Urban ecological resilience in the Tehran metropolis is one of the most important focuses of urban policy-making due to climatic and environmental challenges. The present research was conducted with the aim of analyzing Tehran's ecological resilience based on regulating ecosystem services and environmental and human variables. To this end, the Multiscale Geographically Weighted Regression (MGWR) model and the eleven-fold City Resilience Index (CLI) were employed.
The results showed that vegetation cover (NDVI), with a positive coefficient and small dispersion, has a uniform and determining effect on the CLI, confirming the importance of greenery in enhancing urban ecological resilience. The per capita green space showed a small positive coefficient, indicating a limited but significant impact on resilience, which suggests its unbalanced distribution across neighborhoods. Geomorphological variables, such as land slope and the rate of land subsidence, showed a consistently negative effect on the CLI. Transportation infrastructures, particularly the distance from the metro and BRT (Bus Rapid Transit), had a positive relationship with the CLI, although this relationship likely reflects population density and economic activities along high-traffic corridors.
In terms of air quality, showed a significant negative effect, while and had a slight positive effect on the CLI; showed no significant impact. Pearson's correlation results indicated no correlation higher than between the variables and the CLI, suggesting the complementary and multi-factorial role of environmental and physical indicators in shaping urban resilience.
In total, the findings suggest that enhancing Tehran’s urban ecological resilience requires a systemic and multi-dimensional approach. Policy-making in this area should focus on inclusive greenery development, redesigning compact urban fabrics, and developing green and sustainable transportation.

In the present study, the main aim was the spatial evaluation summer rainfall of northwest of Iran based on30 stations in northwest of Iran during 30 years of statistical period (1985-2014). An attempt, using geo-statistical modeling by ordinary least squares (OLS) and geographically weighted regression (GWR) procedures, was also made. The results represented that the GWR model with higher S2, lower residuals and lower RMSE is an optimized geo-statistical model for rainfall modeling of this area. This model can explain spatio-temporal rainfall distribution in northwest of Iran in a diversified topographical and geographical background. This model revealed that two spatial factors including elevation and slope, have the most important role in the summer rainfall behavior.Therefore Elevations in the mountainous and eastern parts of Lake Urmia, Latitude in the northern regions and slopes in the east of the region, have the most role in the spatial variations of summer precipitation in northwestern Iran.