Revealing functional regions via joint matrix factorization based model

Shan Wang, Yajing Xu, Sheng Gao
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引用次数: 3

Abstract

Various functions in the region are emerging with the process of urbanizations, such as the residential, entertainment or hospital districts, which can be indicated during the urban planning. Recently, researchers try to discover the region functions with the human mobility data based on machine learning and statistical models. However, previous work always employs the single domain data like mobility information or district attributes to measure the functions. In this paper, we will address the problem by integrating multi-domain data, like human trajectories, base station information and Points-of-Interest attributes. For that, we propose a joint nonnegative matrix factorization model to combine the multi-source data and extract the function distribution of each urban region, then the dominated function among the regions can be uncovered based on the clustering process over the extracted distributions. We also evaluate the performance of our method on the real-world dataset to demonstrate the advantages of our proposed model over the baseline methods.
基于联合矩阵分解模型揭示功能区
随着城市化进程的推进,区域内出现了多种功能,如居住区、娱乐区、医院区等,这些都可以在城市规划中体现出来。近年来,研究人员试图利用基于机器学习和统计模型的人类移动数据来发现区域功能。然而,以往的研究大多采用单一领域的数据,如流动性信息或地区属性来衡量其功能。在本文中,我们将通过整合多领域数据来解决这个问题,如人类轨迹、基站信息和兴趣点属性。为此,提出一种联合非负矩阵分解模型,结合多源数据提取各城市区域的函数分布,然后对提取的分布进行聚类处理,揭示各区域间的主导函数。我们还评估了我们的方法在真实数据集上的性能,以证明我们提出的模型优于基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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