土壤 (Soils), 2014, 46(2): 373 378 1 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 1, 2, 3 (1 410128 2 210095 3 210098) GIS F301.4 [1] Bruechner [2] Capozza Sick [3] 20 90 [4 6] [7 9] GIS 1 1.1 GIS [10] [11] (12QN57) (09&ZD046) (71073082 71203054) (1983 )E-mail: renhui1204@163.com
374 46 [12] 1 Fig. 1 图 1 驱动因素综合作用量化过程 The quantification process of comprehensive effect of driving factors 2000 2011 Kriging GIS 1.2 9 2000 1 1 2011 6 30 282 1 表 1 Table 1 南京市住宅地价空间分异地物驱动因素表 The driving factors of space differentiation of residential land price at Nanjing 2 2.1 2011 6 30 Kriging ( 2) [13] GIS 50 m 50 m 2.2 3 1 500 750 500 250
2 375 图 2 2011 年住宅地价 ( 元 /m 2 )Kriging 插值 Fig. 2 The interpolation figure of Kriging of residential land price in 2011 GIS 1 500 0 2.3 Z Z*=Z/Z max 2.4 [14] GIS Ya = A 0 + A 1 X 1 + A 2 X 2 + A 3 X 3 + A 4 X 4 + A 5 X 5 + A 6 X 6 + A 7 X 7 + A 8 X 8 + A 9 X 9 + A 10 X 10 + A 11 X 11 + e (1) Ya A 0 A 1 A 11 e A 0 A 1 A 11 Ya = a 0 +a 1 X 1 +a 2 X 2 +a 3 X 3 +a 4 X 4 +a 5 X 5 +a 6 X 6 +a 7 X 7 + a 8 X 8 +a 9 X 9 +a 10 X 10 +a 11 X 11 +e (2) a 0 a 1 a 11 [14] Ya X 1 X 2 X 11 2.5 Eviews6.0 R Y() X i () 0 ~ 1 R R R Y X i
376 46 [15] 2 2 R 2 R 2 F D.W R 2 (0.661 5) F = 245.069 2 P = 0.000 0 表 2 多元回归分析结果 Table 2 The analysis results of multiple regression R R 2 R 2 D.W F P 1 0.815 0 0.664 2 0.661 5 0.130 7 1.549 3 245.069 2 0.000 0 3 3 P 0.01 P 0.05 3 t P 0.05 表 3 多元回归分析系数 ( 标准化系数回归 ) Table 3 The analysis coefficient of multiple regression (standardized coefficient regression) T P C 0.173 8 0.005 3 33.006 4 0.000 0 X 1 () 0.526 9 0.031 3 16.839 4 0.000 0 X 2 () 0.048 0 0.017 9 2.680 8 0.007 4 X 3 () 0.074 2 0.016 1 4.604 5 0.000 0 X 4 () 0.036 2 0.015 8 2.295 5 0.021 8 X 5 () 0.033 5 0.019 5 1.719 4 0.015 7 X 6 () 0.106 3 0.012 5 8.526 6 0.000 0 X 7 () 0.057 7 0.023 2 2.486 4 0.013 0 X 8 () 0.165 2 0.027 7 5.964 7 0.000 0 X 9 () 0.011 7 0.008 0 1.465 1 0.043 1 X 10 () 0.091 8 0.008 2 11.164 1 0.000 0 X 11 ( ) 0.086 6 0.008 4 10.376 2 0.000 0 2.6 ArcGIS (spatial analyst) 3 3 (1) 1 691 ~ 10 681 /m 2 (2) (3) Kriging ( 2) Alonso [16]
2 377 图 3 2011 年住宅地价 ( 元 /m 2 ) 空间模拟图 Fig. 3 The space simulation of residential land price in 2011 (4) (5) Kriging ( 2) 4
378 46 [1],,,,,,. [J]., 2009, 64(10): 1214 1220 [2] Bruechner JK. Growth control and land values in a open city[j]. Land Economics, 1990, 66(3): 283 293 [3] Capozza DR, Sick GA. The risk structure of land market[j]. Journal of Urban Economics, 1994, 35(3): 297 319 [4],. [J]., 2007, 29(4): 25 32 [5],,,. [J]., 2011, 31(7): 823 828 [6],,,,,,. [J]., 2011, 66(8): 1 045 1 054 [7],,,,. [J]., 2004, 26(1): 14 21 [8],. [J]., 2007, 40(1): 90 94 [9],,,,,. [J]., 2008, 30(4): 591 597 [10],,. [J]., 2007, 18(3): 356 360 [11],,,,,. [J]., 2010, 29(11): 1 981 1991 [12]. [D]. :, 2008 [13],. ESDA [J]., 2011, 31(5): 760 765 [14],. [J].,1997, 52(5): 403 409 [15],. [J]., 2006, 61(6): 604 612 [16],. [J]., 2007(2): 35 42 Space Simulation of the Residential Land Price Study Based on Feature-driven Elements A Case Study of Nanjing City REN Hui 1, WU Qun 2, ZHU Xin-hua 3 (1 College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China; 2 Public Administration College, Nanjing Agricultural University, Nanjing 210095, China; 3 Public Administration College, Hohai University, Nanjing 210098, China) Abstract: It is worthy of further exploration to use spatial modeling in quantitative simulation analysis of the land price space. This paper took residential land in Nanjing as the research object, selected feature driving factors affecting the spatial distribution of residential land, based on spatial sampling and multiple regression models, established space factor and residential land price multivariate regression model, used raster GIS to simulate space of urban residential land price distribution. The results proved good simulation results achieved, which could reflect the real size of the impact of various types of features elements on the residential land price space, especially the rail traffic impact. Therefore, the simulation study of residential land price space could provide the reference value efficiently to improve the value of urban land and achieve urban land intensive and efficient use. Key words: Features elements, Land price space, Simulation, Nanjing City