不确定性视角下的中国省际人口迁移机制分析
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蒲英霞, 武振伟, 葛莹, 孔繁花
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Analyzing the spatial mechanism of interprovincial migration in China under uncertainty
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PU Yingxia, WU Zhenwei, GE Ying, KONG Fanhua
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表2 空间OD模型中后验概率最大的10个模型
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Tab. 2 The 10 highest posterior probability among spatial Origin-Destination (OD) models
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| 变量 | 后验概率模型 | 后验包含概率 | | 10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | | O_POP | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.924 | | O_GDP | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.925 | | O_Wage | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0.456 | | O_Urban | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0.797 | | O_HB | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0.696 | | O_Edu | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0.459 | | O_Flow | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.238 | | D_POP | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0.459 | | D_GDP | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0.446 | | D_Wage | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0.839 | | D_Urban | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0.824 | | D_HB | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.205 | | D_Edu | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.923 | | D_Flow | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.962 | | Distance | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.926 | | 后验概率 | 0.017 | 0.019 | 0.019 | 0.019 | 0.026 | 0.049 | 0.058 | 0.063 | 0.095 | 0.405 | |
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