消费升级视角下城市生活服务空间的演变及影响因素研究——以武汉菜市场为例
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甘依霖, 朱媛媛, 罗静, 高喆
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Spatiotemporal variation and influencing factors of urban consumer service space in the consumption upgrading era: A case study of Wuhan food markets
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GAN Yilin, ZHU Yuanyuan, LUO Jing, GAO Zhe
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表2 武汉市菜市场空间分布负二项回归模型估计结果
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Tab.2 Regression results of negative binomial model for food markets in Wuhan City
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变量 | 模型1: | 模型2: | 模型3: | 模型4: | 模型5: | 模型6: | 全部菜市场 | 农贸市场 | 生鲜超市 | 大卖场生鲜部 | 社区蔬菜便民店 | 2020年新增菜市场 | POP | 0.484*** | 0.315** | 0.471** | 0.905*** | 0.718*** | 0.606** | PRICE | 0.117** | 0.036 | 0.094 | 0.244** | 0.342*** | 0.048 | ROAD | -0.029 | -0.094*** | -0.000 | -0.073 | -0.034 | -0.085 | BUS | 0.007*** | -0.002 | 0.012*** | 0.002 | 0.003 | 0.008* | METRO | 0.036 | 0.005 | 0.032 | -0.041 | 0.101 | 0.094 | CIRCLE | 0.307*** | 0.866 | 0.372** | 0.025 | 0.377* | 0.196 | SCHOOL | 0.001 | -0.002* | 0.002 | -0.001 | 0.001 | 0.001 | HOSPITAL | 0.001 | 0.038*** | -0.005 | 0.024 | -0.034 | 0.006 | CULTURE | -0.017 | 0.002 | -0.025 | 0.004 | -0.008 | -0.022 | MALL | 0.244* | 0.170 | 0.300 | 0.638*** | -0.079 | 0.454** | BASIC | 0.165*** | 0.136*** | 0.160*** | 0.122*** | 0.177*** | 0.110*** | 常数项 | -0.985** | -1.102*** | -1.724*** | -5.328*** | -4.210*** | -2.489*** | α | 0.404 | <0.001 | 0.829 | <0.001 | 0.607 | 0.969 | Log likelihood | -500.351 | -258.722 | -430.794 | -136.890 | -217.642 | -292.891 |
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