Evidence map›Paper›PMID 34755266›Full record

ArticleJournal of urban health : bulletin of the New York Academy of Medicine2021

Examining Time-Variant Spatial Dependence of Urban Places and Shootings.

Stephen N Oliphant

Open access · greenAbstract read
In one paragraph

Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2021. 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
0.7field-weighted citation impact, top 24% of its field
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, 8 citations in OpenAlex.

  1. 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

1 author at 1 institution in 1 country.

Stephen N OliphantSchool of Criminal Justice, Michigan State University, 655 Auditorium Road, 439 Baker Hall, East Lansing, MI, 48824, USA. olipha13@msu.edu.ORCID 0000-0002-5235-173X
Michigan State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Place-based interventions are increasingly implemented to address firearm violence. While research on the social determinants of health and criminological theories suggest that the built environment significantly influences health outcomes and the spatial distribution of crime, little is known about the attraction between urban places and shootings. The present study adds to the literature on firearm violence and micro-place research by exploring the spatial dependence in a Midwest metropolitan area between shootings and bus stops, vacant properties, alcohol outlets, and other locations that have been theoretically or empirically linked to firearm violence. The G-function and Cross-K function are used to characterize the univariate clustering of shootings and bivariate attraction with other locations, respectively. Bus stops, blighted vacant properties, alcohol outlets, and businesses/residential locations participating in a public-private-community initiative to reduce crime exhibited significant locational dependence with shootings at short distances. Attraction between on-premises alcohol outlets and shootings was observed only during the night. No attraction was found between schools and shootings. The findings reaffirm the importance of place-based research-especially at the micro-place level-and suggest that certain urban places may be appropriate targets for interventions that modify existing physical and/or social structures.

Indexed as

FirearmsWounds, GunshotHumansResidence CharacteristicsSocial StructureViolenceFirearm violenceMicro-placesShooting attractorsSpatial analysis

Identifiers

PMID34755266
PMCPMC8688600
OpenAlexW3213254111

What OpenQuestion holds

Textmetadata
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.