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Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps-based analysis of Wuhan, China

机译:城市人类活动密度时空变化与地理因素的关系:武汉武汉的探索性百度热插拔分析

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摘要

With the development and popularity of mobile Internet technology, data sources of human activity in urban centers are rapidly updated and play an important role in supporting urban planning and management. Therefore, it is critical to integrate different data sources and detect spatially implicit information in the spatial pattern of relationships between urban human activity and related geographical factors. A new analytical framework is first proposed to integrate multisource location-based big data and use these data to analyze dynamic real-time human activity density (HAD). Taking Wuhan, the largest city in central China as an example, using the Baidu's thermal data, this paper analyzes spatiotemporal characteristics of HAD distributions at different points on weekends and weekdays, and further combines the relevant cities' points of interest data to analyze the correlations between different spatial elements and HAD distributions. The results show that: (a) Using a new indicator and data processing method can simply achieve effective utilization of Baidu's thermal data; (b) Combined with standardized grids, spatial density estimation can match the two different data sources in this study; (c) The greater the HAD, the greater is the elasticity of change, and in the active population area, the densities of human activity on weekends and weekdays at different times have significant differences; and (d) Different geographically weighted regression models effectively distinguish the influence of different urban elements on weekdays and weekends. In particular, the impact patterns of the workplace, education, and cityscape reflect the unique spatial patterns of research cases. These findings, as well as visual analytics, help in the understanding of the potential value of Baidu heatmaps in urban study and provide support for more scientific and accurate urban planning and space management for the better consideration of real-time changes in human activity.
机译:随着移动互联网技术的发展和普及,城市中心的人类活动数据来源迅速更新,并在支持城市规划和管理方面发挥着重要作用。因此,将不同的数据源集成并在城市人类活动与相关地理因素之间的空间模式中检测空间隐含信息至关重要。首先提出一种新的分析框架来集成基于Multisource位置的大数据,并使用这些数据来分析动态实时人类活动密度(具有)。以武汉是中国中部最大的城市为例,采用百度的热数据,分析周末和平日在不同点处具有不同点的时空特征,并进一步结合了相关城市的兴趣点数据来分析相关性在不同的空间元素之间并具有分布。结果表明:(a)使用新的指标和数据处理方法可以简单地实现百度的热数据的有效利用; (b)结合标准化网格,空间密度估计可以匹配本研究中的两个不同的数据来源; (c)越大,变化的弹性越大,在活跃的人口区,周末和平日在不同时间的人类活动的密度具有显着差异; (d)不同地理上加权回归模型有效地区分不同城市元素对平日和周末的影响。特别是,工作场所,教育和城市景观的影响模式反映了研究案例的独特空间模式。这些发现以及视觉分析有助于了解百度热插拔在城市研究中的潜在价值,并为更好的科学和准确的城市规划和空间管理提供支持,以便更好地考虑人类活动的实时变化。

著录项

  • 来源
    《Growth and Change》 |2020年第1期|共25页
  • 作者单位

    Cent China Normal Univ Coll Publ Adm Luoyu Rd Wuhan 430079 Hubei Peoples R China;

    China Elect Opt Valley Union Inst Architectural D BIM Dept Wuhan Hubei Peoples R China;

    Cent China Normal Univ Coll Publ Adm Luoyu Rd Wuhan 430079 Hubei Peoples R China;

    Huazhong Univ Sci &

    Technol Coll Publ Adm Wuhan Hubei Peoples R China;

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

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