DBTech EXT: Education and Hands-On Training for the Database Professional

Laiho, Martti; Dervos, Dimitris A.; Aldana‐Montes, José F.; Laux, Fritz

doi:10.1007/978-3-642-15576-5_3

Cited by 2 publications

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“…Uncertainties in building the network topology-for example, missing topological integrity, or model errors in the determination of edge weights (such as the weighted combination of segment weights to create edge weights);  Uncertainties in the calculation of the routes-due to the use of different and possibly suboptimal algorithms;  Uncertainties due to the interpolations to isovarones and isovarone areas: in the case of isolines, the points as calculated by routing algorithms are linked on the basis of uncertain assumptions (e.g., by a linear connection between points; see also [32,33]). Furthermore, not all points on the isovarones or within the isovarone areas can necessarily be accessed because only a minority are actually identical with the nodes and edges of the network.…”

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confidence: 99%

Consideration of Uncertainty Information in Accessibility Analyses for an Effective Use of Urban Infrastructures

Schiewe

Knura

2021

IJGI

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Accessibility analyses are an essential step in the evaluation and planning of urban infrastructures such as transport or pipeline networks. However, these studies generally produce sharply defined lines (called isovarones) or areas (called isovarone areas) that represent the same or similar accessibility. Uncertainties in the input data are usually not taken into account. The aim of this contribution is, therefore, to set up a structured framework that describes the integration of uncertainty information for accessibility analyses. This framework takes uncertainties in the input data, in the processing step, in the target variables, and in the final visualization into account. Particular attention is paid, on the one hand, to the impact of the uncertainties in the target values, as these are key factors for reasoning and decision making. On the other hand, the visualization component is emphasized by applying a dichotomous classification of uncertainty visualization methods. This framework leads to a large set of possible combinations of uncertainty categories. Five selected examples that have been generated with a new software tool and that cover important combinations are presented and discussed.

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confidence: 99%