Evidence map›Paper›PMID 34078986›Full record

ArticleScientific reports2021

A route pruning algorithm for an automated geographic location graph construction.

Christoph Schweimer, Bernhard C Geiger, Meizhu Wang, Sergiy Gogolenko, Imran Mahmood, Alireza Jahani, Diana Suleimenova, Derek Groen

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Christoph SchweimerKnow-Center GmbH, Graz, Austria.
Bernhard C GeigerKnow-Center GmbH, Graz, Austria. geiger@ieee.org.
Meizhu WangKnow-Center GmbH, Graz, Austria.
Sergiy GogolenkoHigh Performance Computing Center Stuttgart, Stuttgart, Germany.
Imran MahmoodDepartment of Computer Science, Brunel University London, London, UK.
Alireza JahaniDepartment of Computer Science, Brunel University London, London, UK.
Diana SuleimenovaDepartment of Computer Science, Brunel University London, London, UK. diana.suleimenova@brunel.ac.uk.
Derek GroenDepartment of Computer Science, Brunel University London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is [Formula: see text], which enables the computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible.

Identifiers

PMID34078986
PMCPMC8172915

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.