# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 5 Result and Discussion
### Results of Food Balance Analysis
#### 5.3.2 Geographically Weighted Regression Analysis  
Using food accessibility withing a 20 min walk as a spatial range, the present study used a geographically weighted regression of the valid variables retained by OLS using a broadband of 1200 m. Table 3 shows the GWR model fits (R2 values) for various food items. The results indicated that vegetables, fruits, seafood, meat, and milk show significant aggregation in different areas. Since walking 1200 m takes approximately 20 min, the choice of 1200 m as the bandwidth of GWR was beneficial to local regression. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Food Type** & **Variable** & **Coefficient** & \(p\)**-Value** & **VIFc** & **Include/Omit** & **Adjust R2** & **Koenker Test** & **AICc** \\ \hline \multirow{3}{*}{Grian} & Intercept & 0.000657 & 0.882179 & — & & & \\  & YEAR & \(-0.000002\) & 0.002599 * & 1.002016 & include & 0.001468 & 0.124751 & \(-\)33,027.596035 \\  & LGRPICE & 0.000367 & 0.429808 & 1.002016 & omit & & & \\ \hline \multirow{3}{*}{Vegetable} & Intercept & 0.001154 & 0.000000 * & — & & & \\  & YEAR & 0 & 0.000000 * & 1.002016 & Include & 0.025729 & 0.000000 * & \(-\)71,587.891091 \\  & LGRPICE & \(-0.000084\) & 0.000000 * & 1.002016 & Include & & & \\ \hline \multirow{3}{*}{Fruit} & Intercept & 0.001757 & 0.000000 * & — & & & \\  & YEAR & \(-0.000000\) & 0.012975 * & 1.002016 & include & 0.010771 & 0.108091 & \(-\)65,365.726053 \\  & LGRPICE & \(-0.000139\) & 0.000000 * & 1.002016 & include & & & \\ \hline \multirow{3}{*}{Seafood} & Intercept & 0.000967 & 0.000000 * & — & & & \\  & YEAR & \(-0.000000\) & 0.0007975 * & 1.002016 & Include & 0.014320 & 0.004605 * & \(-\)72,451.277216 \\  & LGRPICE & \(-0.000078\) & 0.000000 * & 1.002016 & Include & & & \\ \hline \multirow{3}{*}{meat} & Intercept & 0.000664 & 0.00000 * & — & & & \\  & YEAR & \(-0.000000\) & 0.000514 * & 1.002016 & include & 0.017188 & 0.000855 * & \(-\)77,181.156384 \\ \cline{1-1}  & LGRPICE & \(-0.000054\) & 0.000000 * & 1.002016 & include & & & \\ \hline \multirow{3}{*}{Milk} & Intercept & 0.000847 & 0.000000 * & — & & & \\ \cline{1-1}  & YEAR & \(-0.00000\) & 0.587469 & 1.002016 & omit & 0.003862 & 0.640117 & \(-\)68,671.957804 \\ \cline{1-1}  & LGRPICE & \(-0.000066\) & 0.000006 * & 1.002016 & include & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of ordinary least squares (OLS) regression analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Food Type** & **Variable** & **Bandwidth** & **AICc** & **R2** & **Adj.R2** \\ \hline Grain & Year & 1200 & 12,899.362 & 0.375 & 0.342 \\ Vegetable & Lgprice, Year & 1200 & 8779.114 & 0.990 & 0.990 \\ Fruit & Lgprice, Year & 1200 & \(-4477.295\) & 0.978 & 0.977 \\ Seafood & Lgprice, Year & 1200 & \(-6846.700\) & 0.986 & 0.985 \\ Meat & Lgprice, Year & 1200 & \(-11,478.409\) & 0.994 & 0.994 \\ Milk & Lgprice & 1200 & 1665.625 & 0.926 & 0.923 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of geographically weighted regression analysis. GWR obtained an overall good fit that was far superior to that of OLS. The coeffcets of all variables with a \(p<10\%\) were plotted, following which they were divided into two categories: greater than 0 and less than 0. For house price, a coefted of house price (beta_igprice) value greater than 0 represents increasing food accessibility with increasing house price, whereas a beta_igprice value less than 0 represents the opposite. For house age, a coefted (beta_Year) value greater than 0 represents decreasing food accessibility with increasing age of the neighborhood, whereas a beta_Year value less than 0 represents the opposite. As shown in Figure 12a-e, the accessibility values of vegetables, fruits, seafood, meat, and milk increased with increasing house prices in some old urban areas. Furthermore, lower food accessibility corresponded with increased housing prices in some new development areas far from the center of the city (University Town, Airport New District and Northern New Town, etc.).