Evidence map›Paper›PMID 42420481›Full record

ReviewNature2026

Computational approaches and the future of urban crime research.

Gian Maria Campedelli, Zubin Jelveh, Aaron Chalfin, Daniel Semenza, Eric Piza, Ariadna Albors Zumel, Bruno Lepri, Patrick Sharkey

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature, 2026. 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

8 authors.

Gian Maria CampedelliDepartment of Sociology and Social Research, University of Trento, Trento, Italy. gianmaria.campedelli@unitn.it.ORCID http://orcid.org/0000-0002-7734-7956
Zubin JelvehDepartment of Criminology and Criminal Justice, University of Maryland, College Park, MD, USA.
Aaron ChalfinDepartment of Criminology, University of Pennsylvania, Philadelphia, PA, USA.
Daniel SemenzaDepartment of Urban-Global Public Health, Rutgers University, Piscataway, NJ, USA.
Eric PizaSchool of Criminology and Criminal Justice, Northeastern University, Boston, MA, USA.
Ariadna Albors ZumelDepartment of Sociology and Social Research, University of Trento, Trento, Italy.
Bruno LepriMobile and Social Computing Lab, Center for Augmented Intelligence, Fondazione Bruno Kessler, Trento, Italy.
Patrick SharkeySchool of Public and International Affairs, Princeton University, Princeton, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban environments have long been a central focus for crime researchers across diverse disciplines. Over the past few decades, this heterogeneous area of inquiry has experienced substantial methodological and empirical change, driven by the emergence of novel datasets and the increasing use of flexible computational methods. In this Review, we take stock of this evolution and examine the potential that these developments hold for advancing urban crime research, while also addressing the persistent challenges that continue to shape the field. Building on this overview, we emphasize the promise that computational methods offer for more rigorous causal inference beyond traditional prediction tasks. Finally, we outline three key directions for future research to ensure that new data and computational tools are used effectively: greater integration across disciplines, improved open science standards and a broader scope of inquiry beyond Western contexts. In doing so, we aim to support more rigorous research and inform the development of more effective policies for safer and more sustainable cities worldwide.

Indexed as

CitiesCrimeComputing MethodologiesHumansResearch

Identifiers

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.