ISPRS International Journal of Geo-Information

25 pages, 11681 KiB

Open AccessArticle

Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach

by Aitor Salas-Peña and Juan Carlos García-Palomares

ISPRS Int. J. Geo-Inf. 2025, 14(4), 179; https://doi.org/10.3390/ijgi14040179 - 20 Apr 2025

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to [...] Read more.

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

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25 pages, 6045 KiB

Open AccessArticle

Spatial Analysis of Urban Expansion and Energy Consumption Using Nighttime Light Data: A Comparative Study of Google Earth Engine and Traditional Methods for Improved Living Spaces

by Thidapath Anucharn, Phongsakorn Hongpradit, Niti Iamchuen and Supattra Puttinaovarat

ISPRS Int. J. Geo-Inf. 2025, 14(4), 178; https://doi.org/10.3390/ijgi14040178 - 18 Apr 2025

Abstract

This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to [...] Read more.

This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to 2023 to assess urban growth dynamics. The primary objectives are to (1) evaluate the performance of GEE and UNSUP in nighttime light data processing, (2) validate urban area classification accuracy using multiple assessment metrics, and (3) examine the relationship between nighttime light intensity and electricity consumption through Pearson’s correlation analysis, thereby establishing urban growth patterns. The methodological framework incorporates a dual-threshold classification mechanism in GEE and K-means clustering in traditional geospatial software. Accuracy assessment is conducted using 256 stratified random sampling points, complemented by land use and land cover (LULC) data for ground truth validation. The results indicate that GEE consistently outperforms UNSUP, achieving overall accuracy values between 0.80 and 0.82, compared to 0.73 and 0.76 for UNSUP. The Kappa coefficient for GEE ranges from 0.60 to 0.65, whereas UNSUP demonstrates lower agreement with ground truth data (0.44–0.52). Furthermore, both approaches reveal a significant correlation between electricity consumption and nighttime light intensity, with R² = 0.9744 for GEE and R² = 0.9759 for UNSUP, confirming the efficacy of nocturnal illumination data in urban expansion monitoring. The findings indicate that urban areas in Chiang Mai have expanded by approximately 70% over the study period. This research contributes to the field by demonstrating the effectiveness of integrated geospatial methodologies in urban development analysis. The findings offer urban planners and policymakers critical insights for sustainable urban growth management and decision-making. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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21 pages, 10240 KiB

Open AccessArticle

A Study of the Non-Linear Relationship Between Urban Morphology and Vitality in Heritage Areas Based on Multi-Source Data and Machine Learning: A Case Study of Dalian

by He Li and Li Miao

ISPRS Int. J. Geo-Inf. 2025, 14(4), 177; https://doi.org/10.3390/ijgi14040177 - 18 Apr 2025

Abstract

The preservation of historic heritage not only fosters cultural significance and socio-economic development, but also enhances urban competitiveness. Investigating the vitality of historic urban areas is crucial for measuring their developmental attractiveness, contributing to more effective preservation and planning. However, existing research primarily [...] Read more.

The preservation of historic heritage not only fosters cultural significance and socio-economic development, but also enhances urban competitiveness. Investigating the vitality of historic urban areas is crucial for measuring their developmental attractiveness, contributing to more effective preservation and planning. However, existing research primarily focuses on urban areas, leaving the applicability of urban form elements to heritage sites and their influence mechanisms unclear. This study employs XGBoost and SHAP, utilizing geographic big data and deep learning techniques, to determine whether the urban form elements impacting the vitality of heritage and urban areas are the same or exhibit different spatial distributions and diurnal variations. Empirical analysis of Dalian reveals significant diurnal variations in the factors affecting vitality, along with distinct key elements for both heritage and urban areas. This study is innovative in being the first to apply deep learning methods to analyze the factors influencing the vitality of Dalian’s heritage areas at the district scale, providing theoretical support for enhancing vitality and promoting urban development. Full article

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28 pages, 16918 KiB

Open AccessArticle

Static–Dynamic Analytical Framework for Urban Health Resilience Evaluation and Influencing Factor Exploration from the Perspective of Public Health Emergencies—Case Study of 61 Cities in Mainland China

by Meijie Chen, Mingjun Peng, Bowen Li, Zhongliang Cai and Rui Li

ISPRS Int. J. Geo-Inf. 2025, 14(4), 176; https://doi.org/10.3390/ijgi14040176 - 17 Apr 2025

Abstract

With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation [...] Read more.

With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation and influencing factor exploration was proposed, which helped not only to analyze the basic static urban health resilience (SUHRI) under normal conditions but also to evaluate the dynamic urban health resilience (DURHI) under an external epidemic shock. The epidemic dynamic model could reasonably simulate the epidemic change trend and quantitatively evaluate resistance and recovery capacity, and the proposed influencing factor exploration model improved the model fitness by filtering out the influence of population flow autocorrelation existing in the residuals. SUHRI and DUHRI, and their corresponding key influencing factors, were compared and discussed. The results of the static–dynamic integration and difference score showed that 60.6% cities within the study area had a similar performance on SUHRI and DUHRI, but there was also a typical difference: some regional hubs exhibited high SUHRI but had only mid-level DUHRI, which was attributed to stronger external disturbances such as higher population mobility. The key influencing factors for static and dynamic urban health resilience also vary. Hospital capacity and income had the strongest influence on static urban health resilience but a relatively weaker or even non-significant correlation with dynamic urban health resilience sub-indices. The extracted population flow eigenvector collection had the strongest influence on dynamic urban health resilience, as it represents the population flow connection among cities, which could reflect the information of policy response, such as policy stringency and support intensity. We hope that our study will shed some light on constructing more resilient urban systems and being prepared for future public health emergencies. Full article

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23 pages, 13812 KiB

Open AccessArticle

Three-Dimensional Outdoor Pedestrian Road Network Map Construction Based on Crowdsourced Trajectory Data

by Jianbo Tang, Tianyu Zhang, Junjie Ding, Ke Tao, Chen Yang, Jianbing Xiang and Xia Ning

ISPRS Int. J. Geo-Inf. 2025, 14(4), 175; https://doi.org/10.3390/ijgi14040175 - 17 Apr 2025

Abstract

Due to the complexity of outdoor environments, we still face challenges in collecting up-to-date outdoor road network maps because of high data collection costs, resulting in a lack of navigation road network maps in outdoor scenarios. Existing road network extraction methods are mainly [...] Read more.

Due to the complexity of outdoor environments, we still face challenges in collecting up-to-date outdoor road network maps because of high data collection costs, resulting in a lack of navigation road network maps in outdoor scenarios. Existing road network extraction methods are mainly divided into trajectory data-based and remote sensing image-based methods. Due to factors such as tree occlusion, methods based on remote sensing images struggle to extract complete road information in outdoor environments. The methods based on trajectory data mainly use vehicle trajectories to extract two-dimensional roads, lacking three-dimensional (3D) road information such as elevation and slope, which are important for navigation path planning in outdoor scenarios. Given this, this paper proposes a hierarchical map construction method for extracting the three-dimensional outdoor pedestrian road network based on crowdsourced trajectory data. This method models the pedestrian road network as a graph composed of pedestrian areas, intersections, and road segments connecting these areas. Three-dimensional roads within and between the intersection areas are generated hierarchically. Experiments and comparative analyses were conducted using real-world outdoor trajectory datasets. Results show that the proposed method has higher accuracy in extracting 3D road information than existing methods. Full article

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24 pages, 7953 KiB

Open AccessArticle

Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China

by Yingxi Chen, Yan Xu and Nannan Ye

ISPRS Int. J. Geo-Inf. 2025, 14(4), 174; https://doi.org/10.3390/ijgi14040174 - 17 Apr 2025

Abstract

Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level [...] Read more.

Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level units and employing spatial autocorrelation analysis, multiple linear regression, and mixed geographically weighted regression (MGWR), this study reveals the spatial heterogeneity and key driving factors of non-grainization. The results indicate strong spatial dependence, with persistent high–high clusters in economically developed southern/coastal areas and low–low clusters in northern regions. Furthermore, the driving mechanism shifted significantly over the two decades. Early constraints from natural endowments (e.g., elevation’s positive impact significantly weakened post 2010) and individual economics diminished with technological progress, while macroeconomic development became dominant, influencing both scale and structure. Infrastructure improvements (reflected by rural electricity use) consistently limited non-grainization; some factors showed phased effects, and annual mean precipitation emerged as a significant influence in 2020. MGWR revealed substantial, dynamic spatial heterogeneity in these drivers’ impacts across different periods. These findings highlight the importance of geoinformation tools in managing regional disparities. Integrating spatial and socio-economic analysis offers practical insights for policymakers to develop targeted strategies that balance food security with agricultural diversification. Full article

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19 pages, 5342 KiB

Open AccessArticle

Spatio-Temporal Analysis of the Redundancies of Construction Land in the Beijing-Tianjin-Hebei Region (2000–2020)

by Ting Zhang, Rui Shen, Yongqing Xie, Haowen Gao and Weitong Lv

ISPRS Int. J. Geo-Inf. 2025, 14(4), 173; https://doi.org/10.3390/ijgi14040173 - 16 Apr 2025

Abstract

Excessive redundancy of construction land in county-level units within the Beijing-Tianjin-Hebei region has become a significant obstacle to achieving high-quality development. The objective of this study is to discover the spatial and temporal patterns of redundancy of construction land, with a view to [...] Read more.

Excessive redundancy of construction land in county-level units within the Beijing-Tianjin-Hebei region has become a significant obstacle to achieving high-quality development. The objective of this study is to discover the spatial and temporal patterns of redundancy of construction land, with a view to providing insights for promoting efficient land use. The study employs the SBM-DEA model, Markov transfer probability matrix analysis, and multiple regression analysis to analyze the spatial change characteristics, spatial differentiation, and influencing factors of construction land redundancy in this Beijing-Tianjin-Hebei county unit during the period of 2000–2020. The study shows that the Beijing-Tianjin-Hebei county unit has a serious oversupply of land and, combined with the reasons for redundancy in each sub-region, the degree of spatial redundancy has already formed a spatial lock-in effect. The degree of redundancy of construction land is affected by a variety of factors such as location, scale, economy, and facilities. Furthermore, the study puts forward suggestions for improving land use efficiency in Beijing-Tianjin-Hebei county units by adjusting the construction land supply and demand relationship, mechanisms to facilitate the flow of development factors, and strengthening land use supervision. These measures aim to reduce redundancy of construction land and support sustainable high-quality development in the region. Full article

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21 pages, 5892 KiB

Open AccessArticle

Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model

by Zehao Yuan, Xuanyan Chen, Biyu Chen, Yubo Luo, Yu Zhang, Wenxin Teng and Chao Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(4), 172; https://doi.org/10.3390/ijgi14040172 - 14 Apr 2025

Abstract

The origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of [...] Read more.

The origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of real data, help address privacy issues and data scarcity. Based on Generative Adversarial Networks (GAN), OD matrix generation models, which can effectively generate a synthetic OD matrix, help to address the challenge of obtaining OD matrix data in ITS research. However, existing OD matrix generation methods can only handle with tens of nodes. To address this challenge, this study proposes the Origin–Destination Progressive Growing Generative Adversarial Networks (OD-PGGAN) for large-scale OD matrix generation task which adapt the PGGAN architecture. OD-PGGAN adopts a progressive learning strategy to gradually learn the structure of the OD matrix from a coarse to fine scale. OD-PGGAN utilizes multi-scale generators and discriminators to perform generation and discrimination tasks at different spatial resolutions. OD-PGGAN introduces a geography-based upsampling and downsampling algorithm to maintain the geographical significance of the OD matrix during spatial resolution transformations. The results demonstrate that the proposed OD-PGGAN can generate a large-scale synthetic OD matrix with 1024 nodes that have the same distribution as the real sample and outperforms two classical methods. The OD-PGGAN can effectively provide reliable synthetic data for transportation applications. Full article

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23 pages, 2652 KiB

Open AccessArticle

Innovation and Research to Support Policies on Sustainable Development Goals: An Integrated ICT Platform for the Definition and Monitoring of Programs in Puglia Region, Italy

by Antonella Lerario, Michele Chieco, Maria Silvia Binetti, Vito Felice Uricchio, Massimo Clemente and Carmine Massarelli

ISPRS Int. J. Geo-Inf. 2025, 14(4), 171; https://doi.org/10.3390/ijgi14040171 - 14 Apr 2025

Abstract

The 2030 Agenda for Sustainable Development, approved by the international community, represents a global strategic guide with 17 Sustainable Development Goals (SDGs) and 169 interconnected targets, aimed at promoting equitable economic, social and environmental development. In this context, innovation and research can play [...] Read more.

The 2030 Agenda for Sustainable Development, approved by the international community, represents a global strategic guide with 17 Sustainable Development Goals (SDGs) and 169 interconnected targets, aimed at promoting equitable economic, social and environmental development. In this context, innovation and research can play a crucial role in supporting policies for the achievement of SDGs, especially at local and regional levels. This article reports what was developed in the Puglia Region according to a double action. On the one hand, the creation of an IT platform to improve collaboration between legislative institutions and research centres facilitates the collection and transfer of data as best practices to support political decisions. On the other hand, practical experimentation on issues related to regional development is conducted, with particular attention to sustainability-oriented partnerships, legislative needs, and knowledge of the territory. The new information system, based on a geo-database and developed entirely with open source software, collects regional regulatory data on SDGs, cooperation projects, and technical and scientific documents contributing to the knowledge of the application of plans and programmes applied to the territory. This tool, in addition to mapping relevant projects, represents an important resource for monitoring progress and supporting sustainable development policies, facilitating the sharing of information between local, national, and European actors. Full article

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20 pages, 4691 KiB

Open AccessArticle

Text Geolocation Prediction via Self-Supervised Learning

by Yuxing Wu, Zhuang Zeng, Kaiyue Liu, Zhouzheng Xu, Yaqin Ye, Shunping Zhou, Huangbao Yao and Shengwen Li

ISPRS Int. J. Geo-Inf. 2025, 14(4), 170; https://doi.org/10.3390/ijgi14040170 - 12 Apr 2025

Abstract

Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount [...] Read more.

Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. Specifically, GeoSG integrates spatial distance and hierarchical constraints to characterize the interactions of POIs and text in a geographic relationship graph. And it designs two self-supervised tasks to train a shared network to learn the relationships among POIs and texts. Finally, the text geolocations are inferred based on the trained shared network. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art baselines and is robust. This study provides a methodological reference for geolocating various text documents and offers a solution for numerous geographic intelligence tasks that lack labeled samples. Full article

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26 pages, 11131 KiB

Open AccessArticle

MVCF-TMI: A Travel Mode Identification Framework via Contrastive Fusion of Multi-View Trajectory Representations

by Yutian Lei, Xuefeng Guan and Huayi Wu

ISPRS Int. J. Geo-Inf. 2025, 14(4), 169; https://doi.org/10.3390/ijgi14040169 - 11 Apr 2025

Abstract

Travel mode identification (TMI) plays a crucial role in intelligent transportation systems by accurately identifying travel modes from Global Positioning System (GPS) trajectory data. Given that trajectory data inherently exhibit spatial and kinematic patterns that complement each other, recent TMI methods generally combine [...] Read more.

Travel mode identification (TMI) plays a crucial role in intelligent transportation systems by accurately identifying travel modes from Global Positioning System (GPS) trajectory data. Given that trajectory data inherently exhibit spatial and kinematic patterns that complement each other, recent TMI methods generally combine these characteristics through image-based projections or direct concatenation. However, such approaches achieve only shallow fusion of these two types of features and cannot effectively align them into a shared latent space. To overcome this limitation, we introduce multi-view contrastive fusion (MVCF)-TMI, a novel TMI framework that enhances identification accuracy and model generalizability by aligning spatial and kinematic views through multi-view contrastive learning. Our framework employs multi-view learning to separately extract spatial and kinematic features, followed by an inter-view contrastive loss to optimize feature alignment in a shared subspace. This approach enables cross-view semantic understanding and better captures complementary information across different trajectory representations. Extensive experiments show that MVCF-TMI outperforms baseline methods, achieving 86.45% accuracy on the GeoLife dataset. The model also demonstrates strong generalization by transferring knowledge from pretraining on the large-scale GeoLife dataset to the smaller SHL dataset. Full article

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17 pages, 14325 KiB

Open AccessArticle

Unveiling the Spatial Inequality of Accessibility to High-Quality Healthcare Resources in the Beijing–Tianjin–Hebei Urban Agglomeration of China: A Focus on the Impacts of Intercity Patient Mobility

by Yandi Wang, Lin Chen, Binglin Liu and Zhuolin Tao

ISPRS Int. J. Geo-Inf. 2025, 14(4), 168; https://doi.org/10.3390/ijgi14040168 - 11 Apr 2025

Abstract

The equality of accessibility to high-quality healthcare resources is an important issue in the development of urban agglomerations. However, comprehensive consideration of the impacts of intercity patient mobility and multilevel transportation networks is still lacking. This study develops a novel directional two-step floating [...] Read more.

The equality of accessibility to high-quality healthcare resources is an important issue in the development of urban agglomerations. However, comprehensive consideration of the impacts of intercity patient mobility and multilevel transportation networks is still lacking. This study develops a novel directional two-step floating catchment area method for measuring spatial accessibility to high-quality hospitals in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. This method emphasizes the direction of intercity patient mobility caused by the hierarchy of high-quality healthcare resource distributions. Empirical analyses were conducted based on subdistrict-level population census data in 2020, 3-A hospital data from healthcare commissions, and door-to-door travel time data via multilevel intercity transportation networks from online maps in 2023. The analyses revealed obvious spatial inequalities in accessibility to high-quality healthcare resources in the BTH urban agglomeration, which is primarily caused by intercity inequality. Intercity patient mobility, however, can significantly mitigate the spatial inequality of healthcare accessibility within the BTH urban agglomeration. Moreover, it was determined that intracity first-mile and last-mile transfer transportation is the major barrier to intercity healthcare seeking and accessibility. This study has valuable implications for the planning and management of high-quality healthcare resources and intercity patient mobility in the BTH urban agglomeration. The developed methods are useful for measuring healthcare accessibility and inequality at the urban agglomeration scale. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)

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29 pages, 13384 KiB

Open AccessArticle

Spatiotemporal Analysis of Urban Vitality and Its Drivers from a Human Mobility Perspective

by Youwan Wu, Chenxi Xie, Aiping Zhang, Tianhong Zhao and Jinzhou Cao

ISPRS Int. J. Geo-Inf. 2025, 14(4), 167; https://doi.org/10.3390/ijgi14040167 - 11 Apr 2025

Abstract

Urban vitality is a critical metric for assessing the development and appeal of urban areas, playing a pivotal role in urban planning and management. Traditionally, surveys and census data have been used to measure urban vitality; however, these methods are often time-consuming, resource-intensive, [...] Read more.

Urban vitality is a critical metric for assessing the development and appeal of urban areas, playing a pivotal role in urban planning and management. Traditionally, surveys and census data have been used to measure urban vitality; however, these methods are often time-consuming, resource-intensive, and limited in coverage. This study addresses these limitations by employing mobile phone signaling data to develop a model for quantifying urban vitality and exploring its spatiotemporal distribution patterns. By integrating socioeconomic, street view, and points-of-interest (POI) data, this study utilizes linear regression and geographically weighted regression (GWR) models to analyze the influence of various factors on urban vitality. The SHapley Additive exPlanations (SHAP) method is then applied to interpret model predictions and identify key determinants of urban vitality. Using Shenzhen as a case study, the results reveal pronounced spatial disparities in vitality. Among all variables, bus stop density, cultural services, and employment density consistently exhibit significant effects on urban vitality. The proposed urban vitality quantification framework enables high-resolution and wide-coverage monitoring of urban vitality, providing scientific support and decision-making guidance for understanding the dynamic characteristics of urban spaces and optimizing urban functional layouts. Full article

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18 pages, 14812 KiB

Open AccessArticle

Design and Development of a Local-First Collaborative 3D WebGIS Application for Mapping

by Bohua Wang, Qiansheng Zhao, Di Zeng, Yibin Yao, Chunchun Hu and Nianxue Luo

ISPRS Int. J. Geo-Inf. 2025, 14(4), 166; https://doi.org/10.3390/ijgi14040166 - 10 Apr 2025

Abstract

Real-time collaborative WebGIS represents a significant trend in the evolution of Geographic Information Systems. Current commercial collaborative WebGIS platforms, such as ArcGIS Online and Felt, rely on centralized servers for data storage and spatial analysis. However, this centralized architecture poses notable limitations: the [...] Read more.

Real-time collaborative WebGIS represents a significant trend in the evolution of Geographic Information Systems. Current commercial collaborative WebGIS platforms, such as ArcGIS Online and Felt, rely on centralized servers for data storage and spatial analysis. However, this centralized architecture poses notable limitations: the software becomes non-functional in the absence of a network connection or if the service is discontinued. Moreover, data ownership resides with the commercial providers, exposing users to potential data loss in the event of service disruptions. To address these challenges, this paper introduces the concept of local-first software into WebGIS. By leveraging Conflict-free Replicated Data Types (CRDTs) and advanced web technologies, we develop a user-friendly, interactive, and offline-capable local-first WebGIS application that supports real-time collaboration. The application enables multi-user collaborative editing, offline functionality, and efficient browser-based spatial analysis. This paper outlines the design methodology and system prototype for the local-first WebGIS application, utilizing open-source software and libraries throughout the development process. Practical examples are provided to demonstrate the application’s functionality. The proposed solution enhances real-time collaboration and data security in WebGIS, ultimately improving user productivity and collaborative experiences. Full article

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21 pages, 4601 KiB

Open AccessArticle

Scientific Production on GPS Trajectory Clustering: A Bibliometric Analysis

by Gary Reyes, Roberto Tolozano-Benites, Laura Lanzarini, César Estrebou and Aurelio F. Bariviera

ISPRS Int. J. Geo-Inf. 2025, 14(4), 165; https://doi.org/10.3390/ijgi14040165 - 10 Apr 2025

Abstract

Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these algorithms and even unique methods considered as “novel” for science [...] Read more.

Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these algorithms and even unique methods considered as “novel” for science have emerged. This work aimed to analyze the scientific production that exists around the topic “GPS trajectories clustering” by means of bibliometrics. Therefore, a total of 559 articles from the main collection of Scopus were analyzed, initially filtering the generated sample to discard any articles that did not have a direct relationship with the topic to be analyzed. This analysis establishes an ideal environment for other disciplines and researchers since it provides a current state of the trend of the subject of study in their field of research. Full article

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15 pages, 7870 KiB

Open AccessArticle

Mapping Spatial Inequity in Urban Fire Service Provision: A Moran’s I Analysis of Station Pressure Distribution

by Jianyu Li and Mingxing Hu

ISPRS Int. J. Geo-Inf. 2025, 14(4), 164; https://doi.org/10.3390/ijgi14040164 - 10 Apr 2025

Abstract

Fire security is an important part of the urban infrastructure system. In existing quantitative application research, fire stations have always used a maximally covered approach to optimize the layout of urban functions. With this planning, different fire stations face different firefighting pressures (FPs) [...] Read more.

Fire security is an important part of the urban infrastructure system. In existing quantitative application research, fire stations have always used a maximally covered approach to optimize the layout of urban functions. With this planning, different fire stations face different firefighting pressures (FPs) because of the different distributions of fires. On the basis of FPs, we explored the characteristics of firefighting behavior at fire stations in Nanjing using Moran’s I. We found that a large portion of the stations are in the periphery of high-fire-risk areas, which tend to experience infrequent fires near the stations. These stations organize inter-regional firefighting to reduce the risk of fires in urban areas in general. We attempt to change the maximally covered approach so that it can be used to quantitatively solve the problem of fire planning. Full article

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24 pages, 2416 KiB

Open AccessArticle

Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction

by Nihad Brahimi, Huaping Zhang and Zahid Razzaq

ISPRS Int. J. Geo-Inf. 2025, 14(4), 163; https://doi.org/10.3390/ijgi14040163 - 9 Apr 2025

Abstract

Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our [...] Read more.

Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. It offers a comprehensive framework for the integration of various data. The eX-STIN model enhances the previous one by utilizing Ensemble Empirical Mode Decomposition (EEMD), which results in refined feature extraction. It uses Minimum Redundancy Maximum Relevance (mRMR) to find features that are relevant and not redundant, and Shapley Additive Explanations (SHAP) to show how each feature affects the model’s predictions. We conducted extensive experiments that use real car-sharing data to thoroughly evaluate the efficacy of the eX-STIN model. The studies revealed the model’s ability to accurately represent the relationships among temporal, spatial, and spatio-temporal features, outperforming the state-of-the-art models. Moreover, the experiments revealed that eX-STIN exhibits enhanced predictive accuracy compared to the USTIN model. This proposed approach enhances both the accuracy of demand prediction and the transparency of resource allocation decisions in car-sharing services. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation (2nd Edition))

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22 pages, 1105 KiB

Open AccessArticle

Computerized Proof of Fundamental Properties of the p-Median Problem Using Integer Linear Programming and a Theorem Prover

by Ting L. Lei and Zhen Lei

ISPRS Int. J. Geo-Inf. 2025, 14(4), 162; https://doi.org/10.3390/ijgi14040162 - 9 Apr 2025

Abstract

The p-median problem is one of the earliest location-allocation models used in spatial analysis and GIS. It involves locating a set of central facilities (the location decision) and allocating customers to these facilities (the allocation decision) so as to minimize the total [...] Read more.

The p-median problem is one of the earliest location-allocation models used in spatial analysis and GIS. It involves locating a set of central facilities (the location decision) and allocating customers to these facilities (the allocation decision) so as to minimize the total transportation cost. It is important not only because of its wide use in spatial analysis but also because of its role as a unifying location model in GIS. A classical way of solving the p-median problem (dating back to the 1970s) is to formulate it as an Integer Linear Program (ILP), and then solve it using off-the-shelf solvers. Two fundamental properties of the p-median problem (and its variants) are the integral assignment property and the closest assignment property. They are the basis for the efficient formulation of the problem, and are important for studying the p-median problems and other location-allocation models. In this paper, we demonstrate that these fundamental properties of the p-median can be proven mechanically using integer linear programming and theorem provers under the program-as-proof paradigm. While these theorems have been proven informally, mechanized proofs using computers are fail-safe and contain no ambiguity. The presented proof method based on ILP and the associated definitions of problem data are general, and we expect that they can be generalized and extended to prove the theoretical properties of other spatial-optimization models, old or new. Full article

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26 pages, 10308 KiB

Open AccessArticle

Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard

by Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha and Jean Pierre Bizimana

ISPRS Int. J. Geo-Inf. 2025, 14(4), 161; https://doi.org/10.3390/ijgi14040161 - 8 Apr 2025

Abstract

Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. [...] Read more.

Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. This study introduces a machine learning framework designed to assess flood susceptibility and socio-economic vulnerability, particularly in urban areas with limited data. Using Kigali, Rwanda, as a case study, we quantified socio-economic vulnerability through a composite index that includes indicators of sensitivity and adaptive capacity. We utilized a variety of data sources, such as demographic, environmental, and remotely sensing datasets, applying machine learning algorithms like Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), and XGBoost. Among these, MLP achieved the best predictive performance, with an AUC score of 0.902 and an F1-score of 0.86. The findings indicate spatial differences in socio-economic vulnerability, with central and southern Kigali showing greater vulnerability due to a mix of socio-economic challenges and high flood risk. The vulnerability maps created were validated against historical flood records, socio-economic research, and expert insights, confirming their accuracy and relevance for urban risk assessment. Additionally, we tested the framework’s scalability and adaptability in Kampala, Uganda, and Dar es Salaam, Tanzania, showing that making context-specific adjustments to the model improves its transferability. This study offers a solid, data-driven approach for combining assessments of flood susceptibility and socio-economic vulnerability, filling important gaps in urban resilience planning. The results support the advancement of risk-informed decision-making, especially in areas with limited access to detailed socio-economic information. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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ISPRS International Journal of Geo-Information

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