# Spatial Distribution Pattern and Influencing Factors of Homestays in Chongqing, China
## 3 Results
### Spatial Heterogeneity of Factors Affecting the Spatial Distribution of Homestays  
It should be noted that the OLS model undertakes the function of eliminating invalid variables before running the GWR model, that is, observe the magnitude of the VIF value. The closer the VIF value is to 1, the lighter the multicollinearity, and vice versa. When the VIF value exceeds 10, there is co-linearity, the higher the value the more severe the co-linearity. Redundant variables with cointegrations should be excluded as they are not meaningful for geographically weighted regressions. After running the OLS model for central and non-central urban areas and eliminating the relevant variables, the fitting operation results of the model were obtained (Table 3). According to the calculation results, the influencing factors of the distribution of homestays in the central urban area mainly had six indicators: class A scenic spots, regional GDP, tertiary output value, per capita living and consumption expenditure of all residents, subway station density, and the number of colleges and universities. The factors affecting the distribution of homestays outside the central urban area mainly had six indicators: average altitude, forest coverage, air-quality rate, number of class A scenic spots, urbanization rate, and total number of tourists. Each index passed the test of significance levels of 1% or 5%. The smaller the AICc (Akaike information criterion) value of the regression analysis model, the better the fitting effect of the model and the more consistent with the data characteristics observed in the study. Comparing the AICc parameter values of the two models in Table 4, the AICc value of the GWR model in the central urban area was 38.76 less than that of the OLS model, and the AICc value of the GWR model in areas outside the central urban area was 32.27 less than that of the OLS model, indicating that the fitting effect of the GWR model was significantly better than that of the OLS model, reflecting the superiority of the local regression model. By comparing the action modes of various factors, it was observed that the regression coefficients of various variables were quite different, and the spatial heterogeneity was obvious (Table 5). From the average value of the absolute value of the regression coefficient, in the central urban area, the influence degrees of the output value of the tertiary industry, GDP, the density of subway stations, class A scenic spots, the number of colleges and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Names** & \begin{tabular}{c} **Observed Mean** \\ **Distance (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Expected Mean** \\ **Distance (m)** \\ \end{tabular} &
\begin{tabular}{c} **Nearest** \\ **Neighbor Ratio** \\ \end{tabular} & **Distribution Types** \\ \hline Chongqing & 922.9240 & 3747.5492 & 0.246274 & Agglomeration \\ Central urban area & 318.5892 & 970.6503 & 0.328222 & Agglomeration \\ Main city new District & 1541.8764 & 3940.5550 & 0.391284 & Agglomeration \\ The urban agglomeration of Wuling & 1706.9458 & 5171.3703 & 0.330076 & Agglomeration \\ Mountain Area & & & & \\ The urban agglomeration of Three Gorges & 3384.9553 & 7545.0662 & 0.448631 & Agglomeration \\ Reservoir Area & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Nearest-neighbor index of homestays in Chongqing and its districts. universities, and the per capita living consumption expenditure of all residents decrease in turn. In terms of the areas outside the central urban area, the impacts of average altitude, forest coverage, A-level scenic spots, urbanization rate, air-quality rate, and the total number of tourists decrease in turn. From the positive and negative values of the regression coefficient, in the central urban area, only class A scenic spots had positive and negative effects, and other variables had positive effects on the spatial layout of homestays.