M the statistical information of the model test, the spatial error
M the statistical data from the model test, the spatial error model has better interpretation within the evaluation of urban human settlements.Table 7. Estimation benefits of spatial error evaluation model for urban human settlements traits. Variables lnX1 lnX2 lnX3 lnX4 lnX5 lnX6 lnX7 spat.aut. teta R2 Sigma2 log-likelihood LMlag R-LMlag LMerror R-LMerror Fixed Effect Coefficient t-Stat Probability 0.019576 0.707617 0.787766 0.000433 0 0.Linuron web 004207 0.016828 0 Coefficient Random Effect t-Stat Probability 0.280624 0.849149 0.24077 0.75485 0.676432 0 0.040944 0-0.002480 0 -0.000016 -0.000020 -0.017970 0.000011 -0.002130 0.988972 –2.334380 -0.375060 -0.269210 -3.519320 -8.161420 2.862237 -2.390450 334.0992 -0.011800 0.0025 2604.1892 3702.4477 347.4313 4933.6623 1578.-0.001050 0 0.000049 -0.000003 -0.002380 0.00029 0.00605 0.996368 88.-1.078920 -0.190200 1.173063 -0.312250 -0.417340 16.3351 2.044096 23658.92 13.41322 0.8881 0.0009 2620.315 1191.641 1250.0513 0.0002 58.The LM test values beneath the spatial error model of fixed effect and random effect are optimistic, and the majority of them pass the 10 significance test, indicating that the outcomes are considerable. Autocorrelation coefficient is positive along with the estimated worth of variable X1 is negative, which all pass the 1 significance probability test. The coefficient of Wdep.var of fixed impact shows that the spillover effect is clear, or the spillover of this city to other cities is clear. From the adjusted R2 , Sigma2 , log-likelihood, the fixed impact spatial error panel model is significantly better than the random effect spatial lag panel model. The spatial autoregressive coefficient in the fixed impact lag model is significantly significantly less than the sub regression coefficient on the random effect lag model. The fixed impact of the spatial error model shows that urban human settlements have a tendency to have an effect on the city’s scientific and technological investment, KU-0060648 Autophagy economic improvement, urbanization, and urban natural advantages. Even so, for the upgrading of industrial structure, it has small influence. The results of random impact show that the driving mechanism includes a terrific influence around the city’s scientific and technological investment, economic development, urbanization, and education. For the urban all-natural benefits on the city, the influence is compact. In order to comprehensively and accurately analyze the spatial effects of urban human settlements, the fixed effect and random impact are analyzed by using the Spatial Durbin Model (SDM). It includes spatial weights of explanatory variables and explained variables (Table 8). When testing its spatial effect, the spatial auto-regressive coefficient Wdep.var. of SDM is substantially good when the significance level is ten (0.993996). Most of the spatial lag coefficients of dependent variables are adverse and the majority of them fail to pass the significance test at the 1 level. Below the random impact, the spatial auto-regressive coefficient Wdep.var. of SDM is considerably good (0.967977) when the significance level is ten . A lot of the spatial lag coefficients from the dependent variables are damaging and most of them fail to pass the significance test of the 1 level. This indicates thatLand 2021, ten,16 ofthere is no clear spatial correlation around the human settlements in different regions. That may be, the degree of human settlements within a region will not depend on the degree of that in adjacent regions and its explanatory variables. For both fixed impact an.
