Methods for visualizing the explosive remnants of war

Lacroix, Pierre; Herzog, Jonas; Eriksson, D.; Weibel, Robert

doi:10.1016/j.apgeog.2013.04.007

Cited by 8 publications

(12 citation statements)

References 50 publications

(35 reference statements)

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“…The concept has been further enriched by Ref. [25] by considering highly heterogeneous spatial distributions, and also by considering the use of non-point data such as polygons. For polygon data such as SHAs, Ref.…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

“…The parameter k "reflects the degree of clustering and the spacing of points, rather than the extent of the study area or the point dataset size" [25].…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

“…For polygon data such as SHAs, Ref. [25] proposed infilling the polygons with points before using them as inputs in the kernel density estimation. The authors pointed out that mapping using KDE applied to points and centroids of polygons "can be used to show more details, is less sensitive to the Modifiable Areal Unit Problem (MAUP), preserves areas of low contamination, stores data at the national level and is likely to encourage the sharing of information".…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

“…Several statistical methods have been suggested to find an optimal bandwidth, such as Likelihood Cross Validation (LCV), Least Squares Cross Validation (LSCV), Biased Cross Validation (BCV) and Smoothed Cross Validation (SCV) [26][27][28][29], among others. Bandwidth selection has a significant effect on the visualization of landmine and ERW events [24,25]. In [25], the bandwidth was adjusted to the input data using a user-defined parameter.…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

“…Bandwidth selection has a significant effect on the visualization of landmine and ERW events [24,25]. In [25], the bandwidth was adjusted to the input data using a user-defined parameter. The authors select the bandwidth as the average distance to the k-th nearest neighbour, with k=round…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

See 4 more Smart Citations

Hazard Mapping of Landmines and ERW Using Geo-Spatial Techniques

Alegria

Zimányi

Cornelis³

et al. 2017

J Remote Sensing & GIS

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Landmines and Explosive Remnants of War (ERW) continue to represent a significant nuisance for society in affected countries. Coping with humanitarian and development activities, mine action aims at both, reducing the impacts of the presence of landmines/ ERW on the population, and ultimately returning cleared land to the communities. These are the main tasks of mine action decision makers. This study combines landmine/ERW contamination data with explanatory variables that contain information about underlying targets. They are integrated into a risk mapping framework using Geographic Information Systems with other information sources, such as remote sensing. The aim of this paper is to provide insights into the populations and/or locations at risk caused by landmine and ERW impacts on a broad and local scale. Thus, the concept of 'hotspots' is particularly useful because it provides a visual representation of exposure, aided by a geo-spatial representation of 'priority areas for mine action planners to focus on. We apply the Kernel Density Estimator (KDE) to derive such 'hotspots'. KDE is proposed as the basis to define landmine and ERW hazard, vulnerability, and element-at-risk maps, which enable producing a final output, the landmine/ERW risk map. This is accomplished by using an adaptive kernel bandwidth for datasets with highly heterogeneous spatial distributions, and a problem specific method for generating point samples from polygon data, before using them as inputs for KDE. The geo-statistical model presented here is a time-and-cost-efficient method to construct a landmine risk map, that is as representative as those produced by mine action actors. It can be used as a complement to the risk area maps made by these actors because they are slightly different but show a large degree of overlap. Moreover, the method helps revealing the variables which are the most linked to landmine/ERW-related events in the study area.

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Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

“…The parameter k "reflects the degree of clustering and the spacing of points, rather than the extent of the study area or the point dataset size" [25].…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%