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首页> 外文期刊>Journal of visualization >Visual exploration of urban functional zones based on augmented nonnegative tensor factorization
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Visual exploration of urban functional zones based on augmented nonnegative tensor factorization

机译：基于增强非负张量分解的城市功能区视觉探索

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摘要

A city contains a variety of different urban functions with referring to the purpose of land use to support the diverse needs of urban residents, such as residence, working and recreation. Exploring urban functional zones is a critical task, which provides valuable applications for business site selection, transportation management and urban planning. It is well known that location information and human mobility semantics both are significant factors for identifying urban functional zones, just considering one factor is not effective. However, most of existing techniques capture the transformation of functional zones and interpret the results based solely on location or mobility semantics, and lack the capacity to deal with the multifaceted features of urban data. To tackle these problems, in this paper, we propose an interactive visual analytics system for effectively exploring urban functional zones based on spatio-temporal OD data and Points of Interest data. We first adaptively partition the territory into region units based on adaptive blue noise sampling method, extract POI feature matrix (location information) as a prior knowledge, model multidimensional spatio-temporal OD data as a tensor for addressing multifaceted features and propose an augment tensor-based algorithm that enables users to simultaneously combine mobility semantics and inherent location information for identifying functional zones. In addition, we design a set of visual encodings to better understand and interpret the results in a visual and intuitive manner. This system has been demonstrated using two case studies with a real-world dataset of HangZhou city and domain-expert interviews.
机译：一个城市包含各种不同的城市功能，参考土地利用目的，以支持城市居民的多样化需求，如居住，工作和娱乐。探索城市功能区是一项关键任务，为商业场所选择，运输管理和城市规划提供了宝贵的应用。众所周知，位置信息和人类流动性语义都是识别城市功能区的重要因素，正考虑一个因素无效。然而，大多数现有技术捕获功能区的转换，并仅根据位置或移动语义来解释结果，并缺乏处理城市数据多方特征的能力。为了解决这些问题，在本文中，我们提出了一种基于时空OD数据和兴趣点数据有效探索城市功能区的互动视觉分析系统。我们首先将领土自适应地将区域分成基于自适应蓝噪声采样方法的区域单元，提取POI特征矩阵（位置信息）作为先验知识，将多维时空时间OD数据模拟为用于寻址多方特征的张量，并提出增强卷基于算法的算法使用户能够同时组合用于识别功能区的移动性语义和固有的位置信息。此外，我们设计了一组视觉编码，以更好地理解和以视觉和直观的方式解释结果。使用两种案例研究证明了这一系统，与杭州市的真实世界数据集和域名专家访谈。

著录项

  • 来源
    《Journal of visualization》 |2021年第2期|331-347|共17页
  • 作者

    Liyan Liu; Hongxin Zhang; Jiaxin Liu; Shangching Liu; Wei Chen; Jiaju Man;

  • 作者单位

    State Key Laboratory of HPC&SIP (MOE of China) and College of Mathematics and Statistics Hunan Normal University Changsha China;

    State Key Laboratory of CAD & CG Zhejiang University Hangzhou China;

    State Key Laboratory of CAD & CG Zhejiang University Hangzhou China;

    State Key Laboratory of CAD & CG Zhejiang University Hangzhou China;

    State Key Laboratory of CAD & CG Zhejiang University Hangzhou China;

    State Key Laboratory of HPC&SIP (MOE of China) and College of Mathematics and Statistics Hunan Normal University Changsha China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Tensor factorization; Visual analytics; Urban functions; Spatio-temporal data;

    机译：张量分解;视觉分析;城市功能;时空数据;

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