Evidence map›Paper›PMID 29904240›Full record

ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2018

Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes.

Jingyu Liu, Walter W Piegorsch, A Grant Schissler, Susan L Cutter

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2018. 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. Article
  2. From terrorism to flooding: How vulnerable is your city?Significance (Oxford, England) · 2021
    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

4 authors.

Jingyu LiuInterdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, USA.
Walter W PiegorschInterdisciplinary Program in Statistics and Department of Mathematics, University of Arizona, Tucson, AZ, USA.
A Grant SchisslerDepartment of Mathematics and Statistics, University of Nevada, Reno, NV, USA.
Susan L CutterHazards & Vulnerability Research Institute and Department of Geography University of South Carolina, Columbia, SC, USA.

Funding

SOUTHWEST ENVIRONMENTAL HEALTH SCIENCES CENTERP30ES006694 · NIEHS · UNIVERSITY OF ARIZONA · PI DEAN BILLHEIMER · 1994 to 2026
$36.7M
Translational mixed-factor benchmark-dose risk assessmentR03ES027394 · NIEHS · UNIVERSITY OF ARIZONA · PI PIEGORSCH, WALTER W · 2016 to 2017
$154k
NIEHS NIH HHS P30 ES006694NIEHS NIH HHS R03 ES027394
6 · The paper itself

Abstract

We develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk-analytic 'benchmark' techniques are then incorporated into the modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting.

Indexed as

Benchmark doseCentered autologistic modelGeospatial analysisMaximum pseudo-likelihoodQuantitative risk analysisSpatial autocorrelation

Identifiers

PMID29904240
PMCPMC5994772

What OpenQuestion holds

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Registered trials

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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.