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

作　　者：Anasua Chakraborty Mitali Yeshwant Joshi Ahmed Mustafa Mario Cools Jacques Teller 

机构地区：[1]Local Environment Management and Analysis(LEMA),Urban and Environmental Engineering,University of Liege,Liege,Belgium [2]Copernicus Institute of Sustainable Development,Utrecht University,Utrecht,the Netherlands [3]Urban Systems Lab,The New School,New York,USA

出　　处：《Geography and Sustainability》2025年第2期143-156,共14页地理学与可持续性(英文)

基　　金：funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).

摘　　要：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.

关 键 词：Urban densification Sensitivity analysis Multinomial logistic regression Stepwise regression 

分 类 号：TU984[建筑科学—城市规划与设计]