Evidence map›Paper›PMID 40959591›Full record

ArticleAnnals of GIS2025

Artificial intelligence in urban science: why does it matter?

Xinyue Ye, Tan Yigitcanlar, Michael Goodchild, Xiao Huang, Wenwen Li, Shih-Lung Shaw, Yanjie Fu, Wenjing Gong, Galen Newman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of GIS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Xinyue YeDepartment of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications, and Technology, Texas A&M University, College Station, Texas, USA.
Tan YigitcanlarCity 4.0 Lab, School of Architecture and Built Environment, Faculty of Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
Michael GoodchildDepartment of Geography, University of California, Santa Barbara, California, USA.
Xiao HuangDepartment of Environmental Sciences, Emory University, Atlanta, Georgia, USA.
Wenwen LiSchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA.
Shih-Lung ShawDepartment of Geography and Sustainability, University of Tennessee, Knoxville, Tennessee, USA.
Yanjie FuSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA.
Wenjing GongDepartment of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications, and Technology, Texas A&M University, College Station, Texas, USA.
Galen NewmanDepartment of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications, and Technology, Texas A&M University, College Station, Texas, USA.

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Efstratios Pistikopoulos · 2017 to 2026
$21.2M
NIEHS NIH HHS P42 ES027704
6 · The paper itself

Abstract

Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the 'black-box' nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science's emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI's ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science - termed the 'New Urban Science' - promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science's foundational goals.

Indexed as

Artificial intelligencedigital twinsexplainable AIhuman dynamicsurban science

Identifiers

PMID40959591
PMCPMC12435530

What OpenQuestion holds

Textmetadata
LicenceTDM
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.