Acta Geographica Sinica ›› 2015, Vol. 70 ›› Issue (6): 893-905.doi: 10.11821/dlxb201506004
• Orginal Article • Previous Articles Next Articles
Shijun WANG1(), Feilong HAO1(
), Lili JIANG2
Received:
2014-06-16
Revised:
2015-03-18
Online:
2015-06-20
Published:
2015-06-20
Supported by:
Shijun WANG, Feilong HAO, Lili JIANG. Locations and their determinants of large-scale commercial sites in Changchun, China[J].Acta Geographica Sinica, 2015, 70(6): 893-905.
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Tab. 1
NNI of large-scale commercial sites in Changchun
业态类型 | 样本数 | 平均最近邻距离(m) | 期望平均最近邻距离(m) | 最近邻指数(NNI) | Z检验值 | 显著性水平 |
---|---|---|---|---|---|---|
大型商业网点 | 343 | 306.66 | 535.50 | 0.57 | -15.01 | 1% |
专业店 | 51 | 579.00 | 719.47 | 0.80 | -2.67 | 1% |
专卖店 | 74 | 341.87 | 719.71 | 0.43 | -9.35 | 1% |
大型商场 | 57 | 493.94 | 970.24 | 0.51 | -7.09 | 1% |
大型超市 | 43 | 998.15 | 928.99 | 1.07 | 0.93 | - |
家居建材商店 | 17 | 2231.30 | 1721.46 | 1.30 | 2.34 | 5% |
综合交易市场 | 101 | 697.40 | 873.30 | 0.80 | -3.87 | 1% |
Tab. 2
Determinants and definition of large-scale commercial sites' spatial distribution
变量 | 编码 | 含义 | 样本均值(标准差) |
---|---|---|---|
因变量 | |||
商业网点区位 | Y | 1=一环内,2=一环与二环间,3=二环与三环间,4=三环以外 | 2.40 (1.080) |
内部变量 | |||
业态类型 | X1 | 1=专业店,2=专卖店,3=大型商场,4=大型超市,5=家居建材商店,6=综合交易市场 | 3.83 (1.934) |
建筑规模 | X2 | 商业网点的建筑面积(m2) | 2.95E4 (6.97E4) |
外部变量 | |||
土地价格 | X3 | 商业土地基准地价1=Ⅰ级,2=Ⅱ级,3=Ⅲ级,4=Ⅳ级,5=Ⅴ级, 6=Ⅵ级 | 3.46 (1.388) |
交通通达性 | X4 | 商业网点所在社区路网密度(km/km2) 1=0~3,2=3~5.4,3=5.4~7.1, 4=7.1~15,5=15以上 | 3.06 (1.070) |
人口规模 | X5 | 商业网点所在社区人口密度(万人/km2) 1=0~0.85,2=0.85~4.25, 3=4.25~5.8,4=5.8以上 | 1.8 (0.854) |
商圈环境 | X6 | 以每家商业网点为中心2 km范围内商业中心的最高等级 1=社区级, 2=区域级,3=市级 | 1.48 (0.675) |
集聚特征 | X7 | 以每家商业网点为中心1 km范围内商业网点的数量 | 3.04 (3.969) |
Tab. 3
Estimation results of ordinal logistic model
因变量/类型 | 基本模型 | 专业店 | 专卖店 | 大型商场 | 大型超市 | 家居建材商店 | 综合交易市场 |
---|---|---|---|---|---|---|---|
X1 | -0.091*** | - | - | - | - | - | - |
X2 | 1.43E-06 | 6.08E-08 | 0.000*** | 1.09E-06 | 8.24E-05* | 4.17E-06 | 2.19E-06 |
X3=1 | -3.745*** | 2.157 | -8.822 | -6.563 | -4.170 | - | -1.177 |
X3=2 | -4.084*** | 0.775 | - | -6.533 | -3.999 | - | -2.563*** |
X3=3 | -2.042*** | -1.067 | -2.679*** | -5.337 | -1.675 | 4.361* | -1.595** |
X3=4 | -1.654*** | -0.181 | -1.277** | -5.347 | -1.297 | 2.205 | -0.650 |
X3=5 | -1.503*** | 0a | -2.824*** | -5.338 | 0a | 4.344* | -0.192 |
X3=6 | 0a | - | 0a | 0a | - | 0a | 0a |
X4=1 | 2.693*** | -0.316 | 1.150 | 2.060* | 1.765 | 6.288* | 13.525 |
X4=2 | 1.937*** | -3.984*** | -2.295 | -0.582 | 0.072 | 3.585 | 8.099*** |
X4=3 | 1.769*** | -2.337** | -0.250 | 0.452 | 1.828 | 1.623 | 7.792*** |
X4=4 | 0.597 | -0.469 | -0.034 | 0.156 | 0.091 | 0a | 7.319 |
X4=5 | 0a | 0a | 0a | 0a | 0a | - | 0a |
X5=1 | 2.423*** | 1.318 | 1.345 | 0.760 | 3.767 | 2.661 | -0.177 |
X5=2 | 1.815** | -1.017 | 3.014 | 1.201 | 1.745 | 0.048 | -0.228 |
X5=3 | 0.357 | -2.384* | 0a | -0.239** | 0.883 | 0a | 0.081 |
X5=4 | 0a | 0a | - | 0a | 0a | - | 0a |
X6=1 | -1.896** | 1.264 | 3.444*** | 1.241 | 0.110 | 0.273 | 6.579 |
X6=2 | 0.668 | 1.121 | 0a | -0.386* | -0.697 | 0a | 6.019 |
X6=3 | 0a | 0a | - | 0a | 0a | - | 0a |
X7 | 0.170*** | -0.425** | 0.171*** | 0.015 | -0.009 | -0.043 | 0.225* |
样本数量 | 343 | 51 | 74 | 57 | 43 | 17 | 101 |
Deviance | 546.892 (1.000) | 52.714 (1.000) | 61.492 (1.000) | 57.229 (1.000) | 21.701 (0.892) | 11.380 (0.955) | 196.555 (1.000) |
Cox-Snell R2 | 0.647 | 0.636 | 0.870 | 0.908 | 0.874 | 0.879 | 0.907 |
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