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
|
胡森林, 焦世泰, 张晓奇
|
Spatio-temporal evolution and influencing factors of China's tourism development: Based on the non-static spatial Markov chain model
|
HU Sen-lin, JIAO Shi-tai, ZHANG Xiao-qi
|
|
表4 动态空间马尔科夫链模型的回归结果(从旅游发展低/中水平类型转出情形)
|
Table 4 Regression results of dynamic spatial Markov chains (transfer out from low/medium level)
|
|
解释变量 | 低—中 | | 低—较高 | | 中—低 | | 中—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 | -0.372***(-3.069) | -0.346***(-2.907) | | -0.505 (-1.277) | -0.570 (-1.362) | | 0.075 (0.709) | 0.133(1.186) | | -0.145 (-1.351) | -0.156 (-1.453) | 星级酒店密度 | 1.918*** (3.106) | 1.775*** (2.821) | | 0.481 (0.141) | 1.514 (0.589) | | 0.001 (0.004) | -0.096 (-0.178) | | 0.606* (1.670) | 0.596* (1.688) | 旅游资源禀赋 | 0.065* (1.847) | 0.079** (2.410) | | 0.163(1.165) | 0.107 (0.829) | | 0.033 (0.827) | 0.010(0.288) | | 0.022 (0.590) | 0.027 (0.871) | 商业网密度 | -0.186** (-2.051) | -0.188** (-2.101) | | -0.510 (-0.833) | -0.525 (-0.893) | | 0.035 (0.775) | 0.055 (1.137) | | -0.160* (-1.765) | -0.168* (-1.846) | 路网密度 | 0.309 (0.749) | 0.478 (1.074) | | 1.917 (1.209) | 1.253 (0.691) | | -0.840**(-2.122) | -0.574 (-1.371) | | 0.250 (0.647) | 0.213 (0.547) | 产业结构 | 0.525 (0.237) | 0.311 (0.139) | | 19.254** (2.247) | 19.897**(2.273) | | -3.632* (-1.691) | -3.659*(-1.689) | | 0.972 (0.490) | 1.033 (0.521) | 对外开放度 | -0.097 (-0.121) | -0.250 (-0.313) | | 2.580* (1.707) | 3.092* (1.749) | | -0.347 (-0.345) | -0.501(-0.463) | | -1.101 (-0.916) | -1.067 (-0.915) | 互联网发展 | 5.914* (1.753) | 6.462* (1.924) | | -5.461 (-0.298) | -10.624(-0.504) | | 1.544 (0.884) | 1.766 (1.028) | | 3.789** (2.202) | 3.738** (2.183) | 经济发展水平 | 0.076 (0.477) | 0.079 (0.500) | | 1.175**(2.132) | 1.247** (2.125) | | -0.187*(-1.669) | -0.182*(-1.667) | | -0.051 (-0.577) | -0.051 (-0.574) | 空间因素 | 0.004 (0.379) | -26.281(-0.842) | | -0.041 (-0.587) | 68.651 (0.762) | | -0.029** (-2.066) | -81.651***(-2.644) | | 0.004 (0.453) | 11.389 (0.911) | intercept | -0.735 (-0.861) | -0.721 (-0.850) | | -10.876*** (-3.022) | -11.198***(-2.999) | | -0.458 (-0.525) | -0.788 (-0.861) | | -1.697* (-1.834) | -1.658* (-1.802) | Log-Likelihood | -224.24 | -223.93 | | -24.230 | -24.206 | | -222.67 | -220.17 | | -229.50 | -229.22 | Observations | 770 | 770 | | 699 | 699 | | 781 | 781 | | 713 | 713 |
|
|
|