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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: 31 May 2026 | Viewed by 4665

Special Issue Editors

Prof. Dr. Helin Liu

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: computational modelling; big data and AI for planning; innovation geography; low-carbon planning
Special Issues, Collections and Topics in MDPI journals
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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Published Papers (6 papers)

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Research

28 pages, 5635 KB  
Article
Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnostics
by Kaiqing Yuan, Haotian Lan, Yao Gao and Kun Wang
Land 2026, 15(3), 449; https://doi.org/10.3390/land15030449 - 12 Mar 2026
Viewed by 394
Abstract
While objective street metrics derived from imagery or GIS have become standard in urban analytics, they remain insufficient to capture subjective perceptions essential to inclusive urban design. This study introduces a novel Multimodal Street Evaluation Framework (MSEF) that fuses a vision transformer (VisualGLM-6B) [...] Read more.
While objective street metrics derived from imagery or GIS have become standard in urban analytics, they remain insufficient to capture subjective perceptions essential to inclusive urban design. This study introduces a novel Multimodal Street Evaluation Framework (MSEF) that fuses a vision transformer (VisualGLM-6B) with a large language model (GPT-4), enabling interpretable dual-output assessment of streetscapes. Leveraging over 15,000 annotated street-view images from Harbin, China, we fine-tune the framework using Low-Rank Adaptation(LoRA) and P-Tuning v2 for parameter-efficient adaptation. The model achieves an F1 score of 0.863 on objective features and 89.3% agreement with aggregated resident perceptions, validated across stratified socioeconomic geographies. Beyond classification accuracy, MSEF captures context-dependent contradictions: for instance, informal commerce boosts perceived vibrancy while simultaneously reducing pedestrian comfort. It also identifies nonlinear and semantically contingent patterns—such as the divergent perceptual effects of architectural transparency across residential and commercial zones—revealing the limits of universal spatial heuristics. By generating natural-language rationales grounded in attention mechanisms, the framework bridges sensory data with socio-affective inference, enabling transparent diagnostics aligned with Sustainable Development Goal 11(SDG 11). This work offers both methodological innovation in urban perception modeling and practical utility for planning systems seeking to reconcile infrastructural precision with lived experience. Full article
(This article belongs to the Special Issue Big Data-Driven Urban Spatial Perception)
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21 pages, 6529 KB  
Article
Urban Street-Scene Perception and Renewal Strategies Powered by Vision–Language Models
by Yuhan Yao, Giuliano Dall’Ò and Feidong Lu
Land 2026, 15(2), 244; https://doi.org/10.3390/land15020244 - 31 Jan 2026
Viewed by 540
Abstract
With rapid urbanization, urban renewal has become increasingly important. Traditional research has relied on expert assessments and objective indicators, lacking scalable frameworks that effectively translate street-level conditions into actionable renewal strategies. This study proposes a Vision–Language Model (VLM)-based framework to address these gaps, [...] Read more.
With rapid urbanization, urban renewal has become increasingly important. Traditional research has relied on expert assessments and objective indicators, lacking scalable frameworks that effectively translate street-level conditions into actionable renewal strategies. This study proposes a Vision–Language Model (VLM)-based framework to address these gaps, using the Hongshan Central District of Urumqi, China, as a case study. Specifically, we collected 4215 street-view images (SVIs) and employed VLMs to assess six perceptual dimensions (i.e., safety, liveliness, beauty, wealthiness, depressiveness, and boringness), together with textual descriptions. The best-performing model, selected by a 500-respondent perception survey validation, was used to conduct spatial pattern and text mining analyses to inform targeted urban renewal strategies. Results show that (1) VLMs have a high consistency with humans in evaluating the spatial perception of six dimensions; (2) spatial clustering analysis successfully delineated four distinct renewal priority tiers, confirming the method’s capability in translating perceptual data into actionable spatial strategies; and (3) textual mining of the VLM’s rationales revealed that areas with lower perceptual scores are predominantly characterized by deficiencies in foundational infrastructure and street-level order, thereby providing explanatory evidence directly linked to the generated renewal priorities. This study provides a generative artificial intelligence (GAI)-driven and interpretable evaluation framework for urban renewal decision-making, facilitating precision-oriented and intelligent urban regeneration. Full article
(This article belongs to the Special Issue Big Data-Driven Urban Spatial Perception)
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25 pages, 8887 KB  
Article
Connectivity-Oriented Ecological Security Pattern Construction Through Multi-Scenario Simulation Approach: A Case Study of Hefei City, China
by Fengyu Wang, Jiawei Zheng, Yaping Huang, Shiwei Lu and Ruiqi Liu
Land 2025, 14(12), 2419; https://doi.org/10.3390/land14122419 - 14 Dec 2025
Cited by 1 | Viewed by 651
Abstract
Rapid urbanization has brought severe threats to regional ecological security. Most research regards ecological security pattern (ESP) focuses on the current situation and ignores future land use and land cover (LULC) impacts. Therefore, this study proposed an ESP construction framework that integrates multi-scenario [...] Read more.
Rapid urbanization has brought severe threats to regional ecological security. Most research regards ecological security pattern (ESP) focuses on the current situation and ignores future land use and land cover (LULC) impacts. Therefore, this study proposed an ESP construction framework that integrates multi-scenario patch-generating land use simulation (PLUS) with ecosystem service value (ESV) evaluation based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Taking Hefei City as a case study, this study predicts land use types under the natural development scenario (NDS), ecological protection scenario (EPS), and economic development scenario (EDS) in 2030. Afterwards, ecological sources are identified by selecting four types of ecosystem services. Ecological corridors and nodes are identified by combining circuit theory and ecological resistance surfaces. The ESP is constructed based on a generic, landscape-scale connectivity-oriented perspective. The results showed that: (1) the LULC in Hefei City underwent significant changes between 2000 and 2020. The main manifestations are the reduction in cropland and increase in construction land. The expansion of construction land under EDS is the most significant. (2) The spatial distribution patterns of ESV for 2020 and three scenarios in 2030 exhibit marked heterogeneity. (3) According to the simulated scenarios in 2030, ecological corridors form a structure that is sparser in the central region and denser in the southern region; ecological pinch points are predominantly located within the Zipeng Mountain and the region situated to the south of Chaohu; ecological barrier points are mainly distributed at the edge of the built-up area and in the middle of long-distance ecological corridors. The ecological network structure under EPS has been expanded and reinforced. (4) Hefei City exhibits an ESP of “Four zones, Three screens, One network, Multiple nodes” as a whole, indicating an ecological security pattern with relatively higher potential ecological connectivity at the city scale. This study aims to provide scientific support for the development of Hefei City in society, economy and ecological security. Full article
(This article belongs to the Special Issue Big Data-Driven Urban Spatial Perception)
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15 pages, 1658 KB  
Article
Subjective and Objective Perception Differences of Urban Public Service Facilities and Their Implications for Planning Decisions
by Haijuan Zhao, Daoyuan Chen, Ying Li, Guoen Wang, Xinlei Lian and Hangyi Ren
Land 2025, 14(12), 2418; https://doi.org/10.3390/land14122418 - 14 Dec 2025
Cited by 1 | Viewed by 675
Abstract
China’s urban development model has shifted from incremental expansion to inventory quality improvement. The demand for planning and allocation of spatial resources has moved from “having or not having” to “being good or not”, and the importance of planning, implementation, evaluation, and feedback [...] Read more.
China’s urban development model has shifted from incremental expansion to inventory quality improvement. The demand for planning and allocation of spatial resources has moved from “having or not having” to “being good or not”, and the importance of planning, implementation, evaluation, and feedback in urban spatial planning and construction has gradually increased. How to accurately allocate resources in response to the public’s demands for urban construction and effectively enhance the public’s satisfaction and sense of gain regarding urban construction has become an important issue in current planning decisions. To strengthen public perception and feedback in spatial planning governance, this paper conducts empirical research on methods such as the subjective and objective perception differences of urban public service facilities by using the social satisfaction survey data from the East Lake High-tech Development Zone of Wuhan. Thereby, it identifies the characteristics of subjective and objective perception differences of urban public service facilities and puts forward targeted optimization methods for planning decisions. This paper can provide a reference for the precision planning and decision-making of public service facilities in the next step. Full article
(This article belongs to the Special Issue Big Data-Driven Urban Spatial Perception)
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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
Cited by 1 | Viewed by 662
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
Cited by 1 | Viewed by 950
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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