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首页> 外文期刊>Progress in Artificial Intelligence >Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China
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Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China

机译：基于地理加权回归模型的工业陆地转移价格推动因素：中国农村土地体制改革试点的证据

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摘要

More and more studies on land transfer prices have been carried out over time. However, the influencing factors of the industrial land transfer price from the perspective of spatial attributes have rarely been explored. Selecting 25 towns as the basic research unit, based on industrial land transfer data, this paper analyzes the influencing factors of the price distribution of industrial land in Dingzhou City, a rural land system reform pilot in China, by using a geographically weighted regression (GWR) model. Eight evaluation factors were selected from five aspects: economy, population, topography, landform, and resource endowment. The results showed that: (1) Compared with the traditional ordinary least squares (OLS) model, the GWR model revealed the spatial differentiation characteristics of the industrial land transfer price in depth. (2) Factors that have a negative correlation with the industrial land transfer price include the proportion of cultivated land area and distance to the city. Factors that have a positive correlation with the industrial land transfer price include the population growth rate, economic growth rate, population density, and number of hospitals per unit area. (3) The results of GWR model analysis showed that the impact of different factors on the various towns of different models had significant spatial differentiation characteristics. This paper will provide a reference for the sustainable use of industrial land in developing countries.
机译：随着时间的推移，更多和更多更多关于土地转移价格的研究。但是，从空间属性视角下，工业土地转移价格的影响因素很少被探索。本文选择了25个城镇作为基础研究单位，本文分析了中国农村土地系统改革试点的工业土地价格分布的影响因素，采用地理加权回归（GWR ） 模型。八个评价因素选自五个方面：经济，人口，地形，地貌和资源禀赋。结果表明：（1）与传统的普通最小二乘（OLS）模型相比，GWR模型揭示了工业土地转移价格的空间分化特征。 （2）与工业陆地转让价格负相关的因素包括耕地面积的比例和与城市的距离。与工业土地转移价格具有正相关的因素包括人口增长率，经济增长率，人口密度和每单位区域的医院数量。 （3）GWR模型分析结果表明，不同因素对不同模型城镇的影响具有显着的空间分化特征。本文将为发展中国家的工业用地可持续利用提供参考。

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