## 2 Materials and Methods
### Moran's I Statistical Test  
Developed by [PERSON] [65], Moran's I test is a statistical tool used to assess spatial autocorrelation in regression residuals by measuring their correlation with neighboring observations. It is used to confirm the presence of spatial correlation in a dataset. If the null hypothesis of no spatial autocorrelation is rejected, the OLS estimates are consid \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Types** & **Variables** & **Descriptions** & **Years** & **Sources** & **Expected Sign** & **References** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Independent \\ Variables \\ \end{tabular} } & \multirow{4}{*}{_Gross Provincial_} & A variable that & & & & \\  & represents economic & & & & \\  & & growth per capita in & & National & & \\  & _Product per capita_ & each province; reflects & & & & \\  & (_GPPc_) & the level of economic & & & & \\  & & output per person in & & Development & & \\  & & that province & & Council (NESDC) & & & \\  & & (baht/person) & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} _Population density_ \\ (_Pop\_dens_) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} \begin{tabular}{c} A variable that \\ represents a \\ province’s population density, indicates how \\ many people live per \\ unit of area in that \\ province (unit/km\({}^{2}\)) \\ \end{tabular} } & \multirow{4}{*}{2015–2022} & Office of the & & & \\  & & & & National & \\  & & province’s population density, indicates how & Economic and & & & \\  & & density, indicates how & Social & + & [33, 36, 41] \\  & & many people live per & & Development & & \\  & & unit of area in that & & Council (NESDC) & & \\  & & province (unit/km\({}^{2}\)) & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} _Read density_ \\ (_Readens_) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} A variable that \\ represents a \\ province’s land \\ surface temperature at night; indicates \\ how high the surface temperature is during \\ nighttime in that \\ province (Celcius) \\ \end{tabular} } & \multirow{4}{*}{2015–2022} & \multirow{4}{*}{Geofabrik} & \multirow{4}{*}{+} & [30, 52, 53] \\  & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Instrumental \\ Variables \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} \begin{tabular}{c} A variable that shows \\ the average annual \\ rainfall in a province \\ (millimeters/year) \\ \end{tabular} } & \multirow{4}{*}{2015–2022} & \multirow{4}{*}{\begin{tabular}{c} CHIRPS Daily: \\ Climate Hazards \\ Center InfraRed \\ Precipitation \\ With Station Data \\ (Version 2.0 \\ Final); bands: \\ precipitation} \\ \end{tabular} & \multirow{4}{*}{2015–2022} & CHIRPS Daily: & & \\  & & & Climate Hazards & \\  & & & Center InfraRed & \\  & & & Precipitation & \\  & & & With Station Data & [54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64] \\  & & & & (Version 2.0 \\  & & & Final); bands: & \\  & & precipitation & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} _Windspeed_ \\ (_Windspeed_) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} A variable that shows \\ the average annual \\ wind speed and \\ direction in a \\ province (m/s) \\ \end{tabular} } & \multirow{4}{*}{2015–2022} & GLDAS-2.1: & & \\  & & & Global Land Data & \\ \cline{1-1}  & & & Assimilation & [54, 55, 56, 58, 60, 63, 64] \\ \cline{1-1}  & & & System; bands: & & \\ \cline{1-1}  & & Wind\_f\_inst & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)ered misspecified due to the omission of spatial relationships. Equation (2) presents the mathematical formulation of [PERSON]'s I test. \[I=\frac{\left[\sum_{i}\sum_{j}w_{ij}(x_{i}-\overline{x})(x_{j}-\overline{x}) \right]}{\sum_{i}\sum_{j}w_{ij}\left(x_{i}-\overline{x}\right)^{2}} \tag{2}\]  
Here, _Wi,j_ is the spatial weight matrix, and \(x_{i}\) and \(x_{j}\) are the values of the variable of interest in the respective areas. A statistically significant [PERSON]'s I result confirms the presence of spatial effects.