# Tourism-Induced Urbanization in Phuket Island, Thailand (1987-2024): A Spatiotemporal Analysis
## 3 Results
### Analysis of Urban Expansion and Tourism  
To understand the effect of tourism on urban expansion, we extend our analysis by extracting urban area data from Landsat imagery using GEE. This allows us to examine its relationship with visitor numbers over time. Additionally, we incorporated supplementary economic and demographic variables into our analysis. We gathered information on the Gross Provincial Product (GPP) of the service sector in Phuket and the population density, which encompasses both registered residents and migrant laborers who are likely to be employed in the tourism sector. Furthermore, we investigated the density of housing, which serves as a metric for the expansion of residential areas (see Appendix A).  
Figure 7: The urban landscape transformation underscores the critical need for strategic planning to address these challenges and ensure sustainable development for Phuket’s future.  
Then, we apply the lagged regression analysis on urban expansion and tourism to investigate the correlation between accumulated urban area expansion and tourism growth (visitor numbers), while integrating economic and demographic variables, including Gross Provincial Product (GPP) in the service sector and housing density. The investigation employed a lagged regression model to forecast urban expansion based on historical data on tourist numbers, GPP, and housing density (Figure 8).  
Figure 8: Correlation heatmap, revealing strong links between urban expansion, housing density, and economic indicators. (**a**) The relationship between urban growth, visitor numbers, and economic activity, highlighting long-term urbanization trends. (**b**) A strong alignment between actual and predicted urban expansion, confirming the model’s accuracy (**c**).  
The regression model (Appendix A) indicates that visitor numbers are not statistically significant in forecasting urban expansion at 1-year, 2-year, or 3-year intervals. The one-year lag coefficient (17.14, \(p\) = 0.113) indicates a minor although statistically insignificant effect. This outcome suggests that urbanization is not primarily influenced by transient variations in tourism but instead by a confluence of enduring economic and demographic elements.  
The lagged housing density variable (1-year lag) exerted the most significant impact on the urban expansion (coef = 6.52, \(p\) < 0.0001). This indicates that urbanization is mostly propelled by residential development rather than direct increases related to tourism. The 1-year lagged GPP variable was statistically significant (\(p\) = 0.021) and had a negative coefficient (\(-\)0.0017). This may suggest that heightened economic activity could impede urban expansion, potentially due to rising land prices, legislative changes, or congestion impacts. The regression model accounted for 99.3% of the variance in urban expansion (R\({}^{2}\) = 0.993, Adjusted R\({}^{2}\) = 0.991). The F-statistic (489.0, \(p\) < 0.0001) indicates that the model is very significant and appropriately fitted (Appendix A). The map comparing anticipated and actual accumulated urban areas demonstrates that the model accurately reflects real-world urbanization trends.  
Urban expansion adheres to a protracted trajectory, predominantly shaped by housing development and economic conditions rather than transient tourism swings. Although tourism fosters economic growth, its direct influence on urban expansion is neither quick nor substantial. Policymakers must prioritize the equilibrium between tourism expansion and housing and infrastructure policy to facilitate sustainable urban development.