ISPRS International Journal of Geo-Information

26 pages, 41917 KB

Open AccessArticle

Spatiotemporal Heterogeneity of Influencing Factors for Urban Spaces Suitable for Running Workouts Based on Multi-Source Big Data

by Xinyu Di and Jun Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 366; https://doi.org/10.3390/ijgi14090366 - 22 Sep 2025

Viewed by 248

Abstract

With the growing emphasis on running in urban health initiatives, understanding the spatiotemporal dynamics of running behavior has become essential for smart city development. This study harnesses multi-source big data—including running trajectories, points of interest (POIs), and remote sensing data—to systematically analyze factors [...] Read more.

With the growing emphasis on running in urban health initiatives, understanding the spatiotemporal dynamics of running behavior has become essential for smart city development. This study harnesses multi-source big data—including running trajectories, points of interest (POIs), and remote sensing data—to systematically analyze factors influencing running space selection. Through stepwise regression analysis, we identify 16 significant variables encompassing accessibility, diversity, and comfort dimensions. The Geographical and Temporally Weighted Regression (GTWR) model is then employed to uncover distinct spatiotemporal heterogeneity patterns, demonstrating how these factors variably influence running activities across different urban zones and time periods. The methodology and findings contribute to geospatial analysis in urban health studies while providing practical guidance for creating more inclusive, runner-friendly urban environments. Full article

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29 pages, 34222 KB

Open AccessArticle

BFRDNet: A UAV Image Object Detection Method Based on a Backbone Feature Reuse Detection Network

by Liming Zhou, Jiakang Yang, Yuanfei Xie, Guochong Zhang, Cheng Liu and Yang Liu

ISPRS Int. J. Geo-Inf. 2025, 14(9), 365; https://doi.org/10.3390/ijgi14090365 - 21 Sep 2025

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Abstract

Unmanned aerial vehicle (UAV) image object detection has become an increasingly important research area in computer vision. However, the variable target shapes and complex environments make it difficult for the model to fully exploit its features. In order to solve this problem, we [...] Read more.

Unmanned aerial vehicle (UAV) image object detection has become an increasingly important research area in computer vision. However, the variable target shapes and complex environments make it difficult for the model to fully exploit its features. In order to solve this problem, we propose a UAV image object detection method based on a backbone feature reuse detection network, named BFRDNet. First, we design a backbone feature reuse pyramid network (BFRPN), which takes the model characteristics as the starting point and more fully utilizes the multi-scale features of backbone network to improve the model’s performance in complex environments. Second, we propose a feature extraction module based on multiple kernels convolution (MKConv), to deeply mine features under different receptive fields, helping the model accurately recognize targets of different sizes and shapes. Finally, we design a detection head preprocessing module (PDetect) to enhance the feature representation fed to the detection head and effectively suppress the interference of background information. In this study, we validate the performance of BFRDNet primarily on the VisDrone dataset. The experimental results demonstrate that BFRDNet achieves a significant improvement in detection performance, with the mAP increasing by 7.5%. To additionally evaluate the model’s generalization capacity, we extend the experiments to the UAVDT and COCO datasets. Full article

(This article belongs to the Topic State-of-the-Art Object Detection, Tracking, and Recognition Techniques)

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23 pages, 7181 KB

Open AccessTechnical Note

Nav-YOLO: A Lightweight and Efficient Object Detection Model for Real-Time Indoor Navigation on Mobile Platforms

by Cheng Su, Litao Zhu, Wen Dai, Jin Zhou, Jialiang Wang, Yucheng Mao and Jiangbing Sun

ISPRS Int. J. Geo-Inf. 2025, 14(9), 364; https://doi.org/10.3390/ijgi14090364 - 19 Sep 2025

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Abstract

Precise object detection is fundamental to robust indoor navigation and localization. However, the practical deployment of deep learning-based detectors on mobile platforms is frequently impeded by their extensive parameter counts, substantial computational overhead, and prolonged inference latency, rendering them impractical for real-time and [...] Read more.

Precise object detection is fundamental to robust indoor navigation and localization. However, the practical deployment of deep learning-based detectors on mobile platforms is frequently impeded by their extensive parameter counts, substantial computational overhead, and prolonged inference latency, rendering them impractical for real-time and GPU-independent applications. To overcome these limitations, this paper presents Nav-YOLO, a highly optimized and lightweight architecture derived from YOLOv8n, specifically engineered for navigational tasks. The model’s efficiency stems from several key improvements: a ShuffleNetv2-based backbone significantly reduces model parameters; a Slim-Neck structure incorporating GSConv and GSbottleneck modules streamlines the feature fusion process; the VoV-GSCSP hierarchical network aggregates features with minimal computational cost; and a compact detection head is designed using Hybrid Convolutional Transformer Architecture Search (HyCTAS). Furthermore, the adoption of Inner-IoU as the bounding box regression loss accelerates the convergence of the training process. The model’s efficacy is demonstrated through a purpose-built Android application. Experimental evaluations on the VOC2007 and VOC2012 datasets reveal that Nav-YOLO substantially outperforms the baseline YOLOv8n, achieving mAP50 improvements of 10.3% and 5.0%, respectively, while maintaining a comparable parameter footprint. Consequently, Nav-YOLO demonstrates a superior balance of accuracy, model compactness, and inference speed, presenting a compelling alternative to existing object detection algorithms for mobile systems. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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36 pages, 5931 KB

Open AccessArticle

Geospatial Impacts of Land Allotment at the Standing Rock Reservation, USA: Patterns of Gain and Loss

by Stephen L. Egbert and Joshua J. Meisel

ISPRS Int. J. Geo-Inf. 2025, 14(9), 363; https://doi.org/10.3390/ijgi14090363 - 19 Sep 2025

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Abstract

Allotment—the division of Native American reservations into individually-owned plots of land—has been extensively studied; yet there exists a paucity of reservation-level studies at granular geospatial scales, i.e., at the level of examining the impacts of allotment on individuals, families, and clan or tribal [...] Read more.

Allotment—the division of Native American reservations into individually-owned plots of land—has been extensively studied; yet there exists a paucity of reservation-level studies at granular geospatial scales, i.e., at the level of examining the impacts of allotment on individuals, families, and clan or tribal groups. In previous research, we described a new semi-automated method for creating detailed GIS allotment databases and discussed the policies and processes that that lay behind allotment at the Standing Rock Reservation. In this study, we employed our Standing Rock database to map and explore allotment patterns in detail. We primarily focused on patterns of clustering versus dispersion of allotment parcels for individuals, families, and tribal groups by calculating median distance (and other descriptive statistics) and standard distance in GIS. Throughout, we used mapped representations of allotment patterns as visualization tools, both for confirming hypotheses and raising new questions. As anticipated, we discovered patterns of both gain and loss. On the one hand, as we had found earlier, the people at Standing Rock gained land through their insistence on allotments for married women and for children born after the beginning date of allotment (“later-born children”), land they otherwise would not have received. We also confirmed that married women only received half the land that their husbands received and that the early sale of “surplus” reservation lands deprived a future generation of children of the opportunity to receive their own land. Perhaps most importantly, however, we discovered that the belated timing of allotments to married women and later-born children caused their allotments to be located at some distance from those of their husbands or fathers, creating disjunct and dispersed patterns of family land holdings that would have significantly hampered the creation of viable farming and ranching operations. Full article

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27 pages, 13052 KB

Open AccessArticle

A Multi-Scale Geographically Weighted Regression Approach to Understanding Community-Built Environment Determinants of Cardiovascular Disease: Evidence from Nanning, China

by Shuguang Deng, Shuyan Zhu, Xueying Chen, Jinlong Liang and Rui Zheng

ISPRS Int. J. Geo-Inf. 2025, 14(9), 362; https://doi.org/10.3390/ijgi14090362 - 18 Sep 2025

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Abstract

Clarifying how the community-scale built environment shapes the spatial heterogeneity of cardiovascular disease (CVD) prevalence is essential for precision urban health interventions. We integrated CVD prevalence data from the Guangxi Zhuang Autonomous Region Hospital (2020–2022) with 14 built-environment indicators across 77 communities in [...] Read more.

Clarifying how the community-scale built environment shapes the spatial heterogeneity of cardiovascular disease (CVD) prevalence is essential for precision urban health interventions. We integrated CVD prevalence data from the Guangxi Zhuang Autonomous Region Hospital (2020–2022) with 14 built-environment indicators across 77 communities in Xixiangtang District, Nanning, and compared ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). MGWR provided the best model fit (adjusted R² increased by 0.136 and 0.056, respectively; lowest AICc and residual sum of squares) and revealed significant scale-dependent effects. Distance to metro stations, road network density, and the number of transport facilities exhibited pronounced local-scale heterogeneity, while population density, building density, healthy/unhealthy food outlets, facility POI density, and public transport accessibility predominantly exerted global-scale effects. High-risk clusters of CVD were identified in mixed-use, high-density urban communities lacking rapid transit access. The findings highlight the need for place-specific, multi-scale planning measures, such as transit-oriented development and balanced food environments, to reduce the CVD burden and advance precision healthy-city development. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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19 pages, 11819 KB

Open AccessArticle

Spatiotemporal Dynamics and Multi-Scale Equity Evaluation of Urban Rail Accessibility: Evidence from Hangzhou

by Jiasheng Zhu and Xiaoping Rui

ISPRS Int. J. Geo-Inf. 2025, 14(9), 361; https://doi.org/10.3390/ijgi14090361 - 18 Sep 2025

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Abstract

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings [...] Read more.

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings in current assessment methods: the neglect of actual road network characteristics, reliance on a single static scale, and the absence of quantitative mechanisms to assess accessibility equity. These deficiencies hinder a comprehensive understanding of how equity evolves with the spatiotemporal dynamics of rail systems. To address the aforementioned issues, this study proposes an innovative spatiotemporally dynamic and multi-scale analytical framework for evaluating urban rail accessibility and its equity implications. Specifically, we develop a network-based buffer decay model to refine service population estimation by incorporating realistic walking paths, capturing both distance decay and road network constraints. The framework integrates multiple spatial analytical techniques, including the Gini coefficient, Lorenz curve, global and local spatial autocorrelation, center-of-gravity shift, and standard deviation ellipse, to quantitatively assess the equity and evolutionary patterns of accessibility across multiple spatial scales. Taking the central urban area of Hangzhou as a case study, this research investigates the spatiotemporal patterns and equity changes in metro station accessibility in 2019 and 2023. The results indicate that the expansion of the metro network has partially improved overall accessibility equity: the Gini coefficient at the TAZ (Traffic Analysis Zone) scale decreased from 0.56 to 0.425. Nevertheless, significant inequality remains at finer spatial resolutions (grid-level Gini coefficient = 0.404). In terms of spatial pattern, the core area (e.g., Wulin Square) forms a ‘high-high’ accessibility agglomeration area, while the urban fringe area (e.g., northern Yuhang) presents a ‘low-low’ agglomeration, and the problem of local ‘accessibility depression’ still exists. Additionally, the accessibility centroid has consistently shifted northwestward, and the long axis of the standard deviation ellipse has rotated from an east–west to a northwest-southeast orientation, indicating a growing spatial polarization between core and peripheral zones. The findings suggest that improving equity in urban rail accessibility cannot rely solely on expanding network size; rather, it requires coordinated strategies involving network structure optimization, branch line development, multimodal integration, and the construction of efficient transfer systems to promote more balanced and equitable spatial distribution of rail transit resources citywide. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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22 pages, 2750 KB

Open AccessArticle

Spatiotemporal Evolution and Differential Characteristics of Logistics Resilience in Provinces Along the Belt and Road in China

by Yi Liang, Zhaoxu Yuan, Yan Fang and Han Liu

ISPRS Int. J. Geo-Inf. 2025, 14(9), 360; https://doi.org/10.3390/ijgi14090360 - 18 Sep 2025

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Abstract

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze [...] Read more.

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze regional disparities in resilience levels. Furthermore, when geographical factors are integrated, spatial autocorrelation analysis via Moran’s I index is conducted on the measurement results to explain the spatial heterogeneity among variables. The results reveal several key findings: (1) During the implementation of the BRI, the logistical resilience of regions along the route has improved to varying degrees, indicating enhanced ability of the logistics industry to withstand external risks and recover from disruptions. (2) The level of regional logistical resilience exhibits a spatial pattern similar to that of logistics industry development, characterized by a gradual decline from the southeastern coastal areas toward the northwestern inland regions. (3) Logistical resilience within the study areas has increasingly significant spatial spillover effects; that is, regions with developed logistics industries positively impact surrounding areas, driving improvements in their resilience levels. The results of this study suggest a growing trend of spatial convergence in logistical resilience across these regions. Based on these results, corresponding policy recommendations are proposed to provide insights for enhancing regional logistical resilience. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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20 pages, 4498 KB

Open AccessArticle

Vessel Traffic Density Prediction: A Federated Learning Approach

by Amin Khodamoradi, Paulo Alves Figueiras, André Grilo, Luis Lourenço, Bruno Rêga, Carlos Agostinho, Ruben Costa and Ricardo Jardim-Gonçalves

ISPRS Int. J. Geo-Inf. 2025, 14(9), 359; https://doi.org/10.3390/ijgi14090359 - 18 Sep 2025

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Abstract

Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel [...] Read more.

Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel information. This paper proposes a novel, privacy-preserving framework for vessel traffic density (VTD) prediction that addresses both challenges. The approach combines the European Maritime Observation and Data Network’s (EMODNet) grid-based VTD calculation method with Convolutional Neural Networks (CNN) to model spatiotemporal traffic patterns and employs Federated Learning to collaboratively build a global predictive model without the need for explicit sharing of proprietary AIS data. Three geographically diverse AIS datasets were harmonized, processed, and used to train local CNN models on hourly VTD matrices. These models were then aggregated via a Federated Learning framework under a lifelong learning scenario. Evaluation using Sparse Mean Squared Error shows that the federated global model achieves promising accuracy in sparse data scenarios and maintains performance parity when compared with local CNN-based models, all while preserving data privacy and minimizing hardware performance needs and data communication overheads. The results highlight the approach’s effectiveness and scalability for real-world maritime applications in traffic forecasting, safety, and operational planning. Full article

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31 pages, 6564 KB

Open AccessArticle

Cross-Domain Travel Mode Detection for Electric Micro-Mobility Using Semi-Supervised Learning

by Eldar Lev-Ran, Mirosława Łukawska, Valentino Servizi and Sagi Dalyot

ISPRS Int. J. Geo-Inf. 2025, 14(9), 358; https://doi.org/10.3390/ijgi14090358 - 17 Sep 2025

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Abstract

Electric micro-mobility modes, such as e-scooters and e-bikes, are increasingly used in urban areas, posing challenges for accurate travel mode detection in mobility studies. Traditional supervised learning approaches require large labeled datasets, which are costly and time-consuming to generate. To address this, we [...] Read more.

Electric micro-mobility modes, such as e-scooters and e-bikes, are increasingly used in urban areas, posing challenges for accurate travel mode detection in mobility studies. Traditional supervised learning approaches require large labeled datasets, which are costly and time-consuming to generate. To address this, we propose xSeCA, a semi-supervised convolutional autoencoder that leverages both labeled and unlabeled trajectory data to detect electric micro-mobility travel modes. The model architecture integrates representation learning and classification in a compact and efficient manner, enabling accurate detection even with limited annotated samples. We evaluate xSeCA on multi-city datasets, including Copenhagen, Tel Aviv, Beijing and San Francisco, and benchmark it against supervised baselines such as XGBoost. Results demonstrate that xSeCA achieves high classification accuracy while exhibiting strong generalization capabilities across different urban contexts. In addition to validating model performance, we examine key travel properties relevant to micro-mobility behavior. This research highlights the benefits of semi-supervised learning for scalable and transferable travel mode detection, offering practical implications for urban planning and smart mobility systems. Full article

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25 pages, 6422 KB

Open AccessArticle

Evaluating UAV Flight Parameters for High-Accuracy in Road Accident Scene Documentation: A Planimetric Assessment Under Simulated Roadway Conditions

by Thanakorn Phojaem, Adisorn Dangbut, Panuwat Wisutwattanasak, Thananya Janhuaton, Thanapong Champahom, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

ISPRS Int. J. Geo-Inf. 2025, 14(9), 357; https://doi.org/10.3390/ijgi14090357 - 17 Sep 2025

Viewed by 344

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly valuable for accident scene reconstruction and forensic surveying due to their flexibility and ability to capture high-resolution imagery. This study investigates the impact of flight altitude, camera angle, and image overlap on the spatial accuracy of [...] Read more.

Unmanned Aerial Vehicles (UAVs) have become increasingly valuable for accident scene reconstruction and forensic surveying due to their flexibility and ability to capture high-resolution imagery. This study investigates the impact of flight altitude, camera angle, and image overlap on the spatial accuracy of 3D models generated from UAV imagery. A total of 27 flight configurations were conducted using a DJI Phantom 4 Pro V2, combining three altitudes (30 m, 45 m, 60 m), three camera angles (90°, 75°, 60°), and three overlap levels (60%, 70%, 80%). The resulting 3D models were assessed by comparing measured linear distances between ground control points with known reference distances. The Root Mean Square Error (RMSE) was used to quantify model accuracy. The results indicated that lower flight altitudes, nadir or moderately oblique camera angles, and higher image overlaps consistently yielded the most accurate reconstructions. A Wilcoxon rank-sum test confirmed that the differences in accuracy across parameter settings were statistically significant. These findings highlight the critical role of flight configuration in achieving centimeter-level accuracy, as evidenced by RMSE values ranging from 1.7 to 7.6 cm, and provide practical recommendations for optimizing UAV missions in forensic and engineering applications. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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25 pages, 7019 KB

Open AccessArticle

Assessment of Land Degradation in the State of Maranhão to Support Sustainable Development Goal 15.3.1 in the Agricultural Frontier of MATOPIBA, Brazil

by Antonia Mara Nascimento Gomes, Andreza Maciel de Sousa, Marcus Willame Lopes Carvalho, Washington da Silva Sousa, Marcos Vinícius da Silva, Gustavo André de Araújo Santos, Aldair de Souza Medeiros, Jhon Lennon Bezerra da Silva, José Francisco de Oliveira-Júnior and Nítalo André Farias Machado

ISPRS Int. J. Geo-Inf. 2025, 14(9), 356; https://doi.org/10.3390/ijgi14090356 - 17 Sep 2025

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Abstract

Globally, land degradation represents both an environmental and socioeconomic challenge, necessitating continuous monitoring due to its impacts on ecosystem services. Given the substantial changes in land use and land cover in Maranhão, this study aimed to evaluate land degradation across the state between [...] Read more.

Globally, land degradation represents both an environmental and socioeconomic challenge, necessitating continuous monitoring due to its impacts on ecosystem services. Given the substantial changes in land use and land cover in Maranhão, this study aimed to evaluate land degradation across the state between 2001 and 2023, based on Sustainable Development Goal (SDG) indicator 15.3.1. To this end, we integrated data on land cover (LC), soil organic carbon (SOC), and land productivity (LP) using the Trends.Earth algorithm (v.2.1.16), based on datasets from the MapBiomas platform (collections 9 and Beta) and MODIS (MOD13Q1 product), along with the application of the RESTREND model for climate adjustment. The results indicated that 39.56% of Maranhão’s territory showed signs of degradation, particularly in the central and northwestern (NW) regions, as well as parts of the southern (S) region. Stable areas accounted for 26.39%, while 32.08% were classified as improving, with notable trends in the southern and southeastern (SE) regions, suggesting vegetation recovery and more sustainable land management practices. The integrated analysis of LC, SOC stocks, and land productivity sub-indicators revealed that environmental degradation in Maranhão is strongly driven by the conversion of natural ecosystems into agricultural and livestock areas, especially in the central-eastern and NW regions. In conclusion, the findings highlight a misalignment with the SDG 15.3.1 target but also point to zones of stability and recovery, indicating potential for mitigation, restoration, and the implementation of sustainable land management strategies. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 7235 KB

Open AccessArticle

Analysis of Land-Use Spatial Equilibrium in the Yangtze River Economic Belt Under the Context of High-Quality Development: Quantity Balance and Efficiency Coordination

by Aihui Ma, Wanmin Zhao and Yijia Gao

ISPRS Int. J. Geo-Inf. 2025, 14(9), 355; https://doi.org/10.3390/ijgi14090355 - 17 Sep 2025

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Abstract

As the spatial carrier, the high-quality development of land complements the high-quality development of the economy and society. Imbalanced land use severely restricts regional high-quality development. This study uses panel data from 110 cities at or above the prefecture level in the Yangtze [...] Read more.

As the spatial carrier, the high-quality development of land complements the high-quality development of the economy and society. Imbalanced land use severely restricts regional high-quality development. This study uses panel data from 110 cities at or above the prefecture level in the Yangtze River Economic Belt (YREB) from 2013 to 2022. Based on a conjugate perspective, it comprehensively considers quantitative balance and efficiency coordination to calculate the spatial equilibrium degree of land use. Kernel density estimation and Moran’s I index are employed to reveal the spatiotemporal differentiation characteristics. This study divides land-use spatial equilibrium into different types and proposes differentiated development paths. The findings are as follows: ① In terms of temporal evolution, the spatial equilibrium degree of land use in the YREB exhibits a nonlinear progression, overall trending towards stable convergence. ② In terms of spatial evolution, provincial capital cities and municipalities directly under the central government drive the development of surrounding cities, forming three major urban clusters in the upper, middle, and lower reaches. ③ The spatial clustering characteristics of land-use equilibrium in the YREB are significant, but the degree of agglomeration is continuously weakening. ④ The optimization paths for different types of land-use spatial equilibrium show significant differences, requiring differentiated governance. These findings provide a scientific foundation for optimizing the national spatial pattern of land use, advancing regional balanced development and achieving high-quality development. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—2nd Edition)

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16 pages, 292 KB

Open AccessArticle

Methodology for Determining Potential Locations of Illegal Graffiti in Urban Spaces Using GRA-Type Grey Systems

by Małgorzata Gerus-Gościewska and Dariusz Gościewski

ISPRS Int. J. Geo-Inf. 2025, 14(9), 354; https://doi.org/10.3390/ijgi14090354 - 16 Sep 2025

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Abstract

This paper defines the term “graffiti” and outlines the origins of this concept. The terminological arrangement allowed for the subject of this research, i.e., illegal graffiti, to be situated in reality, i.e., an urban space. It was assumed that the existence of the [...] Read more.

This paper defines the term “graffiti” and outlines the origins of this concept. The terminological arrangement allowed for the subject of this research, i.e., illegal graffiti, to be situated in reality, i.e., an urban space. It was assumed that the existence of the tag was associated with a disturbance of spatial order and had an impact on safety in a space. This, in turn, is related to whether the principles of sustainable development in the social dimension are applied. This paper makes reference to theories of security in a space (the “broken windows” theory and the strategy of Crime Prevention Through Environmental Design, CPTED) and shows the problem of illegal graffiti against the background of these theories. A new research aspect of the occurrence of illegal graffiti (scribbles and tags) within urban space is the features that determine its emergence in a spatial dimension. The aim of the analyses in this paper is to obtain information on which geospatial features are generators of illegal graffiti. The research field was limited to the space of one city—Olsztyn—with the assumption that the proposed research methodology would be useful for the spaces of other cities. The research methodology consists of several steps: firstly, we determined a list of features in the surroundings of illegal graffiti using direct interviews, and secondly, we analyzed the frequency of occurrence of these features in the researched locations in space. The next step was to standardize the obtained results using the quotient transformation method with respect to a reference point, where the reference point is the sum of all observations. After that, we assigned ranks for standardized results. The last stage involved an analysis using the GRA type of grey systems to obtain a sequence of strengths of relationships. This sequence allowed us to determine which of the features adopted for analysis have the greatest impact on the creation of illegal graffiti in a space. As indicated by the strength of the relationship, in the analyses conducted, geospatial features such as poor sidewalk condition and neglected greenery have the greatest impact on the occurrence of illegal graffiti. Other features that influence the occurrence of illegal graffiti in a given space include a lack of visibility from neighboring windows and the proximity of a two-way street. It can be assumed that these features are generators of illegal graffiti in the studied area and space. The poor condition of the facade has the least impact on the possibility of illegal graffiti occurring in a given space. Full article

20 pages, 9917 KB

Open AccessArticle

Exploring Historical Changes to Architectural Heritage Through Reality-Based 3D Modeling and Virtual Reality: A Case Study

by Domenico Simone Roggio, Sina Shokrollahi, Anna Forte and Gabriele Bitelli

ISPRS Int. J. Geo-Inf. 2025, 14(9), 353; https://doi.org/10.3390/ijgi14090353 - 16 Sep 2025

Viewed by 524

Abstract

Urban and territorial development increasingly threatens the preservation of architectural heritage, often leading to degradation or loss. Many historic architectural works, once integral to community identity, now face the impacts of time and neglect. Addressing these challenges requires innovative solutions, and digital technologies [...] Read more.

Urban and territorial development increasingly threatens the preservation of architectural heritage, often leading to degradation or loss. Many historic architectural works, once integral to community identity, now face the impacts of time and neglect. Addressing these challenges requires innovative solutions, and digital technologies offer significant potential for the conservation and enhancement of cultural heritage. This study employs reality-based 3D modeling techniques to generate accurate digital reconstructions of the Church of Santa Croce in Ravenna (Italy), used here as a case study to demonstrate a workflow for phasing analysis and interactive visualization of architectural transformations. The objective is not to produce a philological reconstruction, but to propose a methodological approach for digitally documenting and visualizing, with geometric rigor, the different constructive phases of historical buildings that have undergone structural changes. Using a spatial–temporal navigation approach, the research explores the various historical phases of the site within an interactive 3D virtual environment. The resulting platform facilitates both scholarly investigation and public engagement by providing immersive visualization and enhanced understanding of the monument’s transformation over time. Through this case study, the project underscores the critical role of contemporary digital tools in cultural heritage conservation and explores novel methods for communicating and disseminating historical knowledge. Full article

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19 pages, 5081 KB

Open AccessArticle

Advanced Division of Search Areas for Missing Persons in Non-Urban Environments

by Kateřina Růžičková, Jan Růžička, Kateřina Skřejpková, Helena Chaloupková and Ivona Svobodová

ISPRS Int. J. Geo-Inf. 2025, 14(9), 352; https://doi.org/10.3390/ijgi14090352 - 15 Sep 2025

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Abstract

Dividing large areas into smaller sub-areas is a common practice across many disciplines, with specific requirements determined by their intended use. This study focuses on preparing search sectors for locating missing persons in non-urban environments. In such settings, search teams must be assigned [...] Read more.

Dividing large areas into smaller sub-areas is a common practice across many disciplines, with specific requirements determined by their intended use. This study focuses on preparing search sectors for locating missing persons in non-urban environments. In such settings, search teams must be assigned sufficiently large yet homogeneous sectors that allow visual orientation even without GNSS. While general search strategies differ in their approach to area coverage, rural and wilderness environments pose unique challenges that demand a systematic method to ensure both navigability and efficiency. To address this, we propose a land-use-based approach that incorporates the artificial extension of linear geo-features to subdivide large polygons. The methodology was first applied to regions of the Czech Republic in 2020 and refined with advanced settings in 2023. Introducing the step for subdividing extensive homogeneous polygons significantly improved outcomes, allowing the method to generate search sectors of the desired size for 86% of the territory in 2020 and 91% in 2023. The main limitation lies in the reliance on cartographic data, which may omit fine details critical for field navigation. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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26 pages, 6234 KB

Open AccessArticle

Automated Identification and Spatial Pattern Analysis of Urban Slow-Moving Traffic Bottlenecks Using Street View Imagery and Deep Learning

by Zixuan Guo, Hong Xu and Qiushuang Lin

ISPRS Int. J. Geo-Inf. 2025, 14(9), 351; https://doi.org/10.3390/ijgi14090351 - 15 Sep 2025

Viewed by 320

Abstract

With rapid urbanization and increasing emphasis on sustainable mobility, slow-moving traffic systems, including pedestrian and cycling infrastructure, have become critical to urban transportation and quality of life. Conventional assessment methods are labor-intensive, time-consuming, and limited in coverage. Leveraging advances in deep learning and [...] Read more.

With rapid urbanization and increasing emphasis on sustainable mobility, slow-moving traffic systems, including pedestrian and cycling infrastructure, have become critical to urban transportation and quality of life. Conventional assessment methods are labor-intensive, time-consuming, and limited in coverage. Leveraging advances in deep learning and computer vision, this study develops a framework for bottleneck detection using street-level imagery and the You Only Look Once version 5 (YOLOv5) model. An evaluation system comprising 15 indicators across continuity, safety, and comfort is established. In a case study of Wuhan’s Third Ring Road, the YOLOv5 model achieved 98.9% mean Average Precision (mAP)@0.5, while spatial hotspot analysis (p < 0.05) identified severe demand–infrastructure mismatches in southeastern Wuhan, contrasted with fewer problems in the northern region due to stronger management. To ensure adaptability, a dynamic optimization mechanism integrating temporal imagery updates, transfer learning, and collaborative training is proposed. The findings demonstrate the effectiveness of street-level remote sensing for large-scale urban diagnostics, extend the application of deep learning in mobility research, and provide practical insights for data-driven planning and governance of slow-moving traffic systems in high-density cities. Full article

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38 pages, 15532 KB

Open AccessArticle

Lightweight Deep Learning Approaches for Lithological Mapping in Vegetated Terrains of the Vălioara Valley, Romania

by Valentin Árvai and Gáspár Albert

ISPRS Int. J. Geo-Inf. 2025, 14(9), 350; https://doi.org/10.3390/ijgi14090350 - 15 Sep 2025

Viewed by 422

Abstract

Mapping lithology in areas with dense vegetation remains a major challenge for remote sensing, as plant cover tends to obscure the spectral signatures of underlying rock formations. This study tackles that issue by comparing the performance of three custom-built lightweight deep learning models [...] Read more.

Mapping lithology in areas with dense vegetation remains a major challenge for remote sensing, as plant cover tends to obscure the spectral signatures of underlying rock formations. This study tackles that issue by comparing the performance of three custom-built lightweight deep learning models in the mixed-vegetation terrain of the surroundings of the Vălioara Valley, Romania. We used time-series data from Sentinel-2 and elevation data from the SRTM, with preprocessing techniques such as the Principal Component Analysis (PCA) and the Forced Invariance Method (FIM) to reduce the spectral interference caused by vegetation. Predictions were made with a Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and a Vision Transformer (ViT). In addition to measuring the classification accuracy, we assessed how the different models handled vegetation coverage. We also explored how vegetation density (NDVI) correlated with the classification results. Tests show that the Vision Transformer outperforms the other models by 6%, offering a stronger resilience to vegetation interference, while FIM doubled the model confidence in specific (locally rare) lithologies and decorrelated vegetation in multiple measures. These findings highlight both the potential of ViTs for remote sensing in complex environments and the importance of applying vegetation suppression techniques like FIM to improve geological interpretation from satellite data. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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25 pages, 4316 KB

Open AccessArticle

Distribution, Dynamics and Drivers of Asian Active Fire Occurrences

by Xu Gao, Wenzhong Shi and Min Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 349; https://doi.org/10.3390/ijgi14090349 - 12 Sep 2025

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Abstract

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics [...] Read more.

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics and drivers remain poorly understood. Here, utilizing active fire and land cover products alongside climate and human footprint datasets, we explored the spatiotemporal distribution and dynamics of active fire counts (FC) over 20 years (2003–2022) in Asia, quantifying the effects of climate and human management. Results analyzed over 10 million active fires, with cropland fires predominating (25.6%) and Southeast Asia identified as the hotspot. FC seasonal dynamics were governed by temperature and precipitation, while spring was the primary burning season. A continental inter-annual FC decline (mean slope: −8716 yr⁻¹) was identified, primarily attributed to forest fire reduction. Subsequently, we further clarified the drivers of FC dynamics. Time series decomposition attributed short-term FC fluctuations to extreme climate events (e.g., 2015 El Niño), while long-term trends reflected cumulative human interventions (e.g., cropland management). The trend analysis revealed that woody vegetation fires in the Indochina Peninsula shifted to herbaceous fires, Asian cropland FC primarily increased but were restricted in eastern China and Thailand by strict policies. Spatially, hydrometeorological factors dominated 58.1% of FC variations but exhibited opposite effects between arid and humid regions, followed by human factor, where human activities shifted from fire promotion to suppression through land-use transitions. These driving mechanism insights establish a new framework for adaptive fire management amid escalating environmental change. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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21 pages, 5662 KB

Open AccessArticle

Study on Spatial Equity of Greening in Historical and Cultural Cities Based on Multi-Source Spatial Data

by Huiqi Sun, Xuemin Shi, Bichao Hou and Huijun Yang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 348; https://doi.org/10.3390/ijgi14090348 - 12 Sep 2025

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Abstract

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the [...] Read more.

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the unique conflict between the preservation of well-known historical and cultural cities and the development of greening. It does this by integrating streetscape big data (2925 sampling points) and point of interest (POI) density data (57,266 records) and using the DeepLab-ResNeSt269 semantic segmentation model in conjunction with spatial statistical techniques (Moran’s Index, Locational Entropy and Theil Index Decomposition) to quantitatively analyze the spatial equity of the green view index (GVI) in Kaifeng City. The results of the study show that (1) The Theil Index reveals that the primary contradiction in Kaifeng City’s distribution pattern—low GVI in the center and high in the periphery—is the micro-street scale difference, suggesting that the spatial imbalance of the GVI is primarily reflected at the micro level rather than the macro urban area difference. (2) The distribution of the GVI in Kaifeng City exhibits a significant spatial polarization phenomenon, with the proportion of low-value area (35.40%) being significantly higher than that of high-value area (25.10%) and the spatial clustering being evident (Moran’s Index 0.3824). Additionally, the ancient city area and the new city area exhibit distinct spatial organization patterns. (3) POI density and GVI had a substantial negative correlation (r = −0.085), suggesting a complicated process of interaction between green space and urban functions. The study reveals that the fairness of green visibility in historical and cultural cities presents the characteristics of differentiated distribution in different spatial scales, which provides a scientific basis for the optimization of greening spatial layouts in historical and cultural cities while preserving the traditional landscape. Full article

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28 pages, 3816 KB

Open AccessArticle

Multi-Size Facility Allocation Under Competition: A Model with Competitive Decay and Reinforcement Learning-Enhanced Genetic Algorithm

by Zixuan Zhao, Shaohua Wang, Cheng Su and Haojian Liang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 347; https://doi.org/10.3390/ijgi14090347 - 9 Sep 2025

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Abstract

In modern urban planning, the problem of bank location requires not only considering geographical factors but also integrating competitive elements to optimize resource allocation and enhance market competitiveness. This study addresses the multi-size bank location problem by incorporating competitive factors into the optimization [...] Read more.

In modern urban planning, the problem of bank location requires not only considering geographical factors but also integrating competitive elements to optimize resource allocation and enhance market competitiveness. This study addresses the multi-size bank location problem by incorporating competitive factors into the optimization process through a novel reinforcement learning-enhanced genetic algorithm (RL-GA) framework. Building upon an attraction-based model with competitive decay functions, we propose an innovative hybrid optimization approach that combines evolutionary computation with intelligent decision-making capabilities. The RL-GA framework employs Q-learning principles to adaptively select optimal genetic operators based on real-time population states and search progress, enabling meta-learning where the algorithm learns how to optimize rather than simply optimizing. Unlike traditional genetic algorithms with fixed operator probabilities, our approach dynamically adjusts its search strategy through an

ε

-greedy exploration mechanism and multi-objective reward functions. Experimental results demonstrate that the RL-GA achieves improvements in early-stage convergence speed while maintaining solution quality comparable to traditional methods. The algorithm exhibits enhanced convergence characteristics in the initial optimization phases and demonstrates consistent performance across multiple optimization trials. These findings provide evidence for the potential of intelligence-guided evolutionary computation in facility location optimization, offering moderate computational efficiency gains and adaptive strategic guidance for banking facility deployment in competitive environments. Full article

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35 pages, 30285 KB

Open AccessArticle

Geological Disaster Risk Assessment Under Extreme Precipitation Conditions in the Ili River Basin

by Xinxu Li, Jinghui Liu, Zhiyong Zhang, Xushan Yuan, Yanmin Li and Zixuan Wang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 346; https://doi.org/10.3390/ijgi14090346 - 7 Sep 2025

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Abstract

Geological Disasters (Geo-disasters) are common in the Ili River Basin, with extreme precipitation being a major triggering factor. As the frequency and intensity of these events increase, the associated risks also rise. This study proposes a hazard assessment framework that integrates extreme precipitation [...] Read more.

Geological Disasters (Geo-disasters) are common in the Ili River Basin, with extreme precipitation being a major triggering factor. As the frequency and intensity of these events increase, the associated risks also rise. This study proposes a hazard assessment framework that integrates extreme precipitation recurrence periods with Geo-disaster susceptibility. Furthermore, based on a comprehensive risk assessment model encompassing hazard, exposure, vulnerability, and disaster mitigation capacity, the study evaluates Geo-disaster risk in the Ili River Basin under extreme precipitation conditions. Hazard levels are assessed by integrating geo-disaster susceptibility with recurrence periods of extreme precipitation, resulting in hazard and risk maps under various conditions. The susceptibility indicator system is refined using K-means clustering, the certainty factor (CF) model, and Pearson correlation to reduce redundancy. Key findings include: (a) Geo-disasters are influenced by a combination of factors. High-susceptibility areas are typically found in moderately sloped terrain (8.5–17.64°) at elevations between 1412 m and 2234 m, especially on east- and southeast-facing slopes. Lithology, soil, hydrology, fault proximity, and the topographic wetness index (TWI) are the primary influences, while high NDVI values reduce susceptibility. (b) The hazard pattern varies with the recurrence period of extreme precipitation. Shorter periods lead to broader high-hazard zones, while longer periods concentrate hazards, particularly in Yining City. (c) Exposure is higher in the east, vulnerability aligns with transportation networks, and disaster mitigation capacity is stronger in the north, particularly in Yining. (d) Low-risk areas are found in valleys and flat terrains, while medium to high-risk zones concentrate in southeastern Zhaosu, Tekes, and Gongliu counties. Some economically active regions require special attention due to their high exposure and vulnerability. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 6560 KB

Open AccessArticle

MART: Ship Trajectory Prediction Model Based on Multi-Dimensional Attribute Association of Trajectory Points

by Senyang Zhao, Wei Guo and Yi Liu

ISPRS Int. J. Geo-Inf. 2025, 14(9), 345; https://doi.org/10.3390/ijgi14090345 - 7 Sep 2025

Viewed by 396

Abstract

Ship trajectory prediction plays an important role in numerous maritime applications and services. With the development of deep learning technology, the deep learning prediction method based on Automatic Identification System (AIS) data has become one of the hot topics in current maritime traffic [...] Read more.

Ship trajectory prediction plays an important role in numerous maritime applications and services. With the development of deep learning technology, the deep learning prediction method based on Automatic Identification System (AIS) data has become one of the hot topics in current maritime traffic research. However, as current models always concatenate dynamic information with distinct meanings (such as position, ship speed, and heading) into a single integrated input when processing trajectory point information as input, it becomes difficult for the models to grasp the correlations between different types of dynamic information of trajectory points and the specific information contained in each type of dynamic information itself. Aiming at the problem of insufficient modeling of the relationships among dynamic information in ship trajectory prediction, we propose the Multi-dimensional Attribute Relationship Transformer (MART) model. This model introduces a simulated trajectory training strategy to obtain the Association Loss (AssLoss) for learning the associations among different types of dynamic information; and it uses the Distance Loss (DisLoss) to integrate the relative distance information of the attribute embedding encoding to assist the model in understanding the relationships among different values in the dynamic information. We test the model on two AIS datasets, and the experiments show this model outperforms existing models. In the 15 h long-term prediction task, compared with other models, the MART model improves the prediction accuracy by 9.5% on the Danish Waters Dataset and by 15.4% on the Northern European Dataset. This study reveals the importance of the relationship between attributes and the relative distance of attribute values in spatiotemporal sequence modeling. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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17 pages, 6488 KB

Open AccessArticle

A Spatial Analysis of the Association Between Urban Heat and Coronary Heart Disease

by Kyle Lucas, Ben Dewitt, Donald J. Biddle and Charlie H. Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 344; https://doi.org/10.3390/ijgi14090344 - 7 Sep 2025

Viewed by 543

Abstract

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution [...] Read more.

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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34 pages, 16240 KB

Open AccessArticle

The Toponym Co-Occurrence Index: A New Method to Measure the Co-Occurrence Characteristics of Toponyms

by Gaimei Wang, Fei He and Li Wang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 343; https://doi.org/10.3390/ijgi14090343 - 5 Sep 2025

Viewed by 359

Abstract

Toponym groups are fundamental units of quantitative spatial analysis of toponyms. Using suitable technical methods to investigate the spatial distribution and co-occurrence characteristics of these groups has significant implications for identifying cultural regions within geographical spaces and elucidating spatial differentiation and integration of [...] Read more.

Toponym groups are fundamental units of quantitative spatial analysis of toponyms. Using suitable technical methods to investigate the spatial distribution and co-occurrence characteristics of these groups has significant implications for identifying cultural regions within geographical spaces and elucidating spatial differentiation and integration of regional cultural characteristics underlying toponyms. Existing research has mainly relied on traditional spatial distribution models such as standard deviation ellipse (SDE) and kernel density estimation (KDE) to analyse the characters used in toponyms. In addition, few quantitative studies exist on the co-occurrence of multiple types of toponym groups from the perspective of words used in toponyms. This study introduced methods, including the local co-location quotient, to propose a general framework for toponymic co-occurrence research and a new toponymic co-occurrence index (TCOI). Data from 64,981 village toponyms in Liaoning Province, China, were used to analyse spatial co-occurrence characteristics of five high-frequency two-character village toponym groups. In addition, two high-frequency single-character toponym groups and three low-frequency two-character toponym groups were used for verification, with a simultaneous comparison of the SDE and KDE methods. The findings indicated that: (1) the proposed general framework and TCOI effectively support toponymic spatial measurement and have good applicability and expansibility; (2) the TCOI enables a more accurate scientific assessment of co-occurrence characteristics of toponymic groups at different scales, thereby enhancing the technical level of toponymic spatial measurement; (3) the TCOI for Liaoning Province was 28.63%, indicating that toponym groups exhibited a partially integrated yet relatively exclusive spatial distribution pattern. The spatial differentiation patterns of rural toponym cultural landscapes in Liaoning Province provide a scientific basis for promoting cultural geography research and strengthening toponym protection. Full article

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27 pages, 5718 KB

Open AccessArticle

A Geospatial Framework for Retail Suitability Modelling and Opportunity Identification in Germany

by Cristiana Tudor

ISPRS Int. J. Geo-Inf. 2025, 14(9), 342; https://doi.org/10.3390/ijgi14090342 - 5 Sep 2025

Viewed by 662

Abstract

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and [...] Read more.

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

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18 pages, 1886 KB

Open AccessArticle

The Integrated Choice and Latent Variable Model for Exploring the Mechanisms of Pedestrian Route Choice

by Cheng-Jie Jin, Ningxuan Li, Chenyang Wu, Dawei Li and Yifan Lin

ISPRS Int. J. Geo-Inf. 2025, 14(9), 341; https://doi.org/10.3390/ijgi14090341 - 5 Sep 2025

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Abstract

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian [...] Read more.

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian route choice experiments. Four groups of experimental runs were designed, each involving two route options. The first three groups introduced specific controls: bottlenecks, distance constraints, and extra rewards, while the fourth group, without any imposed control, focused on the influence of route geometry (lengths and widths). For each group, we developed measurement and structural models, followed by three comparative models: a binary logit model using only measured variables (MV model), a model using only latent variables (LV model), and the ICLV model that integrates both. Across all the four scenarios, the adjusted R² values have been improved from 0.286/0.135/0.108/0.035 (MV model) to 0.329/0.161/0.111/0.056 (ICLV model), and the ICLV model can provide interpretable results. These findings highlight the value of incorporating latent constructs based on Structural Equation Modelling (SEM), which enhances the explanatory power of pedestrian route choice models. Moreover, the differences in significant latent variables across various experimental settings offers further insights into the distinct mechanisms underlying pedestrian decision-making under varying conditions. Full article

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27 pages, 5349 KB

Open AccessArticle

Proportional Symbol Maps: Value-Scale Types, Online Value-Scale Generator and User Perspectives

by Radek Barvir, Martin Holub and Alena Vondrakova

ISPRS Int. J. Geo-Inf. 2025, 14(9), 340; https://doi.org/10.3390/ijgi14090340 - 1 Sep 2025

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Abstract

Proportional symbol maps are a frequently used method of thematic cartography. Using an intuitive principle—the larger, the more—provides a simple and precise way of visualizing quantity in maps using geographic information systems (GIS). However, none of the current GIS software provides a proper [...] Read more.

Proportional symbol maps are a frequently used method of thematic cartography. Using an intuitive principle—the larger, the more—provides a simple and precise way of visualizing quantity in maps using geographic information systems (GIS). However, none of the current GIS software provides a proper map legend that could be used to interpret exact phenomenon quantity values from the map in reverse. Cartographers have been designing value scales manually for such a possibility of interpretation. Eventually, they preferred to resign to the accuracy of the interpretation and use the legend offered by the software. The paper describes the development of an easy-to-use online value scale generator for static maps, aiming to eliminate the time-consuming process to make map design more efficient while preserving the precision of cartographic visualization and its subsequent interpretation. The tool consists of a free web platform performing all necessary calculations and rendering an appropriate value scale based on user-defined input parameters. This functionality is performed for most typically used symbol shapes as well as for custom-design shapes provided by the user in SVG vector graphics. The output is then returned in a vector SVG and PDF file format to be used directly in a map legend or possibly edited in graphic software before such a step. The presented tool is therefore independent of which software was used for map design. Within the research, two user experiments were performed to compare generated value scales with simple legends generated in GIS and to gather insights from cartography experts. Full article

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20 pages, 11319 KB

Open AccessArticle

Using Certainty Factor as a Spatial Sample Filter for Landslide Susceptibility Mapping: The Case of the Upper Jinsha River Region, Southeastern Tibetan Plateau

by Xin Zhou, Ke Jin, Xiaohui Sun, Yunkai Ruan, Yiding Bao, Xiulei Li and Li Tang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 339; https://doi.org/10.3390/ijgi14090339 - 1 Sep 2025

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Abstract

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening [...] Read more.

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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24 pages, 23275 KB

Open AccessArticle

Developing a Replicable ESG-Based Framework for Assessing Community Perception Using Street View Imagery and POI Data

by Jingxue Xie, Zhewei Liu and Jue Wang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 338; https://doi.org/10.3390/ijgi14090338 - 31 Aug 2025

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Abstract

Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this [...] Read more.

Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this gap, this study develops an Environmental, Social, and Governance (ESG)-informed framework for evaluating perceived environmental quality in urban communities. Using Baidu Street View imagery—selected due to its comprehensive coverage of Chinese urban areas—and Point of Interest (POI) data, we analyze seven communities in Shenyang, China, selected for their diversity in built form and demographic context. Kernel Density Analysis and Exploratory Factor Analysis (EFA) are applied to derive latent ESG-related spatial dimensions. These are then correlated with Place Pulse 2.0 perception scores using Spearman analysis to assess subjective livability. Results show that environmental and social factors—particularly greenery visibility—are strongly associated with favorable perceptions, while governance-related indicators display weaker or context-specific relationships. The findings highlight the differentiated influence of ESG components, with environmental openness and walkability emerging as key predictors of perceived livability. By integrating pixel-level spatial features with perception metrics, the proposed framework offers a scalable and transferable tool for human-centered neighborhood evaluation, with implications for planning strategies that align with how residents experience urban environments. Full article

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

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22 pages, 5263 KB

Open AccessArticle

Educational Facility Site Selection Based on Multi-Source Data and Ensemble Learning: A Case Study of Primary Schools in Tianjin

by Zhenhui Sun, Ying Xu, Junjie Ning, Yufan Wang and Yunxiao Sun

ISPRS Int. J. Geo-Inf. 2025, 14(9), 337; https://doi.org/10.3390/ijgi14090337 - 30 Aug 2025

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Abstract

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability [...] Read more.

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability prediction model was constructed based on the existing distribution of primary schools, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. Comprehensive evaluation, feature importance analysis, and SHAP (SHapley Additive exPlanations) interpretation were conducted to ensure model reliability and interpretability. Spatial overlay analysis, incorporating population structure and the education supply–demand ratio, identified highly suitable areas for primary school construction. The results demonstrate: (1) RF and XGBoost achieved evaluation metrics exceeding 85%, outperforming traditional single models such as Logistic Regression, SVM, KNN, and CART. Validation against actual primary school distributions yielded accuracies of 84.70% and 92.41% for RF and XGBoost, respectively. (2) SHAP analysis identified population density, proximity to other educational institutions, and accessibility to transportation facilities as the most critical factors influencing site suitability. (3) Suitable areas for primary school construction are concentrated in central Tianjin and surrounding areas, including Baoping Street (Baodi District), Huaming Street (Dongli District), and Zhongbei Town (Xiqing District), among others, to meet high-quality educational service demands. Full article

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

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ISPRS Int. J. Geo-Inf., Volume 14, Issue 9 (September 2025) – 47 articles

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