TY - JOUR
T1 - A novel concurrent approach for multiclass scenario discovery using Multivariate Regression Trees
T2 - Exploring spatial inequality patterns in the Vietnam Mekong Delta under uncertainty
AU - Jafino, Bramka Arga
AU - Kwakkel, Jan H.
PY - 2021
Y1 - 2021
N2 - To support equitable planning, model-based analyses can be used to explore inequality patterns arising from different scenarios. Scenario discovery is increasingly used to extract insights from ensembles of simulation. Here, we apply two scenario discovery approaches for unraveling inequality patterns and their drivers, with an application to spatial inequality of farms profitability in the Vietnam Mekong Delta. First, we follow an established sequential approach where we begin with clustering the inequality patterns from the simulation results and next identify model input subspaces that best explain each cluster. Second, we propose a novel concurrent approach using Multivariate Regression Trees to simultaneously classify inequality patterns and identify their corresponding input subspaces. Both approaches have comparable output space separability performance. The concurrent approach yields significantly better input space separability, but this comes at the expense of having a larger number of subspaces, requiring analysts to make extra effort to distill policy-relevant insights.
AB - To support equitable planning, model-based analyses can be used to explore inequality patterns arising from different scenarios. Scenario discovery is increasingly used to extract insights from ensembles of simulation. Here, we apply two scenario discovery approaches for unraveling inequality patterns and their drivers, with an application to spatial inequality of farms profitability in the Vietnam Mekong Delta. First, we follow an established sequential approach where we begin with clustering the inequality patterns from the simulation results and next identify model input subspaces that best explain each cluster. Second, we propose a novel concurrent approach using Multivariate Regression Trees to simultaneously classify inequality patterns and identify their corresponding input subspaces. Both approaches have comparable output space separability performance. The concurrent approach yields significantly better input space separability, but this comes at the expense of having a larger number of subspaces, requiring analysts to make extra effort to distill policy-relevant insights.
KW - Adaptation
KW - Deep uncertainty
KW - Equity
KW - Fairness
KW - Model-based decision support
UR - http://www.scopus.com/inward/record.url?scp=85114134405&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2021.105177
DO - 10.1016/j.envsoft.2021.105177
M3 - Article
AN - SCOPUS:85114134405
SN - 1364-8152
VL - 145
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 105177
ER -