Model's parameter sensitivity assessment and their impact on Urban Densification using regression analysis

Abstract

The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them. The interplay between such variables is crucial for modelling urban growth to closely reflects reality. Despite extensive research, ambiguity remains about how variations in these input variables influence urban densification. In this study, we conduct a global sensitivity analysis (SA) using a multinomial logistic regression (MNL) model to assess the model's explanatory and predictive power. We examine the influence of global variables, including spatial resolution, neighborhood size, and density classes, under different input combinations at a provincial scale to understand their impact on densification. Additionally, we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area (BMA). Our results indicate that a finer spatial resolution of 50 m and 100 m, smaller neighborhood size of 5 × 5 and 3 × 3, and specific density classes—namely 3 (non-built-up, low and high built-up) and 4 (non-built-up, low, medium and high built-up)—optimally explain and predict urban densification. In line with the same, the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables, reflecting a lower explanatory power for densification. This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification. Furthermore, these findings are reproducible in a global urban context, offering valuable insights for planners, modelers and geographers in managing future urban growth and minimizing modelling.