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Volume 14
Issue 4
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ISPRS Int. J. Geo-Inf., Volume 14, Issue 4 (April 2025) – 46 articles

Cover Story (view full-size image): This paper introduces conceptual neighborhood graphs for topological relations in discretized space (ℤ2), addressing a critical gap in qualitative spatial reasoning. Using simulation protocols, we derive comprehensive neighborhood structures for translation and isotropic scaling operations that capture topological transitions in pixel-based representations. Our findings reveal that these discrete neighborhood graphs maintain structural consistency with their continuous counterparts when appropriately aggregated. This framework enables crucial functionalities for modern spatial information systems—detecting temporal events, identifying missing state changes, enhancing query systems, and providing richer context for spatial prepositions—thereby advancing applications from cadastral analysis to remotely sensed data processing. View this paper
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25 pages, 11681 KiB  
Article
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
Viewed by 129
Abstract
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  
Article
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
Viewed by 377
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  
Article
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
Viewed by 250
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  
Article
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
Viewed by 149
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  
Article
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
Viewed by 218
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  
Article
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
Viewed by 213
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  
Article
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
Viewed by 202
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  
Article
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
Viewed by 236
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  
Article
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
Viewed by 289
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  
Article
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
Viewed by 379
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  
Article
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
Viewed by 167
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  
Article
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
Viewed by 327
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  
Article
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
Viewed by 417
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  
Article
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
Viewed by 308
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  
Article
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
Viewed by 248
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  
Article
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
Viewed by 239
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  
Article
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
Viewed by 255
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  
Article
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
Viewed by 139
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  
Article
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
Viewed by 483
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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22 pages, 8426 KiB  
Article
Multi-Scenario Forecasting of Land Use and Ecosystem Service Values in Coastal Regions: A Case Study of the Chaoshan Area, China
by Zili Xiong, Song Yao, Hongmei Liu and Liang Yu
ISPRS Int. J. Geo-Inf. 2025, 14(4), 160; https://doi.org/10.3390/ijgi14040160 - 7 Apr 2025
Viewed by 356
Abstract
Modeling changes in ecosystem service value (ESV) resulting from land use/cover change (LUCC) in coastal regions play a crucial role in promoting regional sustainability and guiding policymaking. This study focuses on the Chaoshan region of China and analyzes the impact of land use [...] Read more.
Modeling changes in ecosystem service value (ESV) resulting from land use/cover change (LUCC) in coastal regions play a crucial role in promoting regional sustainability and guiding policymaking. This study focuses on the Chaoshan region of China and analyzes the impact of land use changes in 2000, 2010, and 2020 on ESV. The Patch-generating Land Use Simulation (PLUS) model was used to simulate LUCC for 2030 under three different scenarios: natural development (ND), urban development (UD), and ecological protection (EP). The spatial distribution and aggregation degree of ESV were assessed to explore the intrinsic relationship between land use and ecosystem service value in the Chaoshan region. The results showed the following: (1) The cropland area in the Chaoshan region has significantly decreased, with the per capita cropland area dropping to 113.34 m2 (0.028 acres) by 2020. The continuous expansion of construction land has been mainly concentrated in Shantou, Jieyang, and Chaozhou, with an increasingly evident trend of urban integration among these three cities. By 2030, the growth rate of construction land in the EP scenario is expected to decline, indicating a slowdown in urban expansion. (2) Between 2000 and 2020, Shantou was the only city in the region to experience a decline in total ESV. Low ESV values in the Chaoshan region are primarily concentrated in the southeastern area. As urban integration progresses, ESV values in this region are expected to continue to decline. (3) The ongoing trend of urban integration between Shantou, Chaozhou, and Jieyang may result in the region becoming an ecologically vulnerable area. Close monitoring of potential ecological risks in this area is crucial to ensure a balance between urban development and ecological protection. This study will provide important guidance for land use policies and sustainable development in the Chaoshan region, as well as in similar coastal cities globally. Full article
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19 pages, 2885 KiB  
Article
Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population
by Duo Zhang, Laurette Dubé, Antonia Gieschen, Catherine Paquet and Raja Sengupta
ISPRS Int. J. Geo-Inf. 2025, 14(4), 159; https://doi.org/10.3390/ijgi14040159 - 6 Apr 2025
Viewed by 218
Abstract
This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The findings reveal significant heterogeneity [...] Read more.
This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The findings reveal significant heterogeneity in consumer behaviour, emphasizing the impact of urban geography on shopping decisions. This segmentation also provides valuable insights for retailers optimizing store locations and marketing strategies and for policymakers aiming to enhance urban accessibility. Additionally, our approach strengthens agent-based model (ABM) simulations by incorporating demographic and behavioural diversity, leading to more realistic consumer representations. While integrating loyalty data with synthetic populations mitigates privacy concerns, challenges remain regarding data sparsity and demographic inconsistencies. Future research should explore multi-source data integration and advanced clustering methods. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis. Full article
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34 pages, 9249 KiB  
Article
Spatial Agglomeration Characteristics and Impact Factors of the Cultural and Creative Industries in Harbin
by Zuhang Liu, Daming Xu and Xinyang Wang
ISPRS Int. J. Geo-Inf. 2025, 14(4), 158; https://doi.org/10.3390/ijgi14040158 - 5 Apr 2025
Viewed by 254
Abstract
The cultural and creative industries have garnered widespread attention as an important vehicle for promoting the transformation and upgrading of urban industrial structures. In this investigation, we take Harbin—a city in China with a strong industrial legacy—as a case study. Through kernel density [...] Read more.
The cultural and creative industries have garnered widespread attention as an important vehicle for promoting the transformation and upgrading of urban industrial structures. In this investigation, we take Harbin—a city in China with a strong industrial legacy—as a case study. Through kernel density analysis and the DBSCAN clustering algorithm, we identify and analyze the spatial distribution and spatiotemporal evolution patterns of 157 clusters of cultural and creative industries in Harbin. We construct a Geographic Weighted Regression (GWR) model using 20 indicators from three categories (i.e., social, cultural, and economic) to analyze the factors impacting the agglomeration of cultural and creative industries in Harbin. Our findings reveal that the cultural and creative industries exhibit an agglomeration pattern. A large-scale agglomeration area for large enterprises has formed in the city center, while scattered, small-scale agglomeration zones for emerging small enterprises have formed in newly developed areas on the urban periphery. The GWR analysis indicates that economic factors have the most significant impact on the agglomeration of cultural and creative industries; however, night-time economic facilities show a negative correlation. Among social factors, the convenience of public transportation and new energy transportation infrastructure have a significant impact on industrial agglomeration. Regarding cultural factors, cultural and sports facilities have the greatest influence, while public information kiosks and public arts education facilities exhibit spatial variability. These findings provide a scientific basis for policy formulation and contribute to promoting the healthy development of cultural and creative industries. Full article
(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)
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19 pages, 2893 KiB  
Article
Assess Spatial Equity Considering the Similarity Between GIS-Based Supply and Demand Maps: A New Framework with Case Study in Beijing
by Xiatong Hao, Xiaojian Hu, Ke Zhang and Qian Chen
ISPRS Int. J. Geo-Inf. 2025, 14(4), 157; https://doi.org/10.3390/ijgi14040157 - 4 Apr 2025
Viewed by 228
Abstract
Spatial equity is a critical issue that the supply allocation should align with the level of demand, enabling all community members to equally benefit from the city’s resources and opportunities, yet commonly used assessment methods have inherent limitations. This study proposes a new [...] Read more.
Spatial equity is a critical issue that the supply allocation should align with the level of demand, enabling all community members to equally benefit from the city’s resources and opportunities, yet commonly used assessment methods have inherent limitations. This study proposes a new framework to assess spatial equity based on the evaluation of similarity between GIS-based supply and demand maps and provides a simplified case study that assesses public transportation services across the area inside the Sixth Ring Road of Beijing to facilitate the comprehension of this framework. The results show that while services in this region are relatively spatially equitable, significant spatial inequity remains in certain areas, where targeted policy recommendations are put forward such as promoting innovative transportation solutions and redistributing excessive demand to less congested facilities. The application prospects and future development directions of the proposed framework are thoroughly discussed. This framework stands out for its ease of comprehension, visualization, and general applicability. Specifically, it is capable of identifying areas with severe inequity, thus contributing to the establishment of targeted intervention measures to mitigate spatial inequity. Full article
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19 pages, 2850 KiB  
Article
Use and Effectiveness of Chatbots as Support Tools in GIS Programming Course Assignments
by Hartwig H. Hochmair
ISPRS Int. J. Geo-Inf. 2025, 14(4), 156; https://doi.org/10.3390/ijgi14040156 - 2 Apr 2025
Viewed by 267
Abstract
Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. [...] Read more.
Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. Students self-reported perceived improvements in skills and the use of different help resources across three home assignments of varying complexity and spatial context. In group discussions, students shared their experiences, strategies, and envisioned future applications of chatbots in GIS programming and beyond. The results indicate that prior programming experience enhances students’ perception of assignment usefulness, and that chatbots serve as a partial replacement for traditional help resources (e.g., websites) in completing assignments. Overall, students expressed positive sentiments regarding chatbot effectiveness, especially for complex spatial tasks. While students were optimistic about the potential of chatbots to enhance future learning, concerns were raised about overreliance on AI, which could hinder the development of independent problem-solving and programming skills. In conclusion, this study offers valuable insights into optimizing chatbot integration in GIS education. Full article
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26 pages, 6562 KiB  
Article
A Model of Building Changes to Support Comparative Studies and Open Discussions on Densification
by Bénédicte Bucher, Juste Raimbault, Mouhamadou Ndim, Ana-Maria Raimond, Julien Perret, Sebastian Dembski and Mathias Jehling
ISPRS Int. J. Geo-Inf. 2025, 14(4), 155; https://doi.org/10.3390/ijgi14040155 - 2 Apr 2025
Viewed by 353
Abstract
Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect [...] Read more.
Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect discussions about densification with shared evidence. A specific challenge is to process buildings in city regions and areas in a replicable way across different building data sources. Another challenge is to manage the quality of the representation, i.e., how well the maps represent changes to buildings and how well they can support discussions of densification. Building data and real buildings are different things that sometimes change in an independent way. Addressing these factors requires different forms of expertise, i.e., expertise about the realities depicted in the areas studied, about local data sources, and about advanced matching tools and state-of-the-art densification concepts. We present a collaborative dashboard through which to engage corresponding experts in the production of building change maps and the clarification of related concepts. Full article
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25 pages, 13957 KiB  
Article
A Building Group Recognition Method Integrating Spatial and Semantic Similarity
by Huimin Liu, Wenpei Wang, Jianbo Tang, Min Deng and Chen Ding
ISPRS Int. J. Geo-Inf. 2025, 14(4), 154; https://doi.org/10.3390/ijgi14040154 - 1 Apr 2025
Viewed by 222
Abstract
Recognition and detection of building groups are core tasks in cartographic research. Current recognition methods that rely on spatial and geometric features often neglect semantic aspects, failing to account for the complex relationships between buildings and their real-world semantic associations. This limitation hampers [...] Read more.
Recognition and detection of building groups are core tasks in cartographic research. Current recognition methods that rely on spatial and geometric features often neglect semantic aspects, failing to account for the complex relationships between buildings and their real-world semantic associations. This limitation hampers the ability to fully capture human understanding of the real world. Based on this, this paper proposes a novel method for building group recognition that integrates both spatial geometric and semantic features. The method effectively identifies building group structures by considering spatial proximity, geometry, and semantic similarity. First, spatial proximity between buildings is defined by constructing a neighborhood graph based on Delaunay triangulation, and the spatial geometric features of each building are extracted. The spatial distance and semantic intensity relationships between Point of Interest (POI) data and buildings are used for semantic feature extraction. Subsequently, a spatial–semantic dual clustering strategy is applied in two stages to aggregate the buildings and generate preliminary grouping results. Finally, the grouping results are refined through an optimal graph segmentation strategy, which ensures both global and local optimization. The proposed method is applied to two areas in Shenzhen City, China, and the experimental results demonstrate that, compared with other methods, it more effectively identifies building groups with coherent spatial, geometric, and semantic features, improving the Adjusted Rand Index (ARI) from 0.589 to 0.701. This approach provides significant support for intelligent map generalization and personalized knowledge services in the era of big data. Full article
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27 pages, 2788 KiB  
Article
Critical Success Factors of Participatory Community Planning with Geospatial Digital Participatory Platforms
by Karl Atzmanstorfer, Mona Bartling, Barbora Haltofová, Leo Zurita-Arthos, Judith Grubinger-Preiner and Anton Eitzinger
ISPRS Int. J. Geo-Inf. 2025, 14(4), 153; https://doi.org/10.3390/ijgi14040153 - 1 Apr 2025
Viewed by 383
Abstract
In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning [...] Read more.
In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning processes within their communities. In addition, DPPs facilitate the integration of the spatial domain into participatory community planning. In this paper, we assess the most important Critical Success Factors (CSFs) of participatory community planning with geospatial DPPs, and analyze the potential, opportunities, and challenges associated with integrating these platforms into community planning. We analyze the results of a digital questionnaire that we shared with a selected group of expert scholars and community stakeholders. We then contextualize this feedback with our experiences from the piloting phase and commercial roll-out of the ‘Bürgercockpit’-application for participatory community planning within the Austrian Agenda21-framework. As a result, we identify the most important CSFs of participatory community planning with geospatial DPPs. This set of CSFs should provide a better orientation on how to complement well-established analog participatory methods and practices with geospatial DPPs for the co-production of shared visions and solutions, ultimately empowering all stakeholders of a planning process to better manage their communities. Full article
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15 pages, 2645 KiB  
Article
A New Earth System Spatial Grid Extending the Great Circle Arc QTM: The Spherical Geodesic Degenerate Octree Grid
by Yilin Ren, Mengyun Zhou and Aijun Zhong
ISPRS Int. J. Geo-Inf. 2025, 14(4), 152; https://doi.org/10.3390/ijgi14040152 - 1 Apr 2025
Viewed by 276
Abstract
An Earth system spatial grid (ESSG) is an extension of a discrete global grid system (DGGS) in the radial direction. It is an important tool for organizing, representing, simulating, analyzing, sharing, and visualizing spatial data. The existing ESSGs suffer from complex spatial relationships [...] Read more.
An Earth system spatial grid (ESSG) is an extension of a discrete global grid system (DGGS) in the radial direction. It is an important tool for organizing, representing, simulating, analyzing, sharing, and visualizing spatial data. The existing ESSGs suffer from complex spatial relationships and significant geometric distortion. To mitigate these problems, a spherical geodesic degenerate octree grid (SGDOG) and its encoding and decoding schemes are proposed in this paper. The SGDOG extends the great circle arc QTM in the radial direction and adopts different levels of the great circle arc QTM at different radial depths. The subdivision of SGDOG is simple and clear, and has multi-level characteristics. The experimental results demonstrate that the SGDOG has advantages of simple spatial relationships, convergent volume distortion, and real-time encoding and decoding. The SGDOG has the potential to organize and manage global spatial data and perform large-scale visual analysis of the Earth system. Full article
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17 pages, 8270 KiB  
Article
The Impact of Residents’ Daily Internet Activities on the Spatial Distribution of Online Fraud: An Analysis Based on Mobile Phone Application Usage
by Guangwen Song, Jiajun Liang, Linlin Wu, Lin Liu and Chunxia Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(4), 151; https://doi.org/10.3390/ijgi14040151 - 31 Mar 2025
Viewed by 228
Abstract
In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to [...] Read more.
In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines. Full article
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19 pages, 10779 KiB  
Article
Conceptual Neighborhood Graphs of Topological Relations in Z2
by Brendan Patrick Hall and Matthew Paul Dube
ISPRS Int. J. Geo-Inf. 2025, 14(4), 150; https://doi.org/10.3390/ijgi14040150 - 31 Mar 2025
Viewed by 263
Abstract
Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in [...] Read more.
Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in continuous spaces has the concept of organizing these relations into a larger framework (called a conceptual neighborhood graph) been considered. Previous work leveraged matrix differences to derive the anisotropic scaling neighborhood for these relations. In this paper, a simulation protocol is used to derive conceptual neighborhood graphs of qualitative topological relations in Z2 for the operations of translation and isotropic scaling. It is further shown that, when aggregating raster relations into their continuous counterparts and collapsing neighborhood connections within these groups, the familiar conceptual neighborhood structures for continuous regions appear. Full article
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