Analyzing the spatial factors related to the distributions of building heights in urban areas: A comparative case study in Guangzhou and Shenzhen
Analyzing the spatial factors related to the distributions of building heights in urban areas: A comparative case study in Guangzhou and Shenzhen
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城市建筑高度分布的空间因素分析——以广州、深圳为例
DOI：
10.1016/j.scs.2019.101854
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发表时间：
2020
影响因子：
11.7
通讯作者：
Yutong Cui
中科院分区：
文献类型：
--
作者：
Jinyao Lin;Huiyin Wan;Yutong Cui
快速城市化已成为世界范围内日益严重的问题。以往的研究多集中于城市的二维发展，而对建筑高度的空间特征研究较少。这些信息可以为智能城市规划和管理提供宝贵的影响。然而，以前的尝试并没有系统地研究影响建筑高度的空间因素及其与城市发展的关系。因此，本研究开发了一种基于机器学习的方法来比较广州和深圳这两个不同发展模式的城市的建筑高度分布。首先，我们收集了建筑物的详细资料，例如位置和地价。其次，基于上述信息，使用著名的随机森林，k-最近邻算法和人工神经网络来模拟每个建筑物的高度。随机森林算法在这两个城市的表现都优于其他两个算法。我们还发现，商业用地的价值是最重要的因素与建筑物的高度。此外，广州的建筑高度对距行政中心的距离更为敏感，而深圳的建筑高度则受距交通网络的距离的影响更大。总的来说，这些研究结果可以支持城市规划和管理。更重要的是，所提出的方法可以用来预测新的建筑物的高度和调查的建筑物高度在其他地区的分布。
Rapid urbanization has become an increasingly serious issue worldwide. While most previous studies focused on two-dimensional urban development, the spatial characteristics of building heights are rarely explored. Such information could provide valuable implications for smart urban planning and management. However, previous attempts did not systematically investigate the spatial factors that influence building heights and their associations with urban development. Therefore, this study developed a machine learning-based method to compare the distributions of building heights in Guangzhou and Shenzhen, two cities with different development patterns. First, we collected detailed information on the buildings, such as the location and land values. Second, the height of each building was simulated based on the above information using the well-known random forests,k-nearest neighbor algorithm, and artificial neural network. The random forests algorithm outperformed the other two in both cities. We also found that the commercial land value is the most important factor associated with building heights. Moreover, the building heights in Guangzhou are more sensitive to the distances to administrative centers, while the distances to transportation networks exert stronger influences on the building heights in Shenzhen. Overall, these findings could support urban planning and management. More importantly, the proposed method can be used to predict the heights of new buildings and investigate the distributions of building heights in other regions.
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影响因子：
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