## 4 Findings
### Spatial Correlation and Regional Variations in Selected Economic Sectors  
The analysis of spatial correlation in five key economic sectors significantly associated with average household income provides valuable insights into how these sectors are geographically distributed and interrelated across regions. Moran's I was computed for two key features of each sector: the Compound Annual Growth Rate (CAGR) from 2005 to 2021, and the sectoral productivity (Gross Provincial Product, GPP) per capita in 2021 (Table 4). The benefit of such an analysis is multifaceted; it not only highlights regions with similar growth patterns or productivity levels but also helps in identifying potential areas of spatial dependency or autocorrelation. This information is crucial for policymakers and investors aiming to understand regional economic disparities and for devising strategies to foster balanced regional development. The Moran's I results reveal varying degrees of spatial correlation across sectors and attributes. For example, the agriculture, forestry, and fishing sectors exhibit a significant positive spatial correlation in both CAGR ([PERSON]'s I = 0.262, \(p<0.001\)) and per capita productivity ([PERSON]'s I = 0.353, \(p<0.001\)). This suggests that regions with high growth or productivity in this sector tend to be geographically clustered. Similar patterns are observed in the real estate activities and arts, entertainment, and recreation sectors, indicating that regions with high productivity or growth in these sectors are likely to be near other regions with similar characteristics. Conversely, sectors such as professional, scientific, and technical activities, and administrative and support service activities show no significant spatial correlation in their CAGR ([PERSON]'s I = \(-0.059\), \(p=0.897\) for the former and [PERSON]'s I = 0.045, \(p=0.059\) for the latter). This suggests a more dispersed spatial distribution of growth rates across regions. Figure 3 offers a comprehensive visual analysis of five key economic sectors in Thailand, showcasing the CAGR from 2005 to 2021, productivity per capita in 2021, and LISA cluster maps for each sector. In the _agricultural_ sector, the CAGR maps reveal a pronounced cluster of high growth across the entire Northeastern region, contrasting with low growth in the Central region and the deep South of Thailand. However, the productivity per capita maps indicate a starkly different scenario; the Northeastern region is identified as a low productivity cluster in agriculture, while the Southern region boasts high productivity. This \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sector** & & **Moran’s I** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Agriculture forestry and fishing} & CAGR & 0.262 & \(<\)0.001 \\  & Per Capita & 0.353 & \(<\)0.001 \\  & CAGR & 0.051 & 0.044 \\  & Per Capita & 0.316 & \(<\)0.001 \\  & CAGR & \(-\)0.059 & 0.897 \\  & Per Capita & 0.074 & \(<\)0.001 \\  & CAGR & 0.045 & 0.059 \\  & Per Capita & 0.173 & \(<\)0.001 \\  & CAGR & 0.202 & \(<\)0.001 \\  & Per Capita & 0.063 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Moran’s I of the CAGR and Productivity Per Capita of the Five Sectors. contrast suggests that although the Northeastern region has seen substantial growth, it still lags in productivity, indicating a potential area for targeted development. The _real estate_ sector presents a strong dichotomy. The Bangkok Metropolitan area and the Eastern provinces are highlighted as regions of high productivity per capita. The Eastern region also emerges as a high-growth area, pointing to a significant divergence in growth and prosperity within the real estate sector across the country. Figure 3: CAGR and Productivity Per Capita of the Five Sectors and the LISA Maps. The _professional, scientific, and technical services_ sector does not exhibit a clear growth pattern according to the global Moran's I results.