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Land
Special Issues
Big Data-Driven Urban Spatial Perception
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Big Data-Driven Urban Spatial Perception

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 641

Special Issue Editors

Prof. Dr. Helin Liu

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Guest Editor
School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: computational modeling; big data and AI for planning; innovation geography; low-carbon planning
Dr. Guangliang Xi

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
Interests: digital urban development; spatial planning; smart cities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Feng Zhen

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
Interests: big data and urban planning; smart city planning
Special Issues, Collections and Topics in MDPI journals
Dr. Mingshu Wang

E-Mail Website
Guest Editor
School of Geographical & Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK
Interests: advancing GIScience and big data analytics methods, including GeoAI, explainable artificial intelligence, and econometrics, to better understand urban systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, rapid global urbanization has exacerbated critical urban challenges, including inefficient governance, resource scarcity, environmental degradation, and traffic congestion. Urban spatial perception, which characterizes urban spatial features based on human behavioral patterns, is pivotal for deciphering urban dynamics, optimizing public resource allocation, and enhancing spatial quality. Big data offers transformative potential by integrating multi-source information to record, in real time, diverse population movements, public sentiment in urban spaces, and environmental conditions. This capability presents a ripe opportunity to shift urban spatial perception from experience-driven to data-driven paradigms. Furthermore, artificial intelligence (AI) and big data technology can generate richer and more accurate insights into travel patterns, urban mobility and transportation, spatial vitality and quality, resource utilization efficiency, crime prediction, and public health and urban environments. These technologies also facilitate deeper mechanistic explanations of the underlying causes and interactions shaping urban systems.

The goal of this Special Issue is to invite academics and practitioners to submit original research articles and reviews to provide insights on big data-driven urban spatial perception.

For this Special Issue, we invite you to submit original research articles and reviews to provide insights on big data and urban land use planning. Research areas may include (but are not limited to) the following:

  • Urban mobility and transportation;
  • Human mobility and urban activity patterns;
  • Urban spatial perception and city image;
  • Sentiment analysis in urban spaces;
  • Urban safety and crime prediction;
  • Public health and urban environments.

We look forward to receiving your contributions.

Prof. Dr. Helin Liu
Dr. Guangliang Xi
Prof. Dr. Feng Zhen
Dr. Mingshu Wang
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 250 words) can be sent to the Editorial Office for assessment.

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. Land 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 2600 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

  • urban mobility and transportation
  • human mobility and urban activity patterns
  • urban spatial perception and city image
  • sentiment analysis in urban spaces
  • urban safety and crime prediction
  • public health and urban environments

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

Published Papers (2 papers)

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Research

20 pages, 4300 KB  
Article
Examining the Nonlinear Relationship Between Built Environment and Residents’ Leisure Travel Distance: A Case Study of Guangzhou, China
by Ying Xu, Yankai Wang, Helin Liu, Jialei Huang, Yulin Huang and Mei Luo
Land 2025, 14(12), 2392; https://doi.org/10.3390/land14122392 - 9 Dec 2025
Abstract
Understanding the spatiotemporal characteristics of residents’ leisure travel distances (hereafter referred to as “RLTD”) and their underlying influencing factors is pivotal to reducing leisure travel costs and enhancing travel experiences. However, scholars have yet to identify leisure travel behavior and quantify RLTD accurately, [...] Read more.
Understanding the spatiotemporal characteristics of residents’ leisure travel distances (hereafter referred to as “RLTD”) and their underlying influencing factors is pivotal to reducing leisure travel costs and enhancing travel experiences. However, scholars have yet to identify leisure travel behavior and quantify RLTD accurately, and the nonlinear effects of the built environment on such distances remain underexplored. Therefore, this study, selecting Guangzhou as the case, employed multi-source data to measure RLTD and utilized a random forest model to explore the nonlinear relationship between the built environment and RLTD. Our findings are as follows. (1) Leisure activities among Guangzhou residents are dominated by short- and medium-distance travel (<10 km). Furthermore, RLTD exhibits significant spatiotemporal heterogeneity: on weekdays, it follows a zonal pattern where distances increase from the urban core to the periphery; conversely, on weekends, low-RLTD areas show a multi-center agglomeration pattern. (2) Proximity to central business districts (CBD) and large commercial centers, as well as optimal parking facility provision, emerge as the strongest predictors of RLTD on both weekdays and weekends. (3) All built environment variables exert nonlinear effects on RLTD, with distinct thresholds between weekdays and weekends. Additionally, a noticeable interaction effect is observed between the “distance to CBD” variable and other covariates. This study implies that when designing targeted interventions to promote residents’ leisure travel experience, policymakers should account for the temporal variations in how the built environment complexly influences RLTD. Full article
(This article belongs to the Special Issue Big Data-Driven Urban Spatial Perception)
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27 pages, 14100 KB  
Article
Official Projection vs. Public Perception: Measuring the Perceptual Discrepancy of Creative Industry Parks in the Industrial Heritage Category Using Large Language Models
by Xiaoke Yang, Bin Hu and Jingwei Zhao
Land 2025, 14(12), 2371; https://doi.org/10.3390/land14122371 - 4 Dec 2025
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Abstract
Industrial tourism serves as a medium for disseminating industrial culture and strengthening public awareness. Quantifying the discrepancies between official projections and public perceptions is essential for shaping the destination image and enhancing appeal and competitiveness. This study examines five industrial heritage creative industry [...] Read more.
Industrial tourism serves as a medium for disseminating industrial culture and strengthening public awareness. Quantifying the discrepancies between official projections and public perceptions is essential for shaping the destination image and enhancing appeal and competitiveness. This study examines five industrial heritage creative industry parks using large language models (LLMs) and multimodal data to address this issue. The results indicate the following: (1) Multimodal data fusion improves feature representation. (2) A clear discrepancy exists between official projections and public perceptions. The official perspective emphasizes the Cultural value of heritage, in contrast to the public’s greater concern with the Service experience perception. Despite this divergence, there is alignment in the recognition of the Creative industry form dimension. (3) Public sentiment regarding the parks is predominantly positive. However, an analysis of negative sentiments reveals that insufficient supporting facilities and poor consumption experience are the primary sources of dissatisfaction. Through large language models and multimodal data, this study proposes a framework for quantifying the gaps between official projections and public perceptions. It also provides practical insights and empirical support for the management and planning of industrial heritage creative industry parks. Full article
(This article belongs to the Special Issue Big Data-Driven Urban Spatial Perception)
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