Identification of Essential Descriptors in Spatial Socioeconomic Impact Assessment Modeling: a Case Study of Highway Broadening in Sikkim Himalaya

Identifying the right set of socioeconomic descriptors (SEDs) during the spatial analysis of a socioeconomic impact assessment (SEIA) is pivotal for a reliable impact modeling. For this, methods like factor analysis and sensitivity analysis can be used. As a case study, the spatial socioeconomic impact assessment model (SSEIAM) of the broadening of highway NH 10 in the East district of Sikkim is used to emphasize this issue. Principal component analysis (PCA) is used to identify the most important SEDs contributing to the composite impact estimated by SSEIAM. Furthermore, spatially explicit sensitivity analysis (SESA) is performed to identify the model sensitivity to SED weights. SSEIAM is a GIS-based model that relies on experts’ opinion and peoples’ perception of the impacts of the project on the SEDs. The model uses weighted linear combination (WLC) of kriging-generated SED surfaces to prepare the composite impact map. PCA indicates that farming activities, health facilities, traditional values, demographic profile, tourism, and land use and land value are the major contributors to the variance in the descriptor space. SESA shows that SSEIAM is robust. However, land use and land value and farming activities contribute most to the perturbations of the composite impact value. This suggests that model variable identification is a crucial step towards impact modeling.