为、人类社会活动等信息的城市感知数据具有高维、高噪声的复杂性。不幸的是，最先进的聚类方法不能同时处理高维和高噪声问题。本文提出了一种概率嵌入聚类方法。首先，我们提出了一个概率嵌入模型（PEM），通过概率模型的学习，发现潜在的特征，从高维城市传感数据。通过潜在特征，我们可以捕捉隐藏在高维数据中的本质特征，称为模式;通过概率模型，我们还可以减少高噪声带来的不确定性。其次，通过调整参数，我们的模型可以发现两种类型的城市结构：同质性和结构对等性，即相互作用强烈的社区或在城市结构中扮演相同角色的社区。我们通过对真实世界数据的实验来评估我们的模型的性能，并在上海（中国）的真实数据的实验证明，我们的方法可以发现两种类型的城市结构，同质性和结构等价性，这意味着聚集社区具有强烈的互动或在城市空间中的相同角色。
英文摘要：
Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
