Ate encourage neighboring encourage neighboring industrial facilities. On the whole, six
Ate encourage neighboring encourage neighboring commercial facilities. On the complete, six urban districts have de commercial facilities. On the whole, six urban districts have developed unique clustering veloped unique clustering characteristics of their cultural and entertainment facilities. characteristics of their cultural and entertainment facilities. The C6 Ceramide Inducer pattern of one particular core and also the pattern of 1 core and a number of secondary cores happen to be UCB-5307 Formula formed. The firstlevel multiple secondary cores have been formed. The first-level hot spots are mainly distributed hot spots are mainly distributed in the eastern half of the city, and it covers Dongdan, in the eastern half with the city, and it covers Dongdan, Sanlitun, CBD, Shuangjing and also other Sanlitun, CBD, Shuangjing and also other places in the East Second Ring Road for the East areas from the East Second Ring Road to the East Third Ring Road; the second-level hot Third Ring Road; the secondlevel hot spots tend to be distributed along the ring line; and spots are inclined to be distributed along the ring line; plus the third level hot spots are scattered the third level hot spots are scattered inside the Fourth Ring Road. within the Fourth Ring Road.Figure 8. The outcome of hierarchical clustering of cultural and entertainment facilities in the six Figure 8. The outcome of hierarchical clustering of cultural and entertainment facilities in the six urban districts in Beijing. urban districts in Beijing.3.3. Elements Influencing the Distribution of Facilities inside the Six Urban Districts Following the aforementioned multi-collinearity test, our independent variables have develop into eight. Immediately after running OLS regression and spatial lag regression in GeoDa 1.6.7 (created by Luc Anselin), we obtain the following final results (Table 6). Right here, the R2 for the spatial lag regression equation is 0.72, demonstrating a very good degree of match. This value improved when in comparison to the R2 of 0.71 from the OLS regression. Simultaneously, the value in the SC (Schwarz Criteria) is much less than 0 and has declined, therefore demonstrating that the regression model is additional convincing. Table 6 shows that p-values of housing rent, the distance to the nearest scenic spot, the monetary insurance coverage institution density, the safety company density and the creating density in streets and towns are all much less thanSustainability 2021, 13,16 of0.05; consequently, they all pass the significance test at the 95 self-assurance interval. In the regression coefficients, we come across that the road network density, housing rent, financial insurance institution density and building density are positively correlated with the density of facilities. Comparing the numerous coefficients, we can see that the degree of influence decreases in the following order: financial insurance coverage institutions density building density security organization density housing rent distance to nearest scenic spot. The distance to the nearest scenic spot and security company density are two negatively correlated variables.Table 6. The regression outcome of spatial lag model. Variable POI_den continual luwang_den zhugandao housing rent fengjingqu jinrong_den zhengquan_den louyudasha_den landprice Regression Coefficient 0.164 0.033 0.119 -0.032 0.276 -0.144 0.410 -0.341 0.393 -0.064 Common Error 0.095 0.050 0.073 0.064 0.072 0.063 0.124 0.118 0.098 0.106 Z Value 1.720 0.658 1.631 -0.496 3.845 -2.292 three.314 -2.886 3.999 -0.603 Probability 0.085 0.511 0.103 0.620 0.000 0.022 0.001 0.004 0.000 0.