Impact and a Novel Representation of Spatial Data Uncertainty in Debris Flow Susceptibility Analysis

In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action

Visualisation of Spatial Data Uncertainty. A Case Study of a Database of Topographic Objects

The Database of Topographic Objects (DTO) is the official database of Poland for collecting and providing spatial data with the detail level of a topographic map. Polish national DTOs manage information about the spatial location and attribute values of geographic objects. Data in the DTO are the starting point for geographic information systems (GISs) for various central and local governments as well as private institutions. Every set of spatial data based on measurement-derived data is susceptible to uncertainty. Therefore, the widespread awareness of data uncertainty is of vital importance to all GIS users. Cartographic visualisation techniques are an effective approach to informing spatial dataset users about the uncertainty of the data. The objective of the research was to define a set of methods for visualising the DTO data uncertainty using expert know-how and experience. This set contains visualisation techniques for presenting three types of uncertainty: positional, attribute, and temporal. The positional uncertainty for point objects was presented using visual variables, object fill with hue colour and lightness, and glyphs placed at map symbol positions. The positional uncertainty for linear objects was presented using linear object contours made of dotted lines and glyphs at vertices. Fill grain density and contour crispness were employed to represent the positional uncertainty for surface objects. The attribute value uncertainty and the temporal uncertainty were represented using fill grain density and fill colour value. The proposed set of the DTO uncertainty visualisation methods provides a finite array of visualisation techniques that can be tested and juxtaposed. The visualisation methods were comprehensively evaluated in a survey among experts who use spatial databases. Results of user preference analysis have demonstrated that the set of the DTO data uncertainty visualisation techniques may be applied to the full extent. The future implementation of the proposed visualisation methods in GIS databases will help data users interpret values correctly