Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.9
Journals
Urban Science
Special Issues
Intelligent GIS Application in Cities
share announcement

Intelligent GIS Application in Cities

A special issue of Urban Science (ISSN 2413-8851).

Deadline for manuscript submissions: 30 April 2026 | Viewed by 283

Special Issue Editors

Prof. Dr. Zipan Cai

E-Mail Website
Guest Editor
Department of Urban Planning, Southeast University, Nanjing 210096, China
Interests: spatial dynamic modeling; urban AI; planning support systems
Dr. Yoonshin Kwak

E-Mail Website
Guest Editor
Division of Urban Planning and Landscape Architecture, Gachon University, Seongnam-si 13120, Republic of Korea
Interests: spaital modeling; landscape planning; evidence-based design; decision-making
Special Issues, Collections and Topics in MDPI journals
Dr. Si Chen

E-Mail Website
Guest Editor
School of Planning, Design and Construction, Michigan State University, East Lansing, MI 48824, USA
Interests: urban informatics; planning support systems; transportation modeling; environmental modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, Intelligent GIS Application in Cities, will explore cutting-edge applications of Geographic Information Systems (GISs) integrated with intelligent technologies such as artificial intelligence (AI), machine learning, and big data analytics in the context of urban environments. This Special Issue focuses on how these advancements are transforming spatial analysis, urban planning, infrastructure management, environmental monitoring, and citizen services.

The scope includes novel methods, models, and frameworks that harness intelligent GIS to address complex urban challenges, including but not limited to mobility management, spatial planning, environmental sustainability, infrastructure optimization, disaster resilience, and data-informed governance. We invite contributions that demonstrate interdisciplinary innovation, practical deployment, or theoretical advancement in the use of intelligent GIS.

This Special Issue complements and extends the existing literature by bridging traditional GIS research with the rapidly evolving field of AI-driven urban analytics. It will serve as a timely resource for scholars, planners, and practitioners aiming to understand and shape the intelligent cities of tomorrow.

Prof. Dr. Zipan Cai
Dr. Yoonshin Kwak
Dr. Si Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Urban Science is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent GIS
  • urban analytics
  • urban AI
  • machine learning
  • decision support
  • spatial planning
  • urban planning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 10238 KB  
Article
A Geospatial Framework for Spatiotemporal Crash Hotspot Detection Using Space–Time Cube Modeling and Emerging Pattern Analysis
by Samar Younes and Amr Oloufa
Urban Sci. 2025, 9(10), 411; https://doi.org/10.3390/urbansci9100411 - 3 Oct 2025
Abstract
Traffic crashes remain a critical public safety issue and are among the leading causes of mortality worldwide. Understanding, analyzing, and forecasting crash trends are essential for implementing effective countermeasures and reducing injury severity. In response to the growing number of crashes and their [...] Read more.
Traffic crashes remain a critical public safety issue and are among the leading causes of mortality worldwide. Understanding, analyzing, and forecasting crash trends are essential for implementing effective countermeasures and reducing injury severity. In response to the growing number of crashes and their associated economic and social costs, this study presents a geospatial analytical framework for prioritizing and classifying roadway segments based on crash trends. The framework focuses on a major freeway corridor in the United States, covering a four-year period across 20 counties. This methodology employs spatiotemporal analysis, which integrates both spatial (geographic) and temporal (time-based) dimensions to better understand how crash patterns evolve over time and space. A central component of the analysis is Space–Time Cube (STC) modeling, a three-dimensional GIS-based visualization, and an analytical approach that organizes data into spatial locations (x and y) across a sequence of temporal bins (z-axis) to reveal patterns that may not be evident in a two-dimensional analysis. Additionally, emerging pattern analysis, specifically Emerging Hotspot Analysis (EHA), is used to identify statistically significant trends in crash frequency over time. The results indicate a significant spatial clustering of crashes, with high-risk segments predominantly located in densely populated urban areas with high traffic volumes. Crash hotspots were classified into five distinct categories: persistent, intensifying, new, sporadic, and diminishing, enabling transportation agencies to tailor interventions based on temporal dynamics. The proposed geospatial framework enhances decision making for roadway safety improvements and can be adapted for use in other regional corridors to support infrastructure investment and advance public safety. Full article
(This article belongs to the Special Issue Intelligent GIS Application in Cities)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop