Evidence map›Paper›PMID 38855358›Full record

ArticleAWWA water science

Geospatial model of composition of water service lines in Flint, Michigan: Validation using excavation data and a new compositional geostatistical approach.

Pierre Goovaerts

Abstract read
In one paragraph

Article in AWWA water science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Pierre GoovaertsBioMedware Inc, Ann Arbor, Michigan, 48103, USA.ORCID 0000-0002-9621-7946

Funding

Geostatistical software for merging multivariate data with various spatial supportsR44ES031875 · NIEHS · BIOMEDWARE · PI GOOVAERTS, PIERRE E · 2021 to 2022
$1.7M
Geostatistical Software for Non-Parametric Geostatistical Modeling of UncertaintyR43LM014519 · NLM · BIOMEDWARE · PI GOOVAERTS, PIERRE E · 2023 to 2023
$300k
Geostatistical software for merging multivariate data with various spatial supportsR43ES031875 · NIEHS · BIOMEDWARE · PI GOOVAERTS, PIERRE E · 2020 to 2020
$225k
NIEHS NIH HHS R43 ES031875NIEHS NIH HHS R44 ES031875NLM NIH HHS R43 LM014519
6 · The paper itself

Abstract

Following the Flint drinking water crisis, a service line (SL) replacement program was implemented to replace lead SLs and galvanized SLs connecting residences to Flint's water system, leading to the excavation and inspection over a 5-year period (2016-2020) of a total of 26,750 lines, representing close to 50% of all tax parcels in the City of Flint. These recent data were used to validate an earlier geospatial model created by residual indicator kriging (IK) to predict the probability that a home has a lead, galvanized, or copper private-side SL based on neighboring house inspections (i.e., 3254 homes visited in 2017) and secondary information (i.e., built year and city records on SL composition). Receiver operating characteristic curves indicated an average frequency of detection (i.e., area under the curve [AUC]) of 0.9 for copper and galvanized material and 0.6 for lead service lines. Predicting the composition of SL at unmonitored residences by IK, however, can result in negative probabilities of occurrence and probabilities that do not sum to 1. These limitations were overcome by adopting simplicial IK, whereby data undergo a logratio transform before the geospatial analysis. This first application of a compositional approach to SL data improved the detection of lead SLs (AUC = 0.8 vs. 0.6) while providing coherent predictions. Incorporating secondary information, in particular using standardized cokriging and a new rescaled cross-semivariogram estimator introduced to correct for geographical clustering of house inspections, increased the accuracy of the prediction.

Indexed as

compositionsgalvanizedindicator semivariogramskrigingLead and Copper Rule

Identifiers

PMID38855358
PMCPMC11160116

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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.