# Spatial Distribution Pattern and Influencing Factors of Homestays in Chongqing, China
## 2 Materials and Methods
### Methods
#### 2.2.3 Preparation of Variables and Model Building
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Names** & **Paraphrase** \\ \hline Independent variable & Number of homestays & Number of homestays in districts \\  & & and counties \\ \hline \multirow{4}{*}{Natural geographical conditions} & Average altitude & Average altitude of the region \\  & Forest-cover rate & Regional forest cover \\  & Air-quality rate & Percentage of days with good air quality in \\  & A-level scenic spot & Number of A-level scenic spots in the region \\ \hline \multirow{4}{*}{Regional economic conditions} & GDP & Regional annual gross domestic product \\  & Total tourism revenue & Total annual revenue of regional tourism \\  & Output value of primary industry & Regional agricultural annual gross product \\  & Output value of tertiary industry & Annual total revenue of regional \\  & Output value of tertiary industry & service industry \\  & Per capita disposable income of residents & Per capita disposable income of \\  & & local residents \\  & Residents’ per capita living & Per capita living consumption expenditure of \\  & consumption expenditure & regional residents \\  & Accommodation and catering & Regional accommodation and catering \\  & industry turnover & industry turnover \\ \hline \multirow{4}{*}{Social development conditions} & Permanent residents & Regional resident population \\  & Urbanization rate & Urban population/total population \\  & Road network density & Total regional highway mileage/area \\ \cline{1-1}  & Total number of visitors & The total number of tourists received by the \\ \cline{1-1}  & Metro station density & Number of metro stations/area \\ \cline{1-1}  & College density & Number of regional colleges and universities \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Influencing factors of homestays in Chongqing. Taking the county as the basic research unit, in order to break the limitations of the traditional linear regression model in the spatial characteristics of independent variables and their 'global' estimations, this paper comprehensively used the ordinary least squares method (OLS) and geographically weighted regression (GWR) model to calculate the impacts of the factors above on the spatial layout of homestays in the global and different regions, so as to more intuitively reflect the spatial imbalance and trend change in the driving mechanism of homestay spatial layouts [50]. The regression analysis model is as follows:  
\[y_{i}=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\varepsilon_{i} \tag{4}\]  
In Formula (4), the classical linear regression model is usually composed of a random error term \(\varepsilon_{i}\) and a set of parameters, \(\beta_{0}\) and \(\beta_{i}\); \(i\) is the estimated value of equation parameters obtained by the minimum error smoothing method. It should be noted that the OLS model undertakes the function of eliminating invalid variables when running the GWR model, that is, the VIF value of the variable in the operation's result should be less than 7.5; otherwise, the variable has a collinearity problem. The research conducted on spatial heterogeneity adopts the geographically weighted regression model (GWR), which is essentially an extension of the general linear regression global model. It regresses each observation point, generates the parameters of the corresponding region, and then embeds the geographical location of the data into the regression parameters to form a local rather than global parameter estimation [51].