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Volume 14
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ISPRS Int. J. Geo-Inf., Volume 14, Issue 1 (January 2025) – 39 articles

Cover Story (view full-size image): Web map applications are widely used, yet their cartographic design often receives limited attention. This article evaluates eight popular web map applications (Mapy.cz, OpenStreetMap, Google Maps, Bing Maps, HERE Maps, MapQuest, ViaMichelin, and Locus Map) based on six cartographic aspects (Map Key, Map Scale, Map Layout, Navigation Elements, Labels, and Analytical Tools). By identifying inconsistencies in feature representation, such as the absence of certain symbols and variability in others, this study recommends unifying cartographic principles and further user testing to optimize the user interface and experience of web map applications. An interesting finding is the absence of cartographic symbols and labels of some elements in some applications. View this paper
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23 pages, 11956 KiB  
Article
Interpretable Machine Learning Insights into the Factors Influencing Residents’ Travel Distance Distribution
by Rui Si, Yaoyu Lin, Dongquan Yang and Qijin Guo
ISPRS Int. J. Geo-Inf. 2025, 14(1), 39; https://doi.org/10.3390/ijgi14010039 - 20 Jan 2025
Viewed by 642
Abstract
Understanding intra-urban travel patterns through quantitative analysis is crucial for effective urban planning and transportation management. In previous studies, a range of distribution functions were modeled to lay the groundwork for human mobility research. However, few studies have explored the nonlinear relationships between [...] Read more.
Understanding intra-urban travel patterns through quantitative analysis is crucial for effective urban planning and transportation management. In previous studies, a range of distribution functions were modeled to lay the groundwork for human mobility research. However, few studies have explored the nonlinear relationships between travel distance patterns and environmental factors. Using travel distance data from ride-hailing services, this research divides a study area into 1 × 1 km grid cells, modeling the best travel distance distribution and calculating the coefficients of each grid. A machine learning framework (Extreme Gradient Boosting combined with Shapley Additive Explanations) is introduced to interpret the factors influencing these distributions. Our results emphasize that the travel distance of human movement tends to follow a log-normal distribution and exhibits spatial heterogeneity. Key factors affecting travel distance distributions include the distance to the city center, bus station density, land use entropy, and the density of companies. Most environmental variables exhibit nonlinear and threshold effects on the log-normal distribution coefficients. These findings significantly advance our understanding of ride-hailing travel patterns and offer valuable insights into the spatial dynamics of human mobility. Full article
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36 pages, 8910 KiB  
Article
Mapping the Dream: Designing Optimal E-Bike Routes in Valparaíso, Chile, Using a Multicriteria Analysis and an Experimental Study
by Vicente Aprigliano, Catalina Toro, Gonzalo Rojas, Iván Bastías, Marcus Cardoso, Tálita Santos, Marcelino Aurélio Vieira da Silva, Emilio Bustos, Ualison Rébula de Oliveira and Sebastian Seriani
ISPRS Int. J. Geo-Inf. 2025, 14(1), 38; https://doi.org/10.3390/ijgi14010038 - 20 Jan 2025
Viewed by 500
Abstract
The city of Valparaíso, Chile, faces significant mobility challenges due to its steep slopes, complex urban infrastructure, and socioeconomic conditions. In this direction, this study explores the potential promotion of E-bike uses by identifying the optimal routes that connect metro stations to strategic [...] Read more.
The city of Valparaíso, Chile, faces significant mobility challenges due to its steep slopes, complex urban infrastructure, and socioeconomic conditions. In this direction, this study explores the potential promotion of E-bike uses by identifying the optimal routes that connect metro stations to strategic hilltop streets in the city. A hybrid methodology combining a multicriteria GIS-based analysis and an experimental study was used to evaluate potential routes and the possibility of increasing the power limitations for non-motorized mobility in Chile. Fifteen routes were assessed based on criteria including the slope, traffic safety, directionality, intersections, and travel distance. The results indicate that routes such as Cumming from Puerto and Bellavista stand out as the most viable for e-bike use given their favorable characteristics. The experimental study revealed that higher-powered E-bikes (500 W and 750 W) would be more able to overcome the steep slopes of Valparaíso, with an average speed of 5.36 km/h and 9.52 km/h on routes with a 10.88% average slope. These findings challenge the current regulatory limit of 250 W for non-motorized vehicles in Chile, highlighting the potential benefits of increasing their power limits to enhance sustainable mobility in the hilly urban contexts of this country. This study highlights the need to adapt urban mobility policies to the unique topographical conditions of each city. Future research should build upon more experimental studies, develop specific street-scale analyses using audit methods, incorporate climate-related variables, and evaluate the economic viability of e-bike infrastructure. Addressing these aspects could position Valparaíso as a leading example of sustainable urban mobility for cities facing comparable challenges. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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28 pages, 55723 KiB  
Article
Spatiotemporal Changes and Trade-Offs/Synergies of Ecosystem Services in the Qin-Mang River Basin
by Jiwei Zhao, Luyao Wang, Dong Jia and Yaowen Wang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 37; https://doi.org/10.3390/ijgi14010037 - 19 Jan 2025
Viewed by 613
Abstract
The Qin-Mang River Basin is an important biodiversity conservation area in the Yellow River Basin. Studying the spatiotemporal changes in its ecosystem services (ESs) and the trade-offs and synergies (TOSs) between them is crucial for regional ecological protection and high-quality development. This study, [...] Read more.
The Qin-Mang River Basin is an important biodiversity conservation area in the Yellow River Basin. Studying the spatiotemporal changes in its ecosystem services (ESs) and the trade-offs and synergies (TOSs) between them is crucial for regional ecological protection and high-quality development. This study, based on land use type (LUT), and meteorological and soil data from 1992 to 2022, combined with the InVEST model, correlation analysis, and spatial autocorrelation analysis, explores the impacts of land use/land cover changes (LUCCs) on ESs. The results show that: (1) driven by urbanization and economic development, the expansion of built-up areas has replaced cultivated land and forests, with 35,000 hectares of farmland lost, thereby increasing pressure on ESs; (2) ESs show an overall downward trend, habitat quality (HQ) has deteriorated, carbon storage (CS) remains stable but the area of low CS has expanded, and sediment delivery ratio (SDR) and water yield (WY) fluctuate due to human activities and climate influence; (3) the TOSs of ESs change dynamically, with strong synergies among HQ, CS, and SDR. However, in areas with water scarcity, the negative correlation between HQ and WY has strengthened; (4) spatial autocorrelation analysis reveals that in 1992, significant positive synergies existed between ESs in the northern and northwestern regions, with WY negatively correlated with other services. By 2022, accelerated urbanization has intensified trade-off effects in the southern and eastern regions, leading to significant ecological degradation. This study provides scientific support for the sustainable management and policymaking of watershed ecosystems. Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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25 pages, 12602 KiB  
Article
Concept, Framework, and Data Model for Geographical Soundscapes
by Xiu Lu, Guannan Li, Xiaoqing Song, Liangchen Zhou and Guonian Lv
ISPRS Int. J. Geo-Inf. 2025, 14(1), 36; https://doi.org/10.3390/ijgi14010036 - 18 Jan 2025
Viewed by 435
Abstract
Existing concepts and frameworks of soundscapes focus on the analysis and description of the sound source but do not explore geographical environment parameters and receiver characteristics in the geographical scene. Existing soundscape data models ignore the geographical environment and receiver information, which limits [...] Read more.
Existing concepts and frameworks of soundscapes focus on the analysis and description of the sound source but do not explore geographical environment parameters and receiver characteristics in the geographical scene. Existing soundscape data models ignore the geographical environment and receiver information, which limits the comprehensive understanding and expression of soundscapes. They cannot study the relationship between the elements related to the sound source or explore the interaction mechanism between the sound and geographical environments. From the geographical perspective, this study extends soundscape to geographical soundscape (geo-soundscape), defines geo-soundscape by the cognition of the geographical scene, analyzes and expresses the conceptual framework of soundscapes through a content hierarchy structure, and expands the characteristics of the receiver, geographical environment parameters, further-obtained geographical scene elements, and scene element description dimensions. Based on the MPEG-7 data model, this study develops a geographical-MPEG-7 data model which consists of low-, medium-, and high-level feature classes. Taking as an example soundscape data collected on a university road in Nanjing, Jiangsu Province, in a real geographical environment, the concept, framework, and data model architecture of the geo-soundscape proposed in this study are demonstrated and described to validate the completeness and feasibility of the proposed model. The results show that our basic framework for a geo-soundscape is well adapted to the Geo-MPEG-7 data model. The model can store, organize, and describe all the soundscape information containing all elements and inter-element relationships. The soundscape in the real environment is fully expressed and described. This study provides a new research direction for soundscapes from a geographical perspective and provides guidance for urban planning and landscape design. Full article
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24 pages, 6475 KiB  
Article
Towards AI-Assisted Mapmaking: Assessing the Capabilities of GPT-4o in Cartographic Design
by Abdulkadir Memduhoğlu
ISPRS Int. J. Geo-Inf. 2025, 14(1), 35; https://doi.org/10.3390/ijgi14010035 - 17 Jan 2025
Viewed by 718
Abstract
Cartographic design is fundamental to effective mapmaking, requiring adherence to principles such as visual hierarchy, symbolization, and color theory to convey spatial information accurately and intuitively, while Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed various fields, their application in cartographic [...] Read more.
Cartographic design is fundamental to effective mapmaking, requiring adherence to principles such as visual hierarchy, symbolization, and color theory to convey spatial information accurately and intuitively, while Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed various fields, their application in cartographic design remains underexplored. This study assesses the capabilities of a multimodal advanced LLM, GPT-4o, in understanding and suggesting cartographic design elements, focusing on adherence to established cartographic principles. Two assessments were conducted: a text-to-text evaluation and an image-to-text evaluation. In the text-to-text assessment, GPT-4o was presented with 15 queries derived from key concepts in cartography, covering classification, symbolization, visual hierarchy, color theory, and typography. Each query was posed multiple times under different temperature settings to evaluate consistency and variability. In the image-to-text evaluation, GPT-4o analyzed maps containing deliberate cartographic errors to assess its ability to identify issues and suggest improvements. The results indicate that GPT-4o demonstrates general reliability in text-based tasks, with variability influenced by temperature settings. The model showed proficiency in classification and symbolization tasks but occasionally deviated from theoretical expectations. In visual hierarchy and layout, the model performed consistently, suggesting appropriate design choices. In the image-to-text assessment, GPT-4o effectively identified critical design flaws such as inappropriate color schemes, poor contrast and misuse of shape and size variables, offering actionable suggestions for improvement. However, limitations include dependency on input quality and challenges in interpreting nuanced spatial relationships. The study concludes that LLMs like GPT-4o have significant potential in cartographic design, particularly for tasks involving creative exploration and routine design support. Their ability to critique and generate cartographic elements positions them as valuable tools for enhancing human expertise. Further research is recommended to enhance their spatial reasoning capabilities and expand their use of visual variables beyond color, thereby improving their applicability in professional cartographic workflows. Full article
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19 pages, 1843 KiB  
Article
An Efficient Tourism Path Approach Based on Improved Ant Colony Optimization in Hilly Areas
by Mohamed A. Damos, Wenbo Xu, Jun Zhu, Ali Ahmed and Abdolraheem Khader
ISPRS Int. J. Geo-Inf. 2025, 14(1), 34; https://doi.org/10.3390/ijgi14010034 - 17 Jan 2025
Viewed by 606
Abstract
The expansion of the tourism industry has led to the development of various methods to find optimal tourism paths. However, planning tourism paths in hilly areas remains complex and has specific challenges. Different algorithms have been used to plan tourism paths in flat [...] Read more.
The expansion of the tourism industry has led to the development of various methods to find optimal tourism paths. However, planning tourism paths in hilly areas remains complex and has specific challenges. Different algorithms have been used to plan tourism paths in flat and hilly terrains, including the traditional Ant Colony Optimization (ACO). Although widely used, this algorithm faces a number of limitations due to its slow implementation and pheromone update rules. This paper introduces a new approach to overcome these limitations. It presents a method for efficiently optimizing tourism paths in hilly areas based on an improved version of the ACO algorithm. The limitations of the traditional ACO and the Genetic Algorithm (GA) are addressed by improving pheromone updating techniques and implementing new initialization parameters. This approach provides a comprehensive and efficient method for planning hiking trails in hilly regions, considering dynamic tourism objectives such as temperature, atmospheric pressure, and health status. The proposed method is implemented to develop tourist routes in the hilly Jebel Marra region in Western Sudan. A comparison is provided between the effectiveness of this approach and the GA and traditional ACO algorithms. The advantage of the proposed approach is illustrated by results showing an optimization time of 0 points and 27 s compared to 0 points and 45 s and 0 points and 40 s for GA and ACO, respectively. Full article
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31 pages, 29388 KiB  
Article
Patch-Level and Neighborhood-Dependency Spatial Optimization Method (PNO): Application to Urban Land-Use Planning to Facilitate Both Socio-Economic and Environmental Development in Beijing
by Yuhan Cheng, Xiuyuan Zhang, Qi Zhou, Xiaoyan Dong and Shihong Du
ISPRS Int. J. Geo-Inf. 2025, 14(1), 33; https://doi.org/10.3390/ijgi14010033 - 16 Jan 2025
Viewed by 635
Abstract
Rapid urban expansion and chaotic urban land-use patterns cause many socio-economic and environmental issues, e.g., traffic congestion and urban heat islands; thus, scientific planning considering land-use trade-offs and layout optimization is highly required for resolving these issues, especially in the urban renewal stage. [...] Read more.
Rapid urban expansion and chaotic urban land-use patterns cause many socio-economic and environmental issues, e.g., traffic congestion and urban heat islands; thus, scientific planning considering land-use trade-offs and layout optimization is highly required for resolving these issues, especially in the urban renewal stage. However, previous spatial optimization methods were weak in processing land-use patches and ignored their neighborhood dependency, leading to fragmented and inapplicable optimization results. Accordingly, this study proposes a patch-level and neighborhood-dependency spatial optimization method (PNO) to adjust urban land-use patterns considering multiple optimization targets (i.e., improving population and economy but controlling land surface temperature). The PNO represents land-use patterns in a graph structure, quantifies land-use patterns’ impacts on the population, economy, and land surface temperature, defines the spatiotemporal constraints of land-use optimization considering neighborhood-dependency and optimization sequences, and finally optimizes land uses and their spatial layouts based on a multi-objective genetic algorithm. Experiments were conducted in the urban area of Beijing, and the results suggested that, after optimization, the population and GDP can be improved by 667,323 people (4.72%) and USD 10.69 billion in products (2.75%) in the study area; meanwhile, the land surface temperature can be reduced by 0.12 °C (−0.32%). Through comparison, the proposed PNO outperforms previous spatial optimization methods, e.g., NSGA-II, in processing land-use patches as well as their neighborhoods. Taking the land-use map in 2022 as a reference, the PNO optimization results are more consistent with actual land-use changes (consistency of 25%), compared to the existing spatial optimization results (consistency of 10.6%). Thus, PNO is more applicable to land-use planning in urban renewal circumstances. Full article
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25 pages, 26226 KiB  
Article
Portraying the Geography of US Airspace with 3-Dimensional GIS-Based Analysis and Visualization
by Thi Hong Diep Dao and David G. Havlick
ISPRS Int. J. Geo-Inf. 2025, 14(1), 32; https://doi.org/10.3390/ijgi14010032 - 15 Jan 2025
Viewed by 570
Abstract
The United States identifies, monitors, and defends a vast network of controlled airspaces surrounding its own and allied territories. These controlled airspaces include civilian aviation classes (A through G), drone flying regions, and special use (military) air classifications. These controlled spaces are invisible [...] Read more.
The United States identifies, monitors, and defends a vast network of controlled airspaces surrounding its own and allied territories. These controlled airspaces include civilian aviation classes (A through G), drone flying regions, and special use (military) air classifications. These controlled spaces are invisible to the naked eye and often go unnoticed. Managing and portraying data that function in two and three dimensions poses significant challenges that have hindered prior analyses or geovisualizations of controlled airspaces, but we demonstrate here how many of these can be surmounted to visually represent the spatial extent and patterns of US-controlled airspace. In this paper, we demonstrate how these complex spaces can be graphically represented and highlight how cartographic and geovisual representations of often-overlooked domains contribute to a richer understanding of the reach and character of US airspace. The methods described for this work can be extended to other types of multidimensional objects and may facilitate more robust considerations of how Geographical Information Science (GIS) can be useful in analyzing and depicting airspace and territorial claims in three dimensions. Full article
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15 pages, 9816 KiB  
Article
Spatial Analysis of Maritime Disasters in the Philippines: Distribution Patterns and Identification of High-Risk Areas
by Glenn D. Aguilar, Yasmin P. Tirol, Ryan M. Basina and Jamaica Alcedo
ISPRS Int. J. Geo-Inf. 2025, 14(1), 31; https://doi.org/10.3390/ijgi14010031 - 14 Jan 2025
Viewed by 828
Abstract
Maritime accidents frequently occur in the Philippine archipelagic waters, often resulting in significant loss of life. These incidents highlight the urgent need for improvements in the country’s maritime safety systems. By utilising accident data from the Philippine Coast Guard and the GISIS IMO [...] Read more.
Maritime accidents frequently occur in the Philippine archipelagic waters, often resulting in significant loss of life. These incidents highlight the urgent need for improvements in the country’s maritime safety systems. By utilising accident data from the Philippine Coast Guard and the GISIS IMO databases, spatial analytical approaches were employed to determine incident distribution patterns and resulted in an overall depiction of the likelihood component of risk across the country’s territorial waters. Kernel density and hotspot analysis revealed areas where incidents were concentrated and where statistically significant hotspots occurred. The Maxent tool was used to develop risk likelihood models for the incident locations using environmental rasters representing wind speed, significant wave height, depth, surface current, land distance and port distance. Model performance metrics including the AUC, TSS and Kappa were used to compare the two datasets and provide confidence on model robustness. Variable contribution figures showed that land distance is the most influential variable, with the majority of high-risk areas predominantly located near population centres. The resulting maps provide an intuitive and informative depiction of the characteristic patterns of maritime accidents in the country, identify areas of high risk requiring immediate attention and offer valuable insights to support strategies for improving and enhancing the country’s maritime safety. Full article
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28 pages, 23316 KiB  
Article
Synergy of Remote Sensing and Geospatial Technologies to Advance Sustainable Development Goals for Future Coastal Urbanization and Environmental Challenges in a Riverine Megacity
by Minza Mumtaz, Syed Humayoun Jahanzaib, Waqar Hussain, Sadia Khan, Youssef M. Youssef, Saleh Qaysi, Abdalla Abdelnabi, Nassir Alarifi and Mahmoud E. Abd-Elmaboud
ISPRS Int. J. Geo-Inf. 2025, 14(1), 30; https://doi.org/10.3390/ijgi14010030 - 14 Jan 2025
Viewed by 819
Abstract
Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of [...] Read more.
Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km2 by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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29 pages, 17219 KiB  
Article
Enhancing Accessibility in Public Spaces: A Computational Study of Hatirjheel Lakefront Using Space Syntax
by Sharif Tousif Hossain, Baqer Al-Ramadan, Muhammad Bilal and Hamad Ahmed Altuwaijri
ISPRS Int. J. Geo-Inf. 2025, 14(1), 29; https://doi.org/10.3390/ijgi14010029 - 14 Jan 2025
Viewed by 552
Abstract
Public spaces are vital for urban living, contributing to the environmental, social, and economic aspects of city life. Hatirjheel Lakefront, a newly developed recreational area in Dhaka, offers significant potential for enhancing accessibility and connectivity in a rapidly urbanizing metropolis. This study aims [...] Read more.
Public spaces are vital for urban living, contributing to the environmental, social, and economic aspects of city life. Hatirjheel Lakefront, a newly developed recreational area in Dhaka, offers significant potential for enhancing accessibility and connectivity in a rapidly urbanizing metropolis. This study aims to evaluate global and local integration of access routes and propose strategies to improve pedestrian and vehicular connectivity using Space Syntax methodology and DepthmapX V10 software. The key findings indicate that while Hatirjheel demonstrates strong global integration, regional integration remains moderate, with certain access roads being underutilized due to poor connectivity. Recommendations include enhancing integration through connecting dead-end roads, improving pedestrian pathways, and constructing foot-over bridges to mitigate vehicular traffic barriers. This study contributes to urban planning by providing actionable insights to optimize accessibility in public spaces, supporting recreational and economic activities. The findings are critical for creating a more integrated urban fabric in Dhaka, ensuring sustainable urban growth. This research provides actionable strategies for urban planners to optimize the use of public spaces, reinforcing the role of Hatirjheel as a vital component of Dhaka’s urban network. Full article
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17 pages, 3556 KiB  
Article
Quantification of Soil–Water Erosion Using the RUSLE Method in the Mékrou Watershed (Middle Niger River)
by Rachid Abdourahamane Attoubounou, Hamidou Diawara, Ralf Ludwig and Julien Adounkpe
ISPRS Int. J. Geo-Inf. 2025, 14(1), 28; https://doi.org/10.3390/ijgi14010028 - 14 Jan 2025
Viewed by 537
Abstract
Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in [...] Read more.
Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes. Full article
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21 pages, 7111 KiB  
Article
Construction of 3D Indoor Topological Models Based on Improved Face Sorting
by Qun Sun, Xinwu Zhan and Pu Tang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 27; https://doi.org/10.3390/ijgi14010027 - 13 Jan 2025
Viewed by 476
Abstract
Indoor location-based services and applications need to obtain information about the indoor spatial layouts and topological relationships of indoor spaces. The 3D city modeling data standard CityGML describes the indoor geometric and semantic information of buildings, but the surfaces composing a volume are [...] Read more.
Indoor location-based services and applications need to obtain information about the indoor spatial layouts and topological relationships of indoor spaces. The 3D city modeling data standard CityGML describes the indoor geometric and semantic information of buildings, but the surfaces composing a volume are discrete, leading to invalid volumes. Moreover, the topological adjacency relationships of adjacent indoor spaces have not yet been described, which makes it difficult to realize effective queries and analyses for indoor applications. In this paper, we present a 3D topological data model for indoor spaces that adopts five topological primitives, namely, node, edge, loop, face, and solid, to describe the topological relationships of indoor spaces. Then, by improving the existing face-sorting method according to vector products in 3D space, a method for constructing 3D topological relationships for indoor spaces is proposed, which successively constructs the topological hierarchical combination of volume and the topological adjacency relationships of adjacent volumes. The experimental results show that by using the improved face-sorting method proposed in this work, the relative positions of faces are directly determined to sort the faces set, which avoids relatively cumbersome calculations and improves the efficiency of constructing 3D topological relationships for indoor spaces. Full article
(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)
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20 pages, 6345 KiB  
Article
POI Data Fusion Method Based on Multi-Feature Matching and Optimization
by Yue Wang, Cailin Li, Hongjun Zhang, Baoyun Guo, Xianlong Wei and Zhao Hai
ISPRS Int. J. Geo-Inf. 2025, 14(1), 26; https://doi.org/10.3390/ijgi14010026 - 12 Jan 2025
Viewed by 441
Abstract
The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. [...] Read more.
The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. Firstly, when dealing with POI (point of interest) data, the similarity of POI data in terms of name, address, and distance is calculated by combining the improved hybrid similarity method, the Jaccard method, and the Euclidean metric method. Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. Finally, taking the area within the Second Ring Road in Beijing as the experimental area, the POI data of Tencent Maps and Amap are collected to verify the method proposed in this paper. The experimental results show that, compared with the existing POI matching methods, the accuracy and recall rate of the results obtained by the POI matching and fusion method proposed in this paper are significantly improved, which verifies the accuracy and feasibility of the matching. Full article
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21 pages, 5497 KiB  
Article
A New Construction Method for Rectangular Cartograms
by Lina Wang, Haoxun Yuan, Xiang Li, Pengfei Lu and Yaru Li
ISPRS Int. J. Geo-Inf. 2025, 14(1), 25; https://doi.org/10.3390/ijgi14010025 - 11 Jan 2025
Viewed by 532
Abstract
The rectangular cartogram is a geospatial visualization method that blends the characteristics of maps and charts. By simplifying geographic regions into rectangles and using the area of each rectangle to represent statistical data, it enables efficient geovisualization. This paper summarizes and analyzes the [...] Read more.
The rectangular cartogram is a geospatial visualization method that blends the characteristics of maps and charts. By simplifying geographic regions into rectangles and using the area of each rectangle to represent statistical data, it enables efficient geovisualization. This paper summarizes and analyzes the advantages and limitations of two main approaches used in current rectangular cartogram construction algorithms. To address the issues of high computational cost and inadequate preservation of adjacency and relative positional relationships in existing algorithms, we propose and implement a new rectangular cartogram construction algorithm. This algorithm simplifies the layout computation process while ensuring that the adjacency and relative positional relationships between regions during the layout generation process have only minor errors. In adjusting rectangle areas to match attribute values, the algorithm adopts a “region-by-region placement” strategy, ensuring that errors in area accuracy remain within a small range, while also keeping errors in adjacency and relative positional relationships minimal. Finally, by comparing the results of our algorithm with those of existing algorithms using real-world data with varying distribution characteristics, we demonstrate its effectiveness. The results show that the proposed algorithm not only improves computational efficiency but also effectively displays the adjacency and relative positional relationships between regions. Full article
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23 pages, 13513 KiB  
Article
Revealing the City Influence and Its Pattern Using Web Search Data: A New Perspective Through Attention Flow
by Xiaoxiao Wang, Haiping Zhang, Zitong Li, Xin Yang, Ziyi Qian, Duo Bian and Yongxin Zhao
ISPRS Int. J. Geo-Inf. 2025, 14(1), 24; https://doi.org/10.3390/ijgi14010024 - 10 Jan 2025
Viewed by 624
Abstract
City influence is a critical topic in regional studies, reflecting how cities draw attention and exert impact in various domains. Understanding city influence is essential for fostering sustainable urban growth. However, existing studies have failed to fully explore the characteristics of city influence [...] Read more.
City influence is a critical topic in regional studies, reflecting how cities draw attention and exert impact in various domains. Understanding city influence is essential for fostering sustainable urban growth. However, existing studies have failed to fully explore the characteristics of city influence reflected by collective behaviors from a bottom-up perspective. This study investigates how individual search behaviors mirror the attention cities attract, providing insights into their perceived influence. An “attention flow” model is developed to differentiate between cities that draw significant interest and those that show a strong preference for these influential hubs. This research focuses on cities in China, analyzing the spatial patterns and factors that affect city influence using spatial statistical methods. The results show that 69% of the cities that exhibit a strong preference are geographically closer to the more influential cities, emphasizing the role of geographical proximity in shaping urban influence in the digital age. Additionally, the study reveals patterns of power dislocation, partnership, and siphoning between cities. A consistent relationship is identified between influential cities and their more connected cities, particularly where administrative hubs tend to attract nearby cities focused on science and education. This research deepens our understanding of how city influence is shaped by digital behaviors and spatial relationships, providing insights for policymakers to foster balanced regional development and enhance inter-city cooperation. Full article
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26 pages, 6664 KiB  
Article
Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China
by Yihang Xiao, Cunzhi Li, Zhiwu Zhou, Dongyang Hou and Xiaoguang Zhou
ISPRS Int. J. Geo-Inf. 2025, 14(1), 23; https://doi.org/10.3390/ijgi14010023 - 9 Jan 2025
Viewed by 492
Abstract
As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is [...] Read more.
As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development. Full article
(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)
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22 pages, 3983 KiB  
Article
Evaluation of Cross-Border Transport Connectivity and Analysis of Spatial Patterns in Latin America
by Changqi Miao, Yinbao Zhang, Xinjia Zhang, Jianzhong Liu and Shike Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 22; https://doi.org/10.3390/ijgi14010022 - 8 Jan 2025
Viewed by 606
Abstract
The study of cross-border transport connectivity is significant for the development of regional integration and insight into global patterns. Comprehensive connectivity evaluations are lacking and insufficient attention has been paid to Latin American connectivity, so it is of great practical importance to comprehensively [...] Read more.
The study of cross-border transport connectivity is significant for the development of regional integration and insight into global patterns. Comprehensive connectivity evaluations are lacking and insufficient attention has been paid to Latin American connectivity, so it is of great practical importance to comprehensively and rationally evaluate Latin American connectivity. In this article, based on the four modes of transport, namely, sea, road, air and railroad, and using the actual trade volume as a comparison, a connectivity evaluation index system with considerable reliability and generalization ability was constructed using the expert scoring method, QAP correlation analysis, QAP regression, and statistics, and the connectivity calculations of Latin America were obtained. Analyzing the connectivity structure of Latin America, it was found that cross-border passenger and cargo transport in the region was dominated by sea transport and supplemented by road and air transport, with railroads used the least. The overall connectivity of Latin America was low, and the overall development was unbalanced, with a strong law of spatial differentiation, which was mainly manifested in the strongest connectivity of the integrated coastal countries, followed by the island countries, and the lowest connectivity of the landlocked countries. Different countries assumed different roles in regional connectivity, which could be categorized into global hub type, local hub type and non-hub type based on the calculations. There was a spatial pattern of decreasing connectivity with distance in typical countries, but the rate of decline was closely related to their geographic location and the role they played in the connectivity network. This study can provide reference and inspiration for regional connectivity evaluation, improvement, and sustainable development. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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21 pages, 11620 KiB  
Article
Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering
by Xinjian Fang, Yingdan Zhang, Hao Tan, Chao Liu and Xu Yang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 21; https://doi.org/10.3390/ijgi14010021 - 7 Jan 2025
Viewed by 994
Abstract
This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings [...] Read more.
This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings for the average magnitude of spatial position gradients, and adjustments to the scaling learning rate, the optimal parameter configuration for the 3DGS model, specifically tailored for indoor modeling scenarios, is determined. Firstly, utilizing a self-collected dataset, a comprehensive comparison was conducted among COLLI-SION-MAPping (abbreviated as COLMAP (V3.7), an open-source software based on Structure from Motion and Multi-View Stereo (SFM-MVS)), Context Capture (V10.2) (abbreviated as CC, a software utilizing oblique photography algorithms), Neural Radiance Fields (NeRF), and the currently renowned 3DGS algorithm. The key dimensions of focus included the number of images, rendering time, and overall rendering effectiveness. Subsequently, based on this comparison, rigorous qualitative and quantitative evaluations are further conducted on the overall performance and detail processing capabilities of the 3DGS algorithm. Finally, to meet the specific requirements of indoor scene modeling and rendering, targeted parameter tuning is performed on the algorithm. The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. The experimental results prove that the improved 3DGS algorithm exhibits superior expressive power and persuasiveness in indoor scene rendering. Full article
(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)
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35 pages, 7483 KiB  
Article
Space Efficiency of Transit-Oriented Station Areas: A Case Study from a Complex Adaptive System Perspective
by Jinwen Fan, Zhenwu Shi, Jie Liu and Jinru Wang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 20; https://doi.org/10.3390/ijgi14010020 - 6 Jan 2025
Viewed by 567
Abstract
Transit-oriented development (TOD) has been widely adopted in urban planning to alleviate traffic congestion, urban sprawl, and other problems. The TOD metro station area, as a dynamic and open spatial system, presents typical complex features. To improve urban planning by understanding the complex [...] Read more.
Transit-oriented development (TOD) has been widely adopted in urban planning to alleviate traffic congestion, urban sprawl, and other problems. The TOD metro station area, as a dynamic and open spatial system, presents typical complex features. To improve urban planning by understanding the complex features of metro station areas, this study proposes a comprehensive evaluation method using complex adaptive system theory (CAS) to assess space efficiency and the use of an evaluation method like COWA (continuous ordered weighted averaging) operator and cloud model to show efficiency. Factors include external relevance, internal coordination, and environmental adaptation. This study uses Museum Station of Harbin Railway Transportation as the case study, and the results show that the space efficiency of Harbin’s TOD metro station areas are lacking in internal coordination and environmental adaptation. The proposed evaluation method not only identifies areas of space inefficiencies in urban rail transit station areas but also provides valuable insights for informed decision-making and future urban development initiatives. Full article
(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)
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36 pages, 25347 KiB  
Article
Construction of a Real-Scene 3D Digital Campus Using a Multi-Source Data Fusion: A Case Study of Lanzhou Jiaotong University
by Rui Gao, Guanghui Yan, Yingzhi Wang, Tianfeng Yan, Ruiting Niu and Chunyang Tang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 19; https://doi.org/10.3390/ijgi14010019 - 3 Jan 2025
Viewed by 866
Abstract
Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several [...] Read more.
Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several challenges. They often rely on costly commercial platforms, struggle with integrating heterogeneous datasets, and require complex workflows to achieve both high precision and comprehensive campus coverage. This paper addresses these issues by proposing a systematic multi-source data fusion approach that employs open-source technologies to generate a real-scene 3D digital campus. A case study of Lanzhou Jiaotong University is presented to demonstrate the feasibility of this approach. Firstly, oblique photography based on unmanned aerial vehicles (UAVs) is used to capture large-scale, high-resolution images of the campus area, which are then processed using open-source software to generate an initial 3D model. Afterward, a high-resolution model of the campus buildings is then created by integrating the UAV data, while 3D Digital Elevation Model (DEM) and OpenStreetMap (OSM) building data provide a 3D overview of the surrounding campus area, resulting in a comprehensive 3D model for a real-scene digital campus. Finally, the 3D model is visualized on the web using Cesium, which enables functionalities such as real-time data loading, perspective switching, and spatial data querying. Results indicate that the proposed approach can effectively get rid of reliance on expensive proprietary systems, while rapidly and accurately reconstructing a real-scene digital campus. This framework not only streamlines data harmonization but also offers an open-source, practical, cost-effective solution for real-scene 3D digital campus construction, promoting further research and applications in twin city, Virtual Reality (VR), and Geographic Information Systems (GIS). Full article
(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)
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26 pages, 4300 KiB  
Article
HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning
by Tailong Li, Renyao Chen, Yilin Duan, Hong Yao, Shengwen Li and Xinchuan Li
ISPRS Int. J. Geo-Inf. 2025, 14(1), 18; https://doi.org/10.3390/ijgi14010018 - 3 Jan 2025
Viewed by 582
Abstract
The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge [...] Read more.
The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective use and representation of semantic content. This paper presents HGeoKG, a hierarchical geographic knowledge graph that comprehensively models hierarchical structures, attributes, and spatial relationships of multi-type geographic entities. Based on the concept and construction methods of HGeoKG, this paper developed a dataset named HGeoKG-MHT-670K. Statistical analysis reveals significant regional heterogeneity and long-tail distribution patterns in HGeoKG-MHT-670K. Furthermore, extensive geographic knowledge reasoning experiments on HGeoKG-MHT-670K show that most knowledge graph embedding (KGE) models fail to achieve satisfactory performance. This suggests the need to accommodate spatial heterogeneity across different regions and improve the embedding quality of long-tail geographic entities. HGeoKG serves as both a reference for GeoKG construction and a benchmark for geographic knowledge reasoning, driving the development of geographical artificial intelligence (GeoAI). Full article
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17 pages, 3453 KiB  
Article
Investigating Social Vulnerability to Extreme Heat: Heat Islands and Climate Shelters in Urban Contexts: The Case of Bologna
by Elisa Maccabiani, Munazza Usmani, Riccardo Nanni and Maurizio Napolitano
ISPRS Int. J. Geo-Inf. 2025, 14(1), 17; https://doi.org/10.3390/ijgi14010017 - 3 Jan 2025
Viewed by 1723
Abstract
In this article we present three instruments: (1) a social vulnerability to extreme heat index to identify the areas of a city (and populations thereof) more vulnerable to extreme heat due to climate change (heat islands); (2) a new overall fragility index that [...] Read more.
In this article we present three instruments: (1) a social vulnerability to extreme heat index to identify the areas of a city (and populations thereof) more vulnerable to extreme heat due to climate change (heat islands); (2) a new overall fragility index that incorporates social vulnerability to extreme heat as well as socioeconomic indicators; and (3) a climate shelter index (CSI) to identify areas within a city that can provide relief from extreme heat based on green and blue solutions. We elaborated these three indexes to measure social vulnerability to extreme heat in the municipality of Bologna, which serves as this article’s case study. By analyzing the connections between social vulnerability to extreme heat and several socio-demographic variables in Bologna, we found that a decrease in income is significantly correlated with an increase in social vulnerability to extreme heat in urban contexts. A comparison between our new overall fragility index and the existing index adopted by the municipality of Bologna (Indice di fragilità, Comune di Bologna) showed that about 75% of the statistical areas observed are worse off when social vulnerability to extreme heat is also considered. Considering social vulnerability to extreme heat shows vulnerabilities in a city (here: Bologna) that the pre-existing index did not consider. These findings and our new indexes can support the Bologna administration (and other local administrations) in addressing the consequences of climate change for their most vulnerable residents. Full article
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43 pages, 10533 KiB  
Article
Footprints of the Future: Cleaner and Faster Transportation with Shared E-Scooter Operational Models
by Ömer Kaya
ISPRS Int. J. Geo-Inf. 2025, 14(1), 16; https://doi.org/10.3390/ijgi14010016 - 2 Jan 2025
Viewed by 680
Abstract
In recent years, shared e-scooters have become increasingly popular as a mode of transportation in urban areas. Shared e-scooters have emerged as a convenient and sustainable transportation option in urban areas, providing users with a flexible and efficient way to travel short distances [...] Read more.
In recent years, shared e-scooters have become increasingly popular as a mode of transportation in urban areas. Shared e-scooters have emerged as a convenient and sustainable transportation option in urban areas, providing users with a flexible and efficient way to travel short distances within a city. Many service providers and local municipalities are interested in implementing shared e-scooter operational models. However, determining which operating model to prefer and what the service areas will be is a significant problem. We aimed to solve the implementation of three different operational models, the site selection problem of station locations, and service areas for Erzurum, the metropolitan city in this study. As shared e-scooter is quite a new transportation mode; information collected to assess the operational models’ sustainability performance may be indeterminate and vague. In this study, the Geographic Information System (GIS)-based hybrid multi-criteria decision-making (MCDM) method is proposed for the solution of implementation, site selection, and service areas problems of three different shared e-scooter operational models. To this end, a four-step scientific and strategic solution approach is developed: (i) the identification and detailed explanation of 5 main and 24 sub-criteria, (ii) the weighting of criteria through the Analytical Hierarchical Process (AHP), Multi-Influencing Factor (MIF), and Best–Worst Method (BWM) in order to increase the sensitivity and robustness of the study, (iii) obtaining a suitability map for the solution of implementation, site selection, and service areas problems of operational models, and (iv) assigning shared e-scooter stations and analyzing their performance levels with COmplex PRoportional ASsessment (COPRAS). The results show that, in Erzurum, the central three districts are the most suitable for service areas. The paper’s solution methodology can help service providers and policymakers invest in sustainable shared e-scooter operational models, even in situations of high uncertainty. Full article
(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)
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20 pages, 4554 KiB  
Article
Solving Spatial Optimization Problems via Lagrangian Relaxation and Automatic Gradient Computation
by Zhen Lei and Ting L. Lei
ISPRS Int. J. Geo-Inf. 2025, 14(1), 15; https://doi.org/10.3390/ijgi14010015 - 2 Jan 2025
Viewed by 418
Abstract
Spatial optimization is an integral part of GIS and spatial analysis. It involves making various decisions in space, ranging from the location of public facilities to vehicle routing and political districting. While useful, such problems (especially large problem instances) are often difficult to [...] Read more.
Spatial optimization is an integral part of GIS and spatial analysis. It involves making various decisions in space, ranging from the location of public facilities to vehicle routing and political districting. While useful, such problems (especially large problem instances) are often difficult to solve using general mathematical programming (due to their generality). Traditionally, an alternative solution method is Lagrangian relaxation, which, if well-designed, can be fast and optimal. One has to derive the Lagrangian dual problem and its (sub)gradients, and move towards the optimal solution via a search process such as gradient descent. Despite its merits, Lagrangian relaxation as a solution algorithm requires one to derive the (sub)gradients manually, which is error-prone and makes the solution algorithm difficult to develop and highly dependent on the model at hand. This paper aims to ease the development of Lagrangian relaxation algorithms for GIS practitioners by employing the automatic (sub)gradient (autograd) computation capabilities originally developed in modern Deep Learning. Using the classic p-median problem as an example, we demonstrate how Lagrangian relaxation can be developed with paper and pencil, and how the (sub)gradient computation derivation can be automated using autograd. As such, the human expert only needs to implement the Lagrangian problem in a scientific computing language (such as Python), and the system can find the (sub)gradients of this code, even if it contains complex loops and conditional statements. We verify that the autograd version of the algorithm is equivalent to the original version with manually derived gradients. By automating the (sub)gradient computation, we significantly lower the cost of developing a Lagrangian algorithm for the p-median. And such automation can be applied to numerous other optimization problems. Full article
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19 pages, 290 KiB  
Review
Urban Vitality Measurement Through Big Data and Internet of Things Technologies
by Young-Long Kim
ISPRS Int. J. Geo-Inf. 2025, 14(1), 14; https://doi.org/10.3390/ijgi14010014 - 2 Jan 2025
Viewed by 667
Abstract
This paper examines the evolution of urban vitality measurement, emphasizing the transformative impact of big data and Internet of Things (IoT) technologies. Traditionally assessed through direct observations and surveys, urban vitality measurement has shifted with the advent of these technologies, enabling the collection [...] Read more.
This paper examines the evolution of urban vitality measurement, emphasizing the transformative impact of big data and Internet of Things (IoT) technologies. Traditionally assessed through direct observations and surveys, urban vitality measurement has shifted with the advent of these technologies, enabling the collection of vast amounts of urban data. This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. However, this shift also raises significant methodological and ethical concerns, particularly regarding privacy, reliability, and accuracy. The paper discusses the theoretical underpinnings of urban vitality, current technological advancements, and the challenges and future directions in urban studies. It highlights the need for an interdisciplinary approach to fully harness the potential of emerging technologies in developing livable, sustainable, and responsive cities. Full article
(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)
19 pages, 4203 KiB  
Article
Exploring Cartographic Differences in Web Map Applications: Evaluating Design, Scale, and Usability
by Jakub Zejdlik and Vit Vozenilek
ISPRS Int. J. Geo-Inf. 2025, 14(1), 9; https://doi.org/10.3390/ijgi14010009 - 31 Dec 2024
Viewed by 585
Abstract
Although there are many articles dealing with web map applications, they often focus on just one or a few applications. Several articles deal with the technical solution of the applications, but relatively few are focused on the cartographic aspects of these applications. This [...] Read more.
Although there are many articles dealing with web map applications, they often focus on just one or a few applications. Several articles deal with the technical solution of the applications, but relatively few are focused on the cartographic aspects of these applications. This article evaluates eight web mapping applications based on six cartographic aspects: map key, map scale, map layout, navigation elements, labels, and analytical tools. The objective is to identify differences in the presentation of geographic information and propose improvements for cartographic quality and user-friendliness. The methodology involved visual analysis at two scales. The comparison included applications such as Mapy.cz, OpenStreetMap, Google Maps, Bing Maps, HERE Maps, MapQuest, ViaMichelin, and Locus Map. The results revealed significant differences among the applications that may impact user orientation and experience. For instance, Google Maps does not display forest symbols on its default map, which can reduce clarity, whereas Mapy.cz offers the most comprehensive range of analytical tools. Advertisements in applications like MapQuest and ViaMichelin disrupt the user experience, and some applications lack essential functions, such as distance measurement. The paper identifies strengths and weaknesses in the cartographic design of these applications. Findings reveal that while each application possesses unique characteristics, they share common features. An interesting feature is the absence of cartographic symbols and labels of some elements in some applications. The study recommends the unification of cartographic principles and further user testing to optimize the layout and functionality of web mapping applications. Full article
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27 pages, 18443 KiB  
Article
Revealing Land-Use Dynamics on Thermal Environment of Riverine Cities Under Climate Variability Using Remote Sensing and Geospatial Techniques
by Nazia Iftakhar, Fakhrul Islam, Mohammad Izhar Hussain, Muhammad Nasar Ahmad, Jinwook Lee, Nazir Ur Rehman, Saleh Qaysi, Nassir Alarifi and Youssef M. Youssef
ISPRS Int. J. Geo-Inf. 2025, 14(1), 13; https://doi.org/10.3390/ijgi14010013 - 31 Dec 2024
Viewed by 771
Abstract
Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps [...] Read more.
Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps persist in integrating Landsat imagery (7 and 8), meteorological data, and Geographic Information System (GIS) tools to evaluate the thermal effects of specific LULC types, including cooling and warming transitions, and their influence on air temperature under variable precipitation patterns. This study investigates LST variations in Islamabad, Pakistan, from 2000 to 2020 using quantile classification at three intervals (2000, 2010, 2020). The thermal contributions of each LULC type across the LST-based temperature classes were analyzed using the Land Contribution Index (LCI). Finally, Warming and Cooling Transition (WCT) maps were generated by intersecting LST classes with 2000 as the baseline. Results indicated a rise in LST from 32.39 °C in 2000 to 45.63 °C in 2020. The negative LCI values revealed that vegetation and water bodies in lower temperature zones (Ltc_1 to Ltc_3) contributed to cooling effects, while positive LCI values in built-up and bare land areas in higher temperature zones (Ltc_5–Ltc_7) exhibited warming effects. The WCT map showed a general warming trend (cold-to-hot type) from 2000 to 2020, particularly in newly urbanized areas due to a 49.63% population increase, while cooling effects (hot-to-cold type) emerged in the newly developed agricultural lands with a 46.46% rise in vegetation. The mean annual air temperature gap with LST narrowed from 11.55 °C in 2000 to 2.28 °C in 2020, reflecting increased precipitation due to increasing yearly rainfall from 982.88 mm in 2000 to 1365.47 mm in 2020. This change also coincided with an expansion of water bodies from 2.82 km2 in 2000 to 6.35 km2 in 2020, impacting the local climate and hydrology. These findings highlight the importance of green spaces and water management to mitigate urban heat and improve ecological health. 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, 6259 KiB  
Article
Integration of Multi-Source Landslide Disaster Data Based on Flink Framework and APSO Load Balancing Task Scheduling
by Zongmin Wang, Huangtaojun Liang, Haibo Yang, Mengyu Li and Yingchun Cai
ISPRS Int. J. Geo-Inf. 2025, 14(1), 12; https://doi.org/10.3390/ijgi14010012 - 31 Dec 2024
Viewed by 476
Abstract
As monitoring technologies and data collection methodologies advance, landslide disaster data reflects attributes such as diverse sources, heterogeneity, substantial volumes, and stringent real-time requirements. To bolster the data support capabilities for the monitoring, prevention, and management of landslide disasters, the efficient integration of [...] Read more.
As monitoring technologies and data collection methodologies advance, landslide disaster data reflects attributes such as diverse sources, heterogeneity, substantial volumes, and stringent real-time requirements. To bolster the data support capabilities for the monitoring, prevention, and management of landslide disasters, the efficient integration of multi-source heterogeneous data is of paramount importance. The present study proposes an innovative approach to integrate multi-source landslide disaster data by combining the Flink-oriented framework with load balancing task scheduling based on an improved particle swarm optimization (APSO) algorithm. It utilizes Flink’s streaming processing capabilities to efficiently process and store multi-source landslide data. To tackle the issue of uneven cluster load distribution during the integration process, the APSO algorithm is proposed to facilitate cluster load balancing. The findings indicate the following: (1) The multi-source data integration method for landslide disaster based on Flink and APSO proposed in this article, combined with the structural characteristics of landslide disaster data, adopts different integration methods for data in different formats, which can effectively achieve the integration of multi-source landslide data. (2) A multi-source landslide data integration framework based on Flink has been established. Utilizing Kafka as a message queue, a real-time data pipeline was constructed, with Flink facilitating data processing and read/write operations for the database. This implementation achieves efficient integration of multi-source landslide data. (3) Compared to Flink’s default task scheduling strategy, the cluster load balancing strategy based on APSO demonstrated a reduction of approximately 4.7% in average task execution time and an improvement of approximately 5.4% in average system throughput during actual tests using landslide data sets. The research findings illustrate a significant improvement in the efficiency of data integration processing and system performance. 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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17 pages, 10554 KiB  
Article
Temporal-Spatial Traffic Flow Prediction Model Based on Prompt Learning
by Siteng Cai, Gang Liu, Jing He, Yulun Du, Zhichao Si and Yunhao Jiang
ISPRS Int. J. Geo-Inf. 2025, 14(1), 11; https://doi.org/10.3390/ijgi14010011 - 31 Dec 2024
Viewed by 598
Abstract
Traffic flow prediction is one of the most important and attractive topics in geographical information science (GIS), traffic management, and logistics. Traffic flows exhibit significant complexity and dynamics, requiring a thorough understanding of their spatiotemporal evolution patterns for accurate prediction and analysis. Existing [...] Read more.
Traffic flow prediction is one of the most important and attractive topics in geographical information science (GIS), traffic management, and logistics. Traffic flows exhibit significant complexity and dynamics, requiring a thorough understanding of their spatiotemporal evolution patterns for accurate prediction and analysis. Existing studies utilizing deep learning for traffic flow prediction often suffer from distribution shift issues, leading to poor generalization capabilities when dealing with data that has different spatiotemporal distributions. Based on this, we propose a traffic flow prediction model based on prompt learning, leveraging graph convolutional networks to focus on the spatiotemporal dependencies of traffic flows. The model utilizes spatiotemporal context learning capabilities to capture the periodic states of traffic flows, enhancing the extraction of spatiotemporal features by integrating spatiotemporal information. Experimental results show that the spatiotemporal traffic flow prediction model equipped with a spatiotemporal prompt learning module outperforms several mainstream benchmark models in terms of predictive performance. The model presents efficient learning performance that reaches optimal state in a short period of time, reduces the impact of distribution shifts, and can be adapted to spatiotemporal traffic flow data under varying spatiotemporal contexts. Full article
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