中国城市旅游发展的时空演化及影响因素——基于动态空间马尔科夫链模型的分析
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胡森林, 焦世泰, 张晓奇
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Spatio-temporal evolution and influencing factors of China's tourism development: Based on the non-static spatial Markov chain model
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HU Sen-lin, JIAO Shi-tai, ZHANG Xiao-qi
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表5 动态空间马尔可夫链模型的回归结果(从旅游发展高/较高水平类型转出情形)
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Table 5 Regression results of dynamic spatial Markov chains (transfer out from high level)
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解释变量 | 较高—低 | | 较高—高 | | 高—中 | | 高—较高 | 二进制 | 反距离平方 | 二进制 | 反距离平方 | 二进制 | 反距离平方 | 二进制 | 反距离平方 | 市场化水平 | 1.181*** (2.864) | 1.013*** (2.703) | | -0.020 (-0.182) | -0.015(-0.137) | | 1.380**(2.070) | 0.629* (1.745) | | 0.193* (1.807) | 0.199* (1.970) | 星级酒店密度 | -16.526**(-2.247) | -13.956** (-2.199) | | 0.650**(1.956) | 0.645* (1.930) | | -2.220(-0.730) | -2.278(-0.633) | | -0.451 (-1.354) | -0.443 (-1.362) | 旅游资源禀赋 | -0.288(-0.986) | -0.341 (-1.282) | | -0.001 (-0.071) | 0.006 (0.270) | | -2.964**(-2.282) | -2.646** (-2.117) | | -0.001 (-0.042) | -0.001 (-0.043) | 商业网密度 | 0.036(0.077) | 0.092 (0.777) | | -0.007(-0.376) | -0.008(-0.380) | | 0.029 (0.022) | 0.186 (0.243) | | 0.021(1.238) | 0.021 (1.225) | 路网密度 | 2.898(1.501) | 2.738 (1.421) | | 0.184(0.452) | 0.120 (0.277) | | -0.252 (-0.118) | -0.045 (-0.021) | | 0.367(0.943) | 0.360 (0.904) | 产业结构 | 6.026 (0.624) | 7.878 (0.847) | | -0.035(-0.018) | -0.045(-0.023) | | -22.208* (-1.723) | -16.925 (-1.632) | | -1.206 (-0.710) | -1.225 (-0.725) | 对外开放度 | 1.648 (0.837) | 2.123 (1.249) | | 0.064 (0.089) | -0.005 (-0.007) | | -3.834 (-0.680) | -0.512 (-0.232) | | -0.481 (-0.803) | -0.472 (-0.808) | 互联网发展 | -39.805*(-1.683) | -48.836** (-2.208) | | -4.740(-1.442) | -4.313(-1.372) | | 25.086**(1.957) | 13.765 (1.123) | | 1.600(1.196) | 1.571 (1.190) | 经济发展水平 | 0.160(0.312) | 0.192 (0.550) | | 0.032(0.274) | 0.019 (0.168) | | -4.400* (-1.760) | -2.874* (-1.686) | | -0.087 (-0.727) | -0.088 (-0.733) | 空间因素 | -0.187* (-1.778) | -76.882(-0.671) | | 0.009(0.961) | 8.284 (0.579) | | -0.206 (-1.565) | 64.542 (0.695) | | -0.0004 (-0.038) | 1.519 (0.087) | intercept | -10.064**(-2.115) | -10.905** (-2.265) | | -2.475**(-2.380) | -2.394**(-2.295) | | 5.204(1.011) | 3.365 (0.839) | | -0.334 (-0.388) | -0.299 (-0.344) | Log-Likelihood | -26.096 | -27.876 | | -193.99 | -194.26 | | -12.850 | -14.462 | | -191.98 | -191.97 | Observations | 781 | 781 | | 703 | 703 | | 767 | 767 | | 762 | 762 |
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