基于GWR模型的南京主城区住宅地价空间异质性驱动因素研究
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摘要
以南京市主城区为例,基于2014年住宅用地监测点数据,利用GWR模型分析各驱动因素对住宅地价贡献程度及空间异质性成因,以促进地价的科学规范化管理。研究表明:①各影响因素对住宅地价的平均边际贡献程度为:CBD>河流水系>城市快速路>大学>医院>公园绿地>小学>幼儿园;②CBD仍占据影响住宅地价的主导地位,城市公共设施对住宅地价的负向效应有扩大趋势,"教育型"设施逐渐成为居民购房行为的重要影响因素,面状自然地物在空间影响模式上存在"互补效应";③城市住宅地价影响模式由单一影响因素为主导向多因素共同作用转化,各影响因素虽作用大小仍有所侧重但差异逐渐缩小;④较全局OLS模型(50%),GWR模型可解释监测范围内87%的住宅地价变化,能够更加精准地研究土地市场的空间异质性。
This paper, taking the downtown of Nanjing as an research area and based on the final retained data from residential land monitoring sites in 2014, applies the geographically weighted regression(GWR) model to simulate the contribution of driving factors and the causes of spatial heterogeneity of residential land market in study area, so as to promote the scientific and standardized management of land price. The results indicate that: 1) In the GWR model, the order of the average marginal contribution on the land premium from high to low is the distance from CBD, river,expressway, college, hospital, park, primary school and kindergarten. 2) The distance from CBD has occupied the dominant position in influencing factors of residential land prices. The negative effect of urban public facilities on residential land prices tends to expand and the 'educational-type' infrastructures have gradually become an essential influencing factor in the purchase behavior of residents. There is a 'complementary effect' on the spatial influence model of surface natural features. 3) The impact model of urban residential land price was dominated by a single influencing factor and then transforms to multi-factor interaction. The function intensity difference of each of influencing factors is gradually narrowing. 4) The GWR model can be well applied to stimulate spatial heterogeneity of land market in target area accurately. The model could explain 87% of the price changes of residential land within the monitoring range. The interpreting abilities have been improved significantly than that based on global regression model(50%).
This paper, taking the downtown of Nanjing as an research area and based on the final retained data from residential land monitoring sites in 2014, applies the geographically weighted regression(GWR) model to simulate the contribution of driving factors and the causes of spatial heterogeneity of residential land market in study area, so as to promote the scientific and standardized management of land price. The results indicate that: 1) In the GWR model, the order of the average marginal contribution on the land premium from high to low is the distance from CBD, river,expressway, college, hospital, park, primary school and kindergarten. 2) The distance from CBD has occupied the dominant position in influencing factors of residential land prices. The negative effect of urban public facilities on residential land prices tends to expand and the 'educational-type' infrastructures have gradually become an essential influencing factor in the purchase behavior of residents. There is a 'complementary effect' on the spatial influence model of surface natural features. 3) The impact model of urban residential land price was dominated by a single influencing factor and then transforms to multi-factor interaction. The function intensity difference of each of influencing factors is gradually narrowing. 4) The GWR model can be well applied to stimulate spatial heterogeneity of land market in target area accurately. The model could explain 87% of the price changes of residential land within the monitoring range. The interpreting abilities have been improved significantly than that based on global regression model(50%).
引文
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[17]李颖丽,刘勇,刘秀华.重庆市主城区住房价格影响因子的空间异质性[J].资源科学,2017,39(2):335-345.
[18]苑韶峰,朱从谋,杨丽霞.长三角城市群各业用地价格的空间分布格局及影响因素[J].长江流域资源与环境,2017,26(10):1 538-1 546.
[19] Fischer M M,Getis A. Handbook of applied spatial analysis:software tools,methods and applications[M]. Berlin:Springer,2010:109-139.
[20]曹天邦,黄克龙,李剑波,等.基于GWR的南京市住宅地价空间分异及演变[J].地理研究,2013,32(12):2 324-2 333.
[21]吕萍,甄辉.基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J].经济地理,2010,30(3):472-478.
[22]常疆,廖秋芳,王良健.长沙市区地价的空间分布特征及其影响因素[J].地理研究,2011,30(10):1 901-1 909.
[23]王爱,陆林,包善驹.合肥市地价的空间格局与影响因素研究[J].经济地理,2016,36(10):84-92.
[24] Chun H,Chi S,Hwang B G. A Spatial Disaster Assessment Model of Social Resilience Based on Geographically Weighted Regression[J]. Sustainability,2017,9(12):1-16.
[25]任辉,吴群,朱新华.基于地物驱动要素的住宅地价空间模拟研究——以南京市为例[J].土壤,2014,46(2):373-378.
[2]任荣荣.中国城市土地价格的微观决定机理研究[M].南京:东南大学出版社,2012:3-5.
[3] Asabere P K. The determinants of land values in an African city:the case of Accra,Ghana[J]. Land Economics,1981,57(3):385-397.
[4] Asabere P K,Harvey B. Factors influencing the value of urban land:evidence from Halifax-Dartmouth,Canada[J]. Real Estate Economics,1985,13(4):361-377.
[5] Peiser R B. The determinants of nonresidential urban land values[J]. Journal of Urban Economics,1987,22(3):340-360.
[6] Yeates M H. Some factors affecting the spatial distribution of Chicago land values,1910—1960[J]. Economic Geography,1965,41(1):57-70.
[7]宋佳楠,金晓斌,唐健,等.中国城市地价水平及变化影响因素分析[J].地理学报,2011,66(8):1 045-1 054.
[8]高金龙,陈江龙,苏曦. 2001—2010年南京市区土地出让价格的影响因素[J].地理科学进展,2014,33(2):211-221.
[9] Hwang M, Quigley J M. Economic fundamentals in local housing markets:Evidence from U.S. metropolitan regions[J]. Journal of Regional Science,2006,46(3):425-453.
[10] Araby M M E. The role of the state in managing urban land supply and prices in Egypt[J]. Habitat International,2003,27(3):429-458.
[11]杜德斌,徐建刚.影响上海市地价空间分布的区位因子分析[J].地理学报,1997,52(5):403-411.
[12]吴巍,周生路,杨得志,等.跨江通道对滨江副城住宅地价增值效应的测算——以南京市浦口区为例[J].地理研究,2013,32(1):29-40.
[13]杨剩富,胡守庚,徐枫,等.特殊自然地物对城市住宅地价和房价的影响——以武汉市为例[J].资源科学,2016,38(4):738-749.
[14] Ahlfeldt G M,Maennig W. Impact of sports arenas on land values:Evidence from Berlin[J]. The Annals of Regional Science,2010,44(2):205-227.
[15]董冠鹏,张文忠,武文杰,等.北京城市住宅土地市场空间异质性模拟与预测[J].地理学报,2011,66(6):750-760.
[16]隋雪艳,吴巍,周生路,等.都市新区住宅地价空间异质性驱动因素研究——基于空间扩展模型和GWR模型的对比[J].地理科学,2015,35(6):683-689.
[17]李颖丽,刘勇,刘秀华.重庆市主城区住房价格影响因子的空间异质性[J].资源科学,2017,39(2):335-345.
[18]苑韶峰,朱从谋,杨丽霞.长三角城市群各业用地价格的空间分布格局及影响因素[J].长江流域资源与环境,2017,26(10):1 538-1 546.
[19] Fischer M M,Getis A. Handbook of applied spatial analysis:software tools,methods and applications[M]. Berlin:Springer,2010:109-139.
[20]曹天邦,黄克龙,李剑波,等.基于GWR的南京市住宅地价空间分异及演变[J].地理研究,2013,32(12):2 324-2 333.
[21]吕萍,甄辉.基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J].经济地理,2010,30(3):472-478.
[22]常疆,廖秋芳,王良健.长沙市区地价的空间分布特征及其影响因素[J].地理研究,2011,30(10):1 901-1 909.
[23]王爱,陆林,包善驹.合肥市地价的空间格局与影响因素研究[J].经济地理,2016,36(10):84-92.
[24] Chun H,Chi S,Hwang B G. A Spatial Disaster Assessment Model of Social Resilience Based on Geographically Weighted Regression[J]. Sustainability,2017,9(12):1-16.
[25]任辉,吴群,朱新华.基于地物驱动要素的住宅地价空间模拟研究——以南京市为例[J].土壤,2014,46(2):373-378.