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Prediction of the spatial distribution of high-rise residential buildings by the use of a geographic field based autologistic regression model

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Abstract

As an indicator of urbanization, high-rise residential buildings, can meet the space requirements of an increasing population and improve land use efficiency. Such buildings are continuously built in the central areas of cities worldwide despite residential suburbanization. To predict high-rise residential building location, this study employs a geographic field model-based autologistic regression model (GFM-autologistic model). In line with this goal, a model is determined using both the value of the area under the receiver operating characteristic curve (ROC) and the Akaike information criterion (AIC) for GFM-autologistic, Euclidean distance (ED)-logistic and ED-autologistic models. The minimum AIC and the maximum ROC values of the GFM-autologistic model indicate that this model has the best fit. The GFM defines the external effect of ecological elements and locational factors, and it also quantifies distance decay through a linear intensity function with an influence threshold, thereby avoiding the bias caused by ED. Moreover, land prices are positive related to building height. High-rise residential development also considers open public spaces, such as rivers and city plazas. In summary, the spatial distribution of high-rise residential buildings displays a distance decay in the effect of ecological elements such as open spaces. Thus, this manuscript provides a theoretical basis for modern-city development planning and modern high-rise residential development.

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Acknowledgments

This research is supported by the Public Welfare Scientific Research Project (No. 201412023).

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Authors and Affiliations

  1. School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Peng Zhou, Yanfang Liu & Yiyun Chen

  2. School of Land Resources Management, Huazhong Agricultural University, 1 Lion Road, Wuhan, 430070, China

    Chen Zeng

  3. Hanjiang Water Environment Monitoring Center, Bureau of Hydrology, Changjiang Water Resources Commission, 6 Pipashan Road, Xiangyan, 441022, China

    Zhouyuan Wang

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  1. Peng Zhou
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  2. Yanfang Liu
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  3. Yiyun Chen
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  4. Chen Zeng
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Correspondence to Yanfang Liu.

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Zhou, P., Liu, Y., Chen, Y. et al. Prediction of the spatial distribution of high-rise residential buildings by the use of a geographic field based autologistic regression model. J Hous and the Built Environ 30, 487–508 (2015). https://doi.org/10.1007/s10901-014-9426-1

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  • DOI: https://doi.org/10.1007/s10901-014-9426-1

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