<p>Identifying suitable areas for future urban expansion is necessary for balanced spatial planning and efficient land management. This study presents a hybrid spatial decision-support framework that integrates the Stepwise Weight Assessment Ratio Analysis, the Multi-Influencing Factor method, and bivariate statistical analysis to evaluate urban expansion suitability in Tartous Governorate, Syria. Multi-Influencing Factor was employed to rank the influencing factors according to their interrelationships. To reduce the subjectivity typically associated with multi-criteria decision-making approaches, Chi-square test examined the statistical dependency among criteria, and resulting relationships were incorporated into the Stepwise Weight Assessment Ratio Analysis comparison process. Stepwise Weight Assessment Ratio Analysis was then used to calculate the final weights of the selected factors. The weighted overlay technique within a GIS environment was subsequently applied to generate the final urban suitability map, which indicates that about 54.38% of the study area falls within the high suitability, while 39.31% is deemed as good suitability for urban expansion. However, very highly suitable is restricted to 2.72%. Moreover, slope was identified as the most influential factor, while proximity to the seaport showed the least influence. The developed framework was validated against current urban settlements using receiver-operating characteristic (ROC), and area under curve (AUC) value of 0.804. Additional temporal assessment showed that more than 90% of urban expansion during the last 10 years occurred within good to very high suitability classes, indicating that the model effectively captures the general spatial tendency of urban development. The developed framework demonstrates how combining statistical dependency evaluation with MCDM techniques can improve the dependability of spatial suitability assessments and urban planning decisions.</p> Graphical Abstract <p></p> <p>This study presents a statistically constrained GIS-based framework for evaluating urban expansion suitability in Tartous Governorate, Syria. Spatial datasets representing physical, environmental, and socioeconomic factors were integrated within a GIS environment. Bivariate statistical analysis using Chi-square and Cramer’s V was applied to examine the dependency between influencing factors and existing urban patterns, while the Multi-Influencing Factor (MIF) method was used to establish an objective ranking of criteria and reduce subjectivity. The statistically derived relationships were incorporated into the Stepwise Weight Assessment Ratio Analysis (SWARA) to compute final criterion weights. A weighted overlay technique was then employed to generate the urban suitability map, indicating that 54.38% of the area is highly suitable and 39.31% is suitable for urban expansion, whereas only 2.72% is very highly suitable. Model validation using ROC analysis yielded an AUC value of 0.804, and temporal validation showed that over 90% of recent urban expansion occurred within suitable classes. Results identify slope as the most influential factor and proximity to the seaport as the least influential. Overall, the framework demonstrates that integrating statistical dependency analysis with MCDM techniques enhances the reliability of spatial suitability assessments and supports informed urban planning decisions.</p>