Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices
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多尺度地理和时间加权回归:探索房价的时空决定因素

DOI:
10.1080/13658816.2018.1545158
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发表时间:
2018-11
影响因子:
5.7
通讯作者:
Du Qingyun
Du Qingyun
中科院分区:
地球科学2区
文献类型:
--
作者:
Wu Chao;Ren Fu;Hu Wei;Du Qingyun

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摘要 理解尺度效应对于地理学研究来说是重要且不可或缺的。然而,用于测量不同过程的操作规模的空间和时空统计工具相当有限。本文通过提出多尺度 GTWR (MGTWR) 扩展了流行的地理和时间加权回归 (GTWR) 模型,以考虑运营规模效应,该模型提供了一个灵活且可扩展的框架,用于通过为各种协变量指定灵活的带宽来识别和分析多尺度过程。然后,利用MGTWR来探讨深圳房价的时空变化以及影响因素与房价的关系。本文尝试考虑尺度效应将GTWR扩展到MGTWR,从而凸显不同水平时空异质性的重要性。此外,本研究的实证结果可以为城市规划应解决时间和空间维度多尺度效应的地区的房地产开发提供有价值的政策启示。
ABSTRACT Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.
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