Comparison of spatial and aspatial logistic regression models for landmine risk mapping. (January 2016)

• Record Type: Journal Article
• Authors: Schultz, Craig; Alegría, Aura Cecilia; Cornelis, Jan; Sahli, Hichem
• Is Part Of: Applied geography. Volume 66(2016:Jan.)
• Page Start: 52
• Page End: 63
• Publication Date: 2016-01
• Subjects: Landmine risk -- Logistic regression -- Geographically weighted regression -- Humanitarian mine action; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2015.11.005
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: Landmines continue to affect the lives of millions of people living in war-torn countries. One major challenge in humanitarian mine action (HMA) is finding new and integrated approaches to land release, which remains a slow and costly process. The use of geographic information systems (GIS) in HMA can improve the land release process by efficient mapping and prioritizing of landmine risk areas. This study explores the usage of aspatial and spatial regression techniques to construct a predictive geo-statistical model for landmine risk mapping in a small 160 km 2 municipality in Bosnia and Herzegovina (BiH) and a large 4500 km 2 region in Colombia. The first application of logistic geographically weighted regression to landmine risk mapping is presented. The results show that in the BiH study area, the effect of local parameters that influence the distribution of landmine risk varies significantly across the study area. Conversely, in the Colombia case study the effect of explanatory variables remains more homogeneous over the study area. We produced two landmine risk maps for each study area, based on aspatial and spatial regression models. Risk maps are classified into five classes, i.e. very low, low, medium, high, and very high risk. The landmine risk maps created through the usage of these innovative methodologies improve the assessment of risk and prioritization of the land release process in mine-contaminated areas, compared to existing approaches. Highlights: Use of Geographically Weighted Regression to estimate risk in landmine/ERW affected areas. We explore patterns in the data linked to potential landmine emplacement strategies. We visualize results over the complete study area by using interpolation.