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基于GWR的北京市二手房价影响因素及其规律研究
发表时间：2014-07-15 浏览量：2232 下载量：946
| 全部作者： | 刘秀敏，胡涛 |
| 作者单位： | 首都师范大学数学科学学院 |
| 摘 要： | 应用空间变系数回归模型和地理加权回归（geographical weighted regression, GWR）估计方法，分析北京市二手房价格的空间分布规律，研究二手房屋价格的影响因素及其空间影响程度。对空间变系数回归模型进行空间非平稳性检验，证实空间变系数回归模型优于普通线性回归模型。对20处给定地理坐标的位置，利用加权平均方法估计其位置处的二手房屋价格，并将估计结果与附近小区房价进行对比。所得结果可以指导相关部门合理做出城市规划，促进北京市二手房市场的科学管理，也可让人们了解不同地区的二手房屋价格。 |
| 关 键 词： | 应用统计数学；房价；地理加权回归；空间非平稳性 |
| Title： | Exploration of the influence factors and the law of the factors of the second-hand housing prices in Beijing based on GWR |
| Author： | LIU Xiumin, HU Tao |
| Organization： | School of Mathematical Sciences, Capital Normal University |
| Abstract： | This paper applied the spatial varying coefficient regression model and the estimation method of geographical weighted regression (GWR) to analysis the spatial distribution law of the second-hand housing prices of Beijing, and researched the influence factors of the second-hand housing prices and the degree of special effects. This paper also took a non-stationary test for the spatial varying coefficient regression model and confirmed it had better performance than the ordinary linear regression model. In the end, according to the given geographic coordinates of the 20 locations, the weighted interpolation method had been used to estimate the second-hand housing prices in these places. This paper also compared the housing price between estimation results and neighborhoods. The results could guide the relevant departments to make a reasonable city planning, promote the scientific management of the secondary housing market in Beijing, and let the people understand the secondary housing prices of different regions. |
| Key words： | applied statistical mathematics; housing price; geographical weighted regression; spatial non-stationary |
| 发表期数： | 2014年7月第13期 |
| 引用格式： | 刘秀敏，胡涛. 基于GWR的北京市二手房价影响因素及其规律研究[J]. 中国科技论文在线精品论文，2014，7（13）：1330-1344. |
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