Evidence map›Paper›PMID 28344443›Full record

ArticleEnvironmetrics2017

Flow-directed PCA for monitoring networks.

K Gallacher, C Miller, E M Scott, R Willows, L Pope, J Douglass

Abstract read
In one paragraph

Article in Environmetrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  2. 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

6 authors.

K GallacherSchool of Mathematics and Statistics University of Glasgow Glasgow U.K.
C MillerSchool of Mathematics and Statistics University of Glasgow Glasgow U.K.
E M ScottSchool of Mathematics and Statistics University of Glasgow Glasgow U.K.
R WillowsSchool of Mathematics and Statistics University of Glasgow Glasgow U.K.
L PopeEvidence Directorate Environment Agency U.K.
J DouglassEvidence Directorate Environment Agency U.K.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measurements recorded over monitoring networks often possess spatial and temporal correlation inducing redundancies in the information provided. For river water quality monitoring in particular, flow-connected sites may likely provide similar information. This paper proposes a novel approach to principal components analysis to investigate reducing dimensionality for spatiotemporal flow-connected network data in order to identify common spatiotemporal patterns. The method is illustrated using monthly observations of total oxidized nitrogen for the Trent catchment area in England. Common patterns are revealed that are hidden when the river network structure and temporal correlation are not accounted for. Such patterns provide valuable information for the design of future sampling strategies.

Indexed as

connected monitoring networksflow directionPCA

Identifiers

PMID28344443
PMCPMC5347935

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.