## 4 Results
### Factors Impacting Industrial Location Selection  
#### 4.3.1 Analysis of the Factors Influencing Overall Industrial Location Selection  
Before the variables were introduced into the model, the Pearson correlation coefficient method was used to analyze and evaluate the correlations among nine independent variables capturing the factors that influence industrial location selection, and no high correlations were found among these factors. Assuming that the number of POI in each grid conformed to a Poisson distribution, Poisson regression analysis could be conducted using Stata software. For 2015 and 2020, because the results of the goodness-of-fit test do not satisfy the assumption of equal variance and mean value, the negative binomial regression model was used to revise the Poisson distribution regression model by Equations (2)-(5). The model results are shown in Table 4. R2 values are relatively low. They are meaningful values in this research because the direction and significance of the variables are referential. Agglomeration effects have a significant positive impact on the location selection of industries. As the correlation coefficient becomes larger, industries shows a preference for the original industrial agglomeration layout within the region, which further increases the level of agglomeration within the original regional industries and has a sustained impact \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **2015** & **2020** \\ \hline X1 & \(-\)0.07106 * & \(-\)0.05072 * \\ X2 & 3.1345 *** & 3.2754 *** \\ X3 & 0.1613 *** & 0.3019 *** \\ X4 & \(-\)0.6371 *** & \(-\)0.0593 \\ X5 & \(-\)0.4368 *** & \(-\)0.54245 *** \\ X6 & \(-\)0.2544 *** & \(-\)0.1775 *** \\ X7 & 0.1583 *** & 0.2748 *** \\ X8 & \(-\)0.16148 *** & \(-\)0.1815 *** \\ X9 & \(-\)0.2424 *** & \(-\)0.2397 *** \\ R2 & 0.189 & 0.274 \\ \hline \hline \end{tabular} Where *, *, and *** express significance at 10\(\%\), 5\(\%\), and 1\(\%\).  
\end{table} 
Table 4: Regression results of industrial overall location selection. Figure 5: Kernel density analysis of the accommodation and catering industries. on the spatial structure of the station area. With the development of HSR station areas, the influence of land rents on industrial location decreases. Distance from the HSR station has a significant impact on industrial location selection, and the coefficient on this variable is positive in 2015 and 2020. Contrary to the expected results, the coefficient shows that, with an increase in the distance from the HSR station, the number of industries also increases. For each unit of distance from the HSR station, the probability of an industry choosing that location increases by 3.01%. This is consistent with the previous research showing that there is fewer POI in the core area than in the area of influence or the peripheral area. Zhengzhou East bus station is located to the northeast of Zhengzhou East railway station, and its impact on industrial locations is similar. The coefficient of distance from a bus stop is negative, and the correlation coefficient increased in 2020, which indicates that industries prefer a local layout in which access to transportation via the bus station is convenient. With the development of the station area, this trend has increased. The distance from a subway station had a significant negative influence in 2015 and has no influence on the development of the station area. With the development of the HSR station area, greater distance from expressway entrances and exits reduces accessibility and reduces the probability of an industry choosing that location. The distance from the nearest shopping center has a significant negative impact on industrial location choices, and the correlation coefficient decreased in 2020, which indicates that the probability of choosing a given location decreases with increasing distance from a shopping center.