ISPRS International Journal of Geo-Information

19 pages, 2850 KiB

Open AccessArticle

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

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

Open AccessArticle

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

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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27 pages, 13957 KiB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

Conceptual Neighborhood Graphs of Topological Relations in

Z^{2}

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

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

Z^{2}

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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25 pages, 8220 KiB

Open AccessArticle

NPP-VIIRS Nighttime Lights Illustrate the Post-Earthquake Damage and Subsequent Economic Recovery in Hatay Province, Turkey

by Feng Li, Shunbao Liao, Xingjian Fu and Tianxuan Liu

ISPRS Int. J. Geo-Inf. 2025, 14(4), 149; https://doi.org/10.3390/ijgi14040149 - 30 Mar 2025

Abstract

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on 6 February 2023 caused massive casualties and extensive damage to infrastructure, with Hatay Province of Turkey bearing the brunt of the impact. To swiftly and thoroughly assess the damage caused by [...] Read more.

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on 6 February 2023 caused massive casualties and extensive damage to infrastructure, with Hatay Province of Turkey bearing the brunt of the impact. To swiftly and thoroughly assess the damage caused by the earthquakes and the subsequent reconstruction efforts, this study initially investigated the application of light change ratios between the pre-earthquake monthly nighttime lights (NTLs) and the post-earthquake daily NTL data to identify earthquake damage in Hatay Province. Next, the monthly NTL data were employed to calculate the time series average lighting index (ALI). Subsequently, random noise and seasonal fluctuation were eliminated through data smoothing and seasonal decomposition techniques. Pre- and post-earthquake regression models were then utilised to establish a comprehensive framework for assessing economic recovery following the earthquake. The findings indicated that (1) the seismic damage identification method based on NTL data achieved an overall accuracy exceeding 71.55% in detecting building damage after a disaster. This method provided a swift and effective solution for rapidly assessing disaster-related destruction. (2) The reduced NTLs exhibited a strong correlation with the area of severely and moderately damaged buildings while showing a weaker correlation with areas of slightly damaged buildings. (3) The developed pre- and post-earthquake regression models demonstrated a high degree of fit, making them valuable tools for assessing regional economic recovery after the earthquake. At the county scale, such districts as Erzin and Kumlu exhibited promising signs of recovery, while such severely impacted areas as Antakya faced misconceptions of progress, primarily due to the brightening of NTLs caused by reconstruction efforts. Additionally, such districts as Dortyol and Samandag grappled with substantial short-term recovery challenges. Although the province experienced gradual economic recovery, achieving complete restoration has remained complex and time-intensive. The study offers valuable insights into earthquake damage assessment and economic recovery monitoring while serving as a scientific reference for disaster mitigation and post-disaster reconstruction efforts. 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, 11550 KiB

Open AccessArticle

Nonlinear Impact of Built Environment on Older Adults’ Bus Use Behavior: A Hybrid Model Considering Spatial Heterogeneity

by Jiandong Peng, Jingjing Li, Hong Yang and Lele Sun

ISPRS Int. J. Geo-Inf. 2025, 14(4), 148; https://doi.org/10.3390/ijgi14040148 - 28 Mar 2025

Abstract

Population aging is a pressing global issue. As it progresses, older adults’ demand for public transport will increase. Ensuring their equitable access is vital for social equity. Meanwhile, physiological changes and travel preferences in older adults create unique bus usage patterns, making them [...] Read more.

Population aging is a pressing global issue. As it progresses, older adults’ demand for public transport will increase. Ensuring their equitable access is vital for social equity. Meanwhile, physiological changes and travel preferences in older adults create unique bus usage patterns, making them more susceptible to the built environment. To test this, we compared bus travel behavior between older adults and young people in Wuhan, China. Our results showed that older adults travel more often, with a longer morning peak and less pronounced evening peak. We developed the GWRBoost model, combining Geographic Weighted Regression (GWR) and eXtreme Gradient Boosting (XGBoost), to explore the spatial heterogeneity and nonlinear impact of the built environment on bus travel for both groups. The study found significant differences in how the built environment affects bus ridership between older adults and young people. For older adults, proximity to the nearest bus stop is most critical, regardless of weekday or weekend. These variables also show spatial variations and nonlinear relations with bus ridership for both groups. These findings improve our understanding of older adults’ travel and offer insights for optimizing their travel environment and promoting transportation equity. Full article

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18 pages, 3423 KiB

Open AccessArticle

Voxel-Based Path Planning for Autonomous Vehicles in Parking Lots

by Zhaoyu Lin, Zhiyong Wang, Tailin Gong, Yingying Ma and Weidong Xie

ISPRS Int. J. Geo-Inf. 2025, 14(4), 147; https://doi.org/10.3390/ijgi14040147 - 28 Mar 2025

Abstract

With the development of autonomous driving technology, the application scenarios for mobile robots and unmanned vehicles are gradually expanding from simple structured environments to complex unstructured scenes. In unstructured three-dimensional spaces such as urban environments, traditional two-dimensional map construction and path planning techniques [...] Read more.

With the development of autonomous driving technology, the application scenarios for mobile robots and unmanned vehicles are gradually expanding from simple structured environments to complex unstructured scenes. In unstructured three-dimensional spaces such as urban environments, traditional two-dimensional map construction and path planning techniques struggle to effectively plan accurate paths. To address this, this paper proposes a method of constructing a map and planning a route based on three-dimensional spatial representation. This method utilizes point cloud semantic segmentation to extract navigable space information from environmental point cloud data and employs voxelization techniques to generate a voxel map. Building on this, the paper combines a variable search neighborhood A* algorithm with a road-edge-detection-based path adjustment to generate optimal paths between two points on the map, ensuring that the paths are both short and capable of effectively avoiding obstacles. Our experimental results in multi-story parking garages show that the proposed method effectively avoids narrow areas that are difficult for vehicles to pass through, increasing the average edge distance of the path by 83.3% and significantly enhancing path safety and feasibility. Full article

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40 pages, 3470 KiB

Open AccessArticle

Changes in Tourists’ Perceptions of Community-Based Ecotourism (CBET) After COVID-19 Pandemic: A Study on the Country of Origin and Economic Development Level

by Flavia Dana Oltean, Petru Alexandru Curta, Benedek Nagy, Arzu Huseyn and Manuela Rozalia Gabor

ISPRS Int. J. Geo-Inf. 2025, 14(4), 146; https://doi.org/10.3390/ijgi14040146 - 27 Mar 2025

Abstract

(1) Background: This study investigates the impact of the COVID-19 pandemic on tourists’ perceptions of community-based ecotourism (CBET) in Romania and Spain, taking into account country of origin and economic development. In order to provide insights for sustainable tourism development and policymaking, this [...] Read more.

(1) Background: This study investigates the impact of the COVID-19 pandemic on tourists’ perceptions of community-based ecotourism (CBET) in Romania and Spain, taking into account country of origin and economic development. In order to provide insights for sustainable tourism development and policymaking, this study aims to investigate how the COVID-19 pandemic has influenced tourists’ perceptions of community-based ecotourism (CBET) in Romania and Spain, taking into account differences in country of origin and economic development. (2) Methods: An online questionnaire was administered to 703 ecotourists (353 Romanian, 350 Spanish). (3) Results: The results show statistically significant differences between the two countries regarding the perception of ecotourism principles, information sources and preferred activities. For example, Romanians showed stronger agreement with ecotourism’s positive contribution to local communities and minimal environmental impact than Spaniards (p < 0.01 for EP3, EP4 and EP6). Significant correlations were found between specific ecotourism elements and preferred activities within each country, highlighting different preferences. Multilinear regression analysis showed that gender and region of origin significantly predicted perceptions of the role of ecotourism in biodiversity conservation for Spain. (4) Conclusions: Policy recommendations include targeted awareness campaigns, increased community involvement and cross-cultural collaboration to promote sustainable CBET development. This comparative study fills a gap in CBET research by contrasting perceptions in Eastern and Western European countries with different levels of economic 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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22 pages, 66134 KiB

Open AccessArticle

Analysis of Regional Spatial Characteristics and Optimization of Tourism Routes Based on Point Cloud Data from Unmanned Aerial Vehicles

by Yu Chen, Hui Zhong and Jianglong Yu

ISPRS Int. J. Geo-Inf. 2025, 14(4), 145; https://doi.org/10.3390/ijgi14040145 - 27 Mar 2025

Abstract

In this study, we analyzed regional spatial features and optimized tourism routes based on point cloud data provided by unmanned aerial vehicles (UAVs) with the goal of developing the Xiaosongyuan Red Tourism Scenic Area in Kunming, Yunnan Province, China. We first proposed a [...] Read more.

In this study, we analyzed regional spatial features and optimized tourism routes based on point cloud data provided by unmanned aerial vehicles (UAVs) with the goal of developing the Xiaosongyuan Red Tourism Scenic Area in Kunming, Yunnan Province, China. We first proposed a novel method for UAV point cloud data coverage based on an irregular regional segmentation technique along with an optimized search path designed to minimize travel time within the specified area. Three DJI Phantom drones were employed to collect data over the designated region, and an improved progressive triangular irregular network densification filtering algorithm was used to extract ground points from the UAV-acquired point cloud data. DJI Terra software was used for image stitching to generate a comprehensive map of spatial features in the target area. Using this three-dimensional map of spatial features, we explored tourist routes in complex environments and applied an improved particle swarm optimization algorithm to identify optimal tourist routes characterized by safety, smoothness, and feasibility. The findings provide valuable technical support for enhancing tourism planning and management in scenic areas while maintaining a balance with conservation efforts. Full article

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19 pages, 13263 KiB

Open AccessArticle

Evaluating Shallow Landslide Prediction Mapping by Using Two Different GIS-Based Models: 4SLIDE and SHALSTAB

by Federico Valerio Moresi, Mauro Maesano, Marco di Cristofaro, Giuseppe Scarascia Mugnozza and Elena Brunori

ISPRS Int. J. Geo-Inf. 2025, 14(4), 144; https://doi.org/10.3390/ijgi14040144 - 27 Mar 2025

Abstract

Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land [...] Read more.

Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land surfaces, infrastructure, and inhabited areas. Forest cover and management play a fundamental role in stabilizing soil and reducing landslide susceptibility. This study focuses on landslide forecasting models, which integrate geological, climatic, and topographic data to predict landslide probability and severity. Specifically, we compare the predictive accuracy of the 4SLIDE model with the established SHALSTAB model in a forested mountain catchment within Sila National Park, Southern Italy, using GIS-based analysis. The results demonstrate that both models effectively identify high-risk areas, with ROC analysis confirming the superior predictive capability of the 4SLIDE model. Our findings underscore the critical importance of early landslide identification, supporting timely interventions and the implementation of forest engineering and Civil Protection measures to mitigate the impact of landslides on communities and infrastructure. Full article

(This article belongs to the Topic Geotechnics for Hazard Mitigation)

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18 pages, 36783 KiB

Open AccessArticle

A Selection Method of Massive Point Cluster Using the Delaunay Triangulation to Support Real-Time Visualization

by Chongya Gong, Tinghua Ai, Tianyuan Xiao, Huafei Yu and Pengcheng Liu

ISPRS Int. J. Geo-Inf. 2025, 14(4), 143; https://doi.org/10.3390/ijgi14040143 - 26 Mar 2025

Abstract

One of the goals of map generalization is to achieve real-time visualization of massive entities while adapting to zoom-in/zoom-out conditions. Unlike traditional map generalization, this type of scaling operation does not simplify the data to produce a final result; it only outputs temporary [...] Read more.

One of the goals of map generalization is to achieve real-time visualization of massive entities while adapting to zoom-in/zoom-out conditions. Unlike traditional map generalization, this type of scaling operation does not simplify the data to produce a final result; it only outputs temporary visualization data. To meet the current visualization scale requirements, we insert a simplification algorithm prior to visualization to process the data. Taking point simplification as an example, this study proposes a novel massive point selection method and optimizes the entire algorithmic process, enabling the method to quickly and efficiently handle point selection for datasets ranging from tens of thousands to millions of points. The method employs a geometric construction, namely a Delaunay triangulation, is applied to discover the distribution characteristics with real-time efficiency. Initially, we construct the Delaunay triangulation of the point cluster. Subsequently, we calculate the mean distance of each point as the selection feature. Finally, we incorporate a ‘fixed point’ concept to rank and stabilize the points during the selection process. Experimental results indicate that our method not only achieves commendable performance in considering spatial structure, comparable to both traditional and state-of-the-art methods but also demonstrates significantly higher efficiency. This method can efficiently handle point selection for datasets ranging from tens of thousands to millions of points in a short time, thereby greatly enhancing the practicality of the algorithm in complex point selection scenarios. Full article

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21 pages, 7660 KiB

Open AccessArticle

A New Typification Method for Combined Linear Building Patterns with the Resolution of Spatial Conflicts

by Ying Tong, Qingsheng Guo, Wei Zhu and Chuanbang Zheng

ISPRS Int. J. Geo-Inf. 2025, 14(4), 142; https://doi.org/10.3390/ijgi14040142 - 26 Mar 2025

Abstract

Typification is a crucial generalization operator for maintaining the structure of building patterns in map generalization. However, in densely built-up areas, the structure of building patterns is complicated, and spatial conflicts exist not only within building patterns but also between them. Applying typification [...] Read more.

Typification is a crucial generalization operator for maintaining the structure of building patterns in map generalization. However, in densely built-up areas, the structure of building patterns is complicated, and spatial conflicts exist not only within building patterns but also between them. Applying typification solely to individual patterns fails to resolve conflicts between them and does not adequately preserve their spatial distribution. To address these challenges, this study proposes a new typification method that treats linear building patterns with common buildings as a whole named combined linear patterns, simultaneously resolving the spatial conflict both within and between combined linear patterns. Firstly, linear building patterns and combined linear patterns are recognized, respectively, using visual perception principles and the common elements of linear building patterns. Secondly, spatial conflicts within the combined linear pattern are resolved through the segmental building reconstruction of linear building patterns and a reduction in linear building patterns at junction buildings. Finally, spatial conflicts between combined linear patterns are solved based on the position and number of conflict buildings in the patterns. The proposed method is evaluated using a building dataset from OpenStreetMap. The experimental results indicate that the proposed method can preserve the spatial distribution of building patterns with better legibility than the usual typification method for individual linear building patterns. Full article

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16 pages, 3201 KiB

Open AccessArticle

Geographic Distribution of Lung and Bronchus Cancer Mortality and Elevation in the United States: Exploratory Spatial Data Analysis and Spatial Statistics

by Hoehun Ha

ISPRS Int. J. Geo-Inf. 2025, 14(4), 141; https://doi.org/10.3390/ijgi14040141 - 25 Mar 2025

Abstract

Lung and bronchus cancer, collectively called lung cancer, remains one of the most lethal malignancies worldwide, with its incidence and mortality rates continuing to pose significant public health challenges. Numerous studies have explored various risk factors for lung cancer, including smoking, environmental pollutants, [...] Read more.

Lung and bronchus cancer, collectively called lung cancer, remains one of the most lethal malignancies worldwide, with its incidence and mortality rates continuing to pose significant public health challenges. Numerous studies have explored various risk factors for lung cancer, including smoking, environmental pollutants, genetic predispositions, and occupational hazards. However, emerging research suggests that elevation above sea level may also influence lung and bronchus cancer prevalence and outcomes. We analyzed elevation data for 2662 contiguous U.S. counties to determine if there is a significant relationship between lung cancer and elevation. Moreover, we employed hierarchical multiple regression and a complex sample general linear model (CSGLM) to enhance the understanding of the factors influencing lung and bronchus cancer, with a particular focus on elevation. Using Local Moran’s I cluster analysis, we identified statistically significant hot spots and cold spots for the mortality rate related to lung cancer. In the hierarchical regression model, a significant correlation between lung cancer and elevation remained evident. This suggests that the risk of mortality from lung and bronchus cancer increases with decreasing elevation (R² = 0.601). Furthermore, within the CSGLM framework, an R² value of 0.763 highlighted a strong link between lung cancer mortality and elevation. This relationship remained significant even after accounting for complex sample designs and applying weight adjustments. This geographic correlation has not been documented in previous studies. Further research is necessary to elucidate the precise mechanisms through which elevation influences lung cancer biology. Full article

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19 pages, 3074 KiB

Open AccessArticle

Exploring the Use of Data in a Digital Twin for the Marine and Coastal Environment

by Shelley Haupt, Bolelang Sibolla, Raymond Molapo, Lizwe Mdakane and Nicolene Fourie

ISPRS Int. J. Geo-Inf. 2025, 14(4), 140; https://doi.org/10.3390/ijgi14040140 - 25 Mar 2025

Abstract

The ocean plays a vital role in our society and represents a constantly changing landscape that is not well understood and therefore needs continuous monitoring and research. Sustainable monitoring is essential to assess both the current and future state of our oceans. However, [...] Read more.

The ocean plays a vital role in our society and represents a constantly changing landscape that is not well understood and therefore needs continuous monitoring and research. Sustainable monitoring is essential to assess both the current and future state of our oceans. However, conventional monitoring faces significant challenges, including issues of accessibility, and spatial and temporal constraints. The development of digital twins of the ocean (DTO) offers an emerging technology that could revolutionise our understanding of marine and coastal environments. Current DTO have shown effectiveness in monitoring marine and coastal environments in the European context. However, there is a need for a DTO for the Southern African and Western Indian Ocean regions that addresses specific concerns that are relevant to these regions. Successful development of a DTO depends on the availability of high-quality data. Therefore, various data inputs are necessary to build an accurate digital twin. This paper explores the data that can be utilised in a DTO, detailing how different ocean variables are collected and integrated into the digital twin. As a first step towards the development of a DTO in these regions, the paper proposes a data management plan and its implementation in the development of DTO. The data management plan is based on the phases of data in a geospatial data life cycle. Challenges regarding the management of data in this DTO and possible solutions are presented in the conclusion. Full article

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24 pages, 9082 KiB

Open AccessArticle

Documentation for Architectural Heritage: A Historical Building Information Modeling Data Modeling Approach for the Valentino Castle North Wing

by Xiang Li, Lorenzo Teppati Losè and Fulvio Rinaudo

ISPRS Int. J. Geo-Inf. 2025, 14(4), 139; https://doi.org/10.3390/ijgi14040139 - 25 Mar 2025

Abstract

Although HBIM (Historical Building Information Modeling) excels in geometric data acquisition and modeling within Scan-to-BIM (Building Information Modeling) workflows, its application in digital documentation faces persistent challenges, such as balancing precision and efficiency, ambiguous information structures, and the absence of standardized protocols. To [...] Read more.

Although HBIM (Historical Building Information Modeling) excels in geometric data acquisition and modeling within Scan-to-BIM (Building Information Modeling) workflows, its application in digital documentation faces persistent challenges, such as balancing precision and efficiency, ambiguous information structures, and the absence of standardized protocols. To address these issues, this study refines key steps from the systematic Scan-to-BIM process and proposes a documentation-oriented HBIM workflow. The workflow is designed to tackle data complexity and semantic alignment challenges through detailed strategic planning, standard data collection, efficient geometric modeling, and ontology-based information integration. Validated in the Valentino Castle’s north wing digital archiving project, the proposed framework emphasizes archival management and standardization, reducing reliance on high-precision point cloud data and complex geometric modeling. Instead, it adopts low-precision geometric models as information storage containers, employing standardized information structures to manage and transfer heterogeneous data. Key contributions include the following: (1) establishing a requirements-driven and model-level-based framework for standardized project management; (2) introducing a component alignment concept to harmonize IFC (Industry Foundation Classes) standards and traditional terminologies; and (3) developing a four-level information structure to enhance HBIM parameter and database management. The workflow significantly reduces data acquisition and modeling time while offering a replicable methodology for heritage documentation, promoting cross-disciplinary collaboration and standardization in digital preservation practices. Full article

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19 pages, 6062 KiB

Open AccessArticle

Multi-Scenario Simulation of Urban Land Expansion Modes Considering Differences in Spatial Functional Zoning

by Jing Yang, Zheng Wang and Yizhong Sun

ISPRS Int. J. Geo-Inf. 2025, 14(4), 138; https://doi.org/10.3390/ijgi14040138 - 24 Mar 2025

Abstract

As a precious non-renewable resource, the rational utilization of land resources is crucial for global sustainable development, with urban land development scenario prediction and analysis serving as key methodologies to achieve this goal. Although previous studies have extensively explored urban land expansion simulation [...] Read more.

As a precious non-renewable resource, the rational utilization of land resources is crucial for global sustainable development, with urban land development scenario prediction and analysis serving as key methodologies to achieve this goal. Although previous studies have extensively explored urban land expansion simulation and scenario forecasting, further investigation is still required to simultaneously address spatial functional zoning differentiation and urban expansion mode diversity while simulating development trends under various expansion modes. In this study, we integrated major functional zones and ecological redlines to delineate urban spatial functional units and define development coefficients for construction land within each unit. Based on the spatial heterogeneity of expansion modes, the scopes of infill, sprawl, and leapfrog expansion modes were determined. Combining functional zoning and expansion mode zoning, we employed cellular automata model principles to design land conversion rules and simulate the evolution of land use under different expansion modes. Using Jiangyin City, China, as a case study, the model achieved a high simulation accuracy (kappa coefficient of 0.959), significantly outperforming comparative models. By predicting land-use patterns under different expansion scenarios and aligning with Jiangyin’s territorial planning goals, we recommend implementing infill–sprawl–leapfrog and infill–leapfrog–sprawl expansion modes. The results demonstrate that the model effectively supports the refined simulation of urban land expansion, providing a scientific basis for optimizing land resource allocation and balancing ecological protection with urban development. Future research could integrate multiple types of territorial control elements, refine land-use categories, and optimize prediction scenarios to enhance the model’s practicality and applicability. Full article

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

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