ISPRS International Journal of Geo-Information

29 pages, 6743 KiB

Open AccessArticle

Research on the Coordinated Development of “Node-Place” in Intercity Railway Station Areas: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area, China

by Shuaibing Zhang, Zhengdong Huang and Kaixu Zhao

ISPRS Int. J. Geo-Inf. 2025, 14(3), 121; https://doi.org/10.3390/ijgi14030121 - 6 Mar 2025

Abstract

Intercity railways are key transportation infrastructures in the interconnection of urban agglomerations. Their stations are usually distributed based on densely populated and economically active areas, and they also play roles as regional network nodes, intra-city nodes, and functional areas. However, the academic research [...] Read more.

Intercity railways are key transportation infrastructures in the interconnection of urban agglomerations. Their stations are usually distributed based on densely populated and economically active areas, and they also play roles as regional network nodes, intra-city nodes, and functional areas. However, the academic research on the spatial development of station areas is still very limited. In particular, there is no sufficient in-depth discussion about the coordinated development mechanism of the “regional node-place” and “urban node-place” of intercity railways. Based on the case study of Guangdong–Hong Kong–Macao Greater Bay Area in China (GBA), this paper provides an in-depth analysis of the regional node development level, urban node development level, station area development level, comprehensive station area development level, and coordinated development of “regional node-place” and “urban node-place” in the GBA in 2012, 2016, 2020, and 2023 by constructing a node-place model, development index of regional nodes, development level index, and coupling coordination degree model. Findings: (1) From 2012 to 2023, the development of regional nodes, urban nodes, and places of the GBA intercity railway saw a significant improvement, with the proportion of high-value stations increasing by 13.3%, 7%, and 8.8%, respectively. Despite some improvement on the whole, the three still exhibited an unbalanced spatial distribution of “high in the middle-low in the periphery”; (2) The relative gap in development levels between “regional node-place” and “urban node-place” of intercity railways decreased by 0.159 and 0.168, respectively, showing an overall upward trend, but still showing an unbalanced spatial distribution of “high in the middle-low in the periphery”; (3) The development level of regional nodes and urban nodes is lower than that of areas and is dominated by the unbalance place and dependence types, while the unbalance node and balance types account for less; (4) The coordination of the “regional node-place” and “urban node-place” of intercity railways is gradually improved, and the stations with high coordination and high coordination levels accounts for an increased proportion from 4% to 7% and 8%, respectively. However, the coordination remains at a low level on the whole, with most sites still in the low-level coupling and lower-level coupling stages. Some stations in Guangzhou, Shenzhen, Foshan, and Dongguan have witnessed a level leap and are showing a transition towards a medium to high level of coordinated development, with the surrounding areas moving away from low-level coupling and coordination. Full article

17 pages, 2218 KiB

Open AccessArticle

Application of GIS Technologies in Tourism Planning and Sustainable Development: A Case Study of Gelnica

by Marieta Šoltésová, Barbora Iannaccone, Ľubomír Štrba and Csaba Sidor

ISPRS Int. J. Geo-Inf. 2025, 14(3), 120; https://doi.org/10.3390/ijgi14030120 - 6 Mar 2025

Abstract

This study examines the application of Geographic Information Systems (GIS) in tourism planning and sustainable destination management, using Gelnica, Slovakia, as a case study. The research highlights a key challenge—the absence of systematic visitor data collection—which hinders tourism market analysis, demand assessment, and [...] Read more.

This study examines the application of Geographic Information Systems (GIS) in tourism planning and sustainable destination management, using Gelnica, Slovakia, as a case study. The research highlights a key challenge—the absence of systematic visitor data collection—which hinders tourism market analysis, demand assessment, and strategic decision-making. The study integrates alternative data sources, including the Google Places API, to address this gap to analyse Points of Interest (POIs) based on user-generated reviews, ratings, and spatial attributes. The methodological framework combines data acquisition, spatial analysis, and GIS-based visualisation, employing thematic and heat maps to assess tourism resources and visitor behaviour. The findings reveal critical spatial patterns and tourism dynamics, identifying high-demand zones and underutilised locations. Results underscore the potential of GIS to optimise tourism infrastructure, enhance visitor management, and inform evidence-based decision-making. This study advocates for systematically integrating GIS technologies with visitor monitoring and digital tools to improve destination competitiveness and sustainability. The proposed GIS-driven approach offers a scalable and transferable model for data-informed tourism planning in similar historic and environmentally sensitive regions. Full article

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17 pages, 9938 KiB

Open AccessArticle

Study on Spatially Nonstationary Impact on Catering Distribution: A Multiscale Geographically Weighted Regression Analysis Using POI Data

by Lu Tan and Xiaojun Bu

ISPRS Int. J. Geo-Inf. 2025, 14(3), 119; https://doi.org/10.3390/ijgi14030119 - 6 Mar 2025

Abstract

Factors related to catering distribution are typically characterized by local changes, but few studies have quantitatively investigated the inherent spatial nonstationarity correlations. In this study, a multiscale geographically weighted regression (MGWR) model was adopted to locally examine the impact of various factors on [...] Read more.

Factors related to catering distribution are typically characterized by local changes, but few studies have quantitatively investigated the inherent spatial nonstationarity correlations. In this study, a multiscale geographically weighted regression (MGWR) model was adopted to locally examine the impact of various factors on catering distribution, which were obtained through a novel method incorporating GeoDetector analysis and exploratory factor analysis (EFA) using point of interest (POI) data. GeoDetector analysis was used to identify the effective variables that truly contribute to catering distribution, and EFA was adopted to extract interpretable latent factors based on the underlying structure of the effective variables and thus eliminate multicollinearity. In our case study in Nanjing, China, four primary factors, namely commuting activities, shopping activities, tourism activities, and gathering activities, were retained from eight categories of POIs with respect to catering distribution. The results suggested that GeoDetector working in tandem with EFA could improve the representativeness of factors and infer POI configuration patterns. The MGWR model explained the most variations (adj. R²: 0.903) with the lowest AICc compared to the OLS regression model and the geographically weighted regression (GWR) model. Mapping MGWR parameter estimates revealed the spatial variability of relationships between various factors and catering distribution. The findings provide useful insights for guiding catering development and optimizing urban functional spaces. Full article

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24 pages, 9987 KiB

Open AccessArticle

Ecological Zoning Based on Suitability Evaluation of Ecological Product Development from the Value-Risk-Cost-Demand Perspective

by Ming Gao, Pei Du, Xinxin Zhou, Zhenxia Liu, Wen Luo, Zhaoyuan Yu and Linwang Yuan

ISPRS Int. J. Geo-Inf. 2025, 14(3), 118; https://doi.org/10.3390/ijgi14030118 - 6 Mar 2025

Abstract

Current ecological value assessment models predominantly emphasize the potential value of ecological resources, neglecting the crucial aspect of value realization processes. Analyzing the value of ecological resources from the perspective of ecological products (EPs) is more instructive in realizing ecological values. The key [...] Read more.

Current ecological value assessment models predominantly emphasize the potential value of ecological resources, neglecting the crucial aspect of value realization processes. Analyzing the value of ecological resources from the perspective of ecological products (EPs) is more instructive in realizing ecological values. The key factors controlling the realization of ecological product value are potential value, ecological risk, development costs, and human demand. Previous research has rarely integrated these four factors within the ecological zoning framework. This study proposes a suitability evaluation and zoning framework for ecological product development based on the “value-risk-cost-demand” perspective. First, an evaluation index system for the potential value of ecological products was developed, dividing EPs into ecological agriculture (EA), ecological industry (EI), and ecological tourism (ET), and assessing them using 13 indicators. Ecological risks were modeled using multi-scale patch analysis (MSPA) and other models. Development costs were estimated using cost entropy. The impact of population dynamics on EP demand was quantified using population density, night-time light data, and average land GDP, along with stacked buffer analysis. Next, an improved TOPSIS method was applied to integrate these four dimensions, producing a comprehensive suitability assessment for EP development. Finally, EP zoning was determined by overlaying the comprehensive evaluation results. This framework was used to identify the dominant mode zones of EPs within the region of Jintan District, Jiangsu Province, China. The findings suggest that the integrated assessment model proposed in this study has produced more reasonable outcomes in terms of spatial layout, land use area, reduction of fragmentation and ecological risk. This conclusion is supported by spatial distribution comparisons, optimal area deviation analyses, landscape index calculations and multi-model driven future simulations. This model effectively resolves the spatial mismatch present in the traditional approach, which solely focuses on the potential value of EPs. This study can be applied to other regions with developed economies and rich ecological resources, providing an effective reference for the choice of paths to realize the value of EPs. Full article

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20 pages, 6355 KiB

Open AccessArticle

How Did the Fever Visit Management Policy During the COVID-19 Epidemic Impact Fever Medical Care Accessibility?

by Zhiyuan Zhao, Youjun Tu and Yicheng Ding

ISPRS Int. J. Geo-Inf. 2025, 14(3), 117; https://doi.org/10.3390/ijgi14030117 - 6 Mar 2025

Abstract

Fever visit management (FVM) played a critical role in reducing the risk of local outbreaks caused by positive cases during the coronavirus disease 2019 (COVID-19) pandemic under the dynamic zero-COVID-19 policy. Fever clinics were established to satisfy the healthcare needs of citizens with [...] Read more.

Fever visit management (FVM) played a critical role in reducing the risk of local outbreaks caused by positive cases during the coronavirus disease 2019 (COVID-19) pandemic under the dynamic zero-COVID-19 policy. Fever clinics were established to satisfy the healthcare needs of citizens with fever symptoms, including those with and without COVID-19. Learning how FVM affects fever medical care accessibility for citizens in different places can support decision making in establishing fever clinics more equitably. However, the dynamic nature of the population at different times has rarely been considered in evaluating healthcare facility accessibility. To fill this gap, we adjusted the Gaussian-based two-step floating catchment area method (G2SFCA) by considering the hourly dynamics of the population distribution derived from mobile phone location data. The results generated from Xining city, China, showed that (1) the accessibility of fever clinics explicitly exhibited spatial distribution patterns, being high in the center and low in surrounding areas; (2) the accessibility reduction in suburban areas caused by FVM was approximately 2.8 times greater than that in the central city for the 15 min drive conditions; and (3) the accessibility of fever clinics based on the nighttime anchor point was overestimated in central areas, but underestimated in suburban areas. Full article

(This article belongs to the Topic The Use of Big Data in Public Health Research and Practice)

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17 pages, 1788 KiB

Open AccessArticle

The Effects of Employment Center Characteristics on Commuting Time: A Case Study of the Seoul Metropolitan Area

by Sangyeon Nam and Sungjo Hong

ISPRS Int. J. Geo-Inf. 2025, 14(3), 116; https://doi.org/10.3390/ijgi14030116 - 5 Mar 2025

Abstract

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics [...] Read more.

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income workers, which likely contributed to shorter commutes. On the other hand, the positive relationship between housing prices and commuting times highlights the need for affordable housing near employment centers to reduce commuting times. These findings underscore the complex interactions between each employment center’s characteristics and workers’ socioeconomic factors in shaping commuting behavior. Full article

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28 pages, 4720 KiB

Open AccessArticle

Developing a Methodology for Assessing Visual and Environmental Sensitivity for Agrivoltaics Land Suitability Projects: The Case Study of Viterbo Province (Italy)

by Ilaria Angelelli, Daniele Codato, Salvatore Eugenio Pappalardo and Massimo De Marchi

ISPRS Int. J. Geo-Inf. 2025, 14(3), 115; https://doi.org/10.3390/ijgi14030115 - 5 Mar 2025

Abstract

The transition to renewable energy is crucial for combating climate change but faces challenges like local socio-environmental impacts and territorial conflicts. Scientific research on mapping renewable energy suitability areas and identifying socio-culturally and environmentally sensitive zones is essential to guide project siting appropriately. [...] Read more.

The transition to renewable energy is crucial for combating climate change but faces challenges like local socio-environmental impacts and territorial conflicts. Scientific research on mapping renewable energy suitability areas and identifying socio-culturally and environmentally sensitive zones is essential to guide project siting appropriately. This study proposes a replicable methodology to analyze scenarios and compare alternatives for agrivoltaics plant siting, using the province of Viterbo, Italy, as a case study. The methodology employs spatial data, thematic maps, and multi-criteria analysis in open-source GIS software to identify suitable solar belts, map environmental sensitivity through 14 criteria, and assess visual sensitivity based on proximity to landscape elements. The resulting workflow and customizable QGIS models provide a comprehensive, transparent decision-support tool to optimize agrivoltaics deployment while minimizing impacts and enhancing acceptance. Mapping multi-factor sensitivity offers crucial insights for sustainable planning and design. The Viterbo case study illustrates the ‘conflict between green alternatives’ where renewable energy development potential clashes with environmental and landscape protection needs. The analysis reveals significant spatial variability in suitability and sensitivity among the province’s municipalities. The study highlights the importance of a nuanced approach to assessing suitability, moving beyond simple binary classification, and provides a tool adaptable to different regulations and contexts. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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20 pages, 16826 KiB

Open AccessArticle

Leveraging a Cooler, Healthier, and Decarbonized School Commute: City-Scale Estimation and Implications for Nanjing, China

by Lifei Wang, Ziqun Lin, Zhen Xu and Lingyun Han

ISPRS Int. J. Geo-Inf. 2025, 14(3), 114; https://doi.org/10.3390/ijgi14030114 - 5 Mar 2025

Abstract

An important aspect of a well-designed urban form is supporting active school travel by adolescents, as it has positive effects on physical activity, healthy lifestyles, and reducing vehicle-related carbon emissions. To achieve this, it is necessary to provide sufficient shading and fewer detours [...] Read more.

An important aspect of a well-designed urban form is supporting active school travel by adolescents, as it has positive effects on physical activity, healthy lifestyles, and reducing vehicle-related carbon emissions. To achieve this, it is necessary to provide sufficient shading and fewer detours on home–school routes, especially in an era of frequent heatwaves. Analyzing the school travel environment at the city scale is essential for identifying practical solutions and informing comprehensive urban policy-making. This study proposes a framework for investigating, assessing, and intervening in home–school routes in Nanjing, China, emphasizing a dual assessment of commuting routes based on the pedestrian detour ratio and shading ratio. This work reveals that approximately 34% of middle school households in Nanjing face challenges in walking to and from school, with only 24.18% of walking routes offering fewer detours and sufficient shade. We advocate reengineering urban forms by reducing barriers to facilitate shortcuts, thereby providing school-age students with better access to cooler and healthier environments, aiming to promote walking and reduce car dependence. The findings may encourage more families to engage in active commuting and serve as a lever to drive school decarbonization and combat climate warming. Our work, with transferability to other cities, can assist urban designers in piloting urban (re)form incrementally and pragmatically to promote sustainable urban agendas. Full article

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

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22 pages, 7377 KiB

Open AccessArticle

Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method

by Cheng Zhang, Yiwen Wang, Haozhe Cheng and Wanfeng Dou

ISPRS Int. J. Geo-Inf. 2025, 14(3), 113; https://doi.org/10.3390/ijgi14030113 - 4 Mar 2025

Abstract

With the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and inherent meanings between geographical entities, [...] Read more.

With the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and inherent meanings between geographical entities, including topological relationships, metric relationships, etc. This study proposes a novel method of viewshed analysis, which solves the limitation of treating the viewshed as a unified unit in traditional viewshed analysis by decomposing the viewshed into multiple viewsheds and quantifying their spatial semantic relationships. The method uses a DBSCAN clustering algorithm with terrain adaptability to divide a viewshed into spatially different viewsheds and characterizes these viewsheds through a systematic measurement framework, including azimuth, area, and sparsity. The method was applied to a case study of Purple Mountain in Nanjing. The experiment used 12.5 m accuracy topographic data from Purple Mountain, and two observation points were selected. For the first observation point near the mountain park, during the DBSCAN clustering partition of the viewshed, the number of clusters and the number of noise points were compared with determine the neighborhood radius of 18 m and the minimum sample point number of 4. Five viewsheds were successfully generated, with the largest viewshed having 468 visible points and the smallest only 16, located in different locations from the observer, reflecting the spatial variability of terrain features. All viewsheds are basically distributed to the north of the observer, two of which also share the northeast 87° direction with the observer in a straight line distribution but at different distances. In three-dimensional space, the distance between the two viewsheds is 317.298 m. Azimuth angle verification showed significant aggregation in the northeast direction. The second point is near the ridgeline, where one viewshed accounts for 87.52% of the total viewshed, showing significant visual effects. One viewshed is 3121.113 m away from the observer, with only 113 visible points, and is not located at a low altitude, so it is suitable for a long-distance fixed-point intermittent observation. The experimental results of the two observation points reveal the directional dominance and distance stratification of viewshed spatial relationships. This paper proposes a model to express topographical viewshed spatial relationships. The model analyzes and describes the spatial features of the viewshed through quantitative and qualitative methods. These metric features provide a basis for constructing spatial topological relationships between observation points and viewsheds, helping optimize viewpoint selection and enhance landscape planning. Compared with traditional methods, the proposed method significantly improves the resolution of spatial semantic relationship expression and has practical application value in fields such as archaeology, tourism planning, and urban design. Full article

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20 pages, 2640 KiB

Open AccessArticle

Geospatial Analytics of Urban Bus Network Evolution Based on Multi-Source Spatiotemporal Data Fusion: A Case Study of Beijing, China

by Xiao Li, Shaohua Wang, Liang Zhou, Yeran Sun, Jiayi Zheng, Chang Liu, Junyuan Zhou, Cheng Su and Dachuan Xu

ISPRS Int. J. Geo-Inf. 2025, 14(3), 112; https://doi.org/10.3390/ijgi14030112 - 4 Mar 2025

Abstract

Bus networks are a crucial support for urban commuting. By studying the evolutionary characteristics of bus networks, we can uncover their development patterns, coverage efficiency, and changes in regional balance, providing a scientific basis for sustainable urban development and the optimization of transportation [...] Read more.

Bus networks are a crucial support for urban commuting. By studying the evolutionary characteristics of bus networks, we can uncover their development patterns, coverage efficiency, and changes in regional balance, providing a scientific basis for sustainable urban development and the optimization of transportation resources. This study systematically analyzes the spatiotemporal evolution characteristics of the bus network in Beijing from 2006 to 2024 using specific spatial analysis tools to analyze spatiotemporal evolution characteristics. By analyzing spatial coverage rates of transit stations using road network and administrative division data, the study reveals the convenience of bus networks in different regions. By combining the research methodology of the Sustainable Development Goals (SDGs) report, a 500-m service radius for bus stops was assessed. A complex network model was used to extract the nodes and edges of the bus network, and the betweenness centrality (BC) characteristics were analyzed. The findings indicate that Beijing’s bus network has gradually expanded from the central urban areas to peripheral regions, with notable expansion in Tongzhou and Yanqing, resulting in an improved balance in the distribution of stations and routes and the emergence of Tongzhou as a new bus hub. The diffusion characteristics of the bus network are significantly influenced by administrative boundaries and the layout of the ring roads. Bus routes and stops are highly concentrated in the central urban areas and within the Second Ring Road, while as the number of ring roads increases, various network indices gradually decrease. The distribution of bus stops shows notable clustering and an uneven directional development. Beijing’s bus stop distribution exhibits significant clustering characteristics, and the areas with a high Population Conveniently Served by Buses (PCSB) are predominantly concentrated in the central urban areas, with a large gap compared to the outer suburban districts. These conclusions expand on the exploration of isolated and static characteristics of the bus network structure, revealing the dynamic mechanisms and evolution patterns of Beijing’s bus network. They provide guidance and recommendations for improving the bus network and offer more comprehensive support for urban planning and resource allocation. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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23 pages, 5183 KiB

Open AccessArticle

Solving the Traveling Salesman Problem Using the IDINFO Algorithm

by Yichun Su, Yunbo Ran, Zhao Yan, Yunfei Zhang and Xue Yang

ISPRS Int. J. Geo-Inf. 2025, 14(3), 111; https://doi.org/10.3390/ijgi14030111 - 3 Mar 2025

Abstract

The Traveling Salesman Problem (TSP) is a classical discrete combinatorial optimization problem that is widely applied in various domains, including robotics, transportation, networking, etc. Although existing studies have provided extensive discussions of the TSP, the issues of improving convergence and optimization capability are [...] Read more.

The Traveling Salesman Problem (TSP) is a classical discrete combinatorial optimization problem that is widely applied in various domains, including robotics, transportation, networking, etc. Although existing studies have provided extensive discussions of the TSP, the issues of improving convergence and optimization capability are still open. In this study, we aim to address this issue by proposing a new algorithm named IDINFO (Improved version of the discretized INFO). The proposed IDINFO is an extension of the INFO (weighted mean of vectors) algorithm in discrete space with optimized searching strategies. It applies the multi-strategy search and a threshold-based 2-opt and 3-opt local search to improve the local searching ability and avoid the issue of local optima of the discretized INFO. We use the TSPLIB library to estimate the performance of the IDINFO for the TSP. Our algorithm outperforms the existing representative algorithms (e.g., PSM, GWO, DSMO, DJAYA, AGA, CNO_PSO, Neural-3-OPT, and LIH) when tested against multiple benchmark sets. Its effectiveness was also verified in the real world in solving the TSP in short-distance delivery. Full article

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24 pages, 10312 KiB

Open AccessArticle

Spatial Network in SQL Databases for Real-Time Multimodal Emergency Routing in Wildland Fires

by Mateusz Ilba

ISPRS Int. J. Geo-Inf. 2025, 14(3), 110; https://doi.org/10.3390/ijgi14030110 - 2 Mar 2025

Abstract

Evacuation routing in wildland areas is an important aspect during various emergencies, including fire incidents. A review of the literature found a lack of research on vector routing systems for evacuations from wildland areas. This article aims to address the issue of determining [...] Read more.

Evacuation routing in wildland areas is an important aspect during various emergencies, including fire incidents. A review of the literature found a lack of research on vector routing systems for evacuations from wildland areas. This article aims to address the issue of determining evacuation routes using vector object database technology with various optimization methods. To this end, the author developed a novel algorithm for network creation and optimization through heuristic data aggregation. Case studies were conducted in a wooded area of the Bieszczady Mountains, where the potential of determining evacuation routes in the proprietary geodatabase (SQLite SpatiaLite) was examined, and the results were compared with traditional methods based on raster least-cost path analyses. The analyses confirmed the feasibility of creating a network of connections in the database within an area of 3.74 km² with undefined roads. Through the implementation of optimizations, the determination of evacuation routes in wildland areas was reduced to less than 1 s. Additionally, the possibility of the system operating for areas covering 40 km² was presented. The use of optimized vector data and database technology enabled the development of a comprehensive forest area management system, encompassing points of rescue units situated at significant distances from the area. This facilitated the establishment of flexible evacuation routes or rescue missions, particularly allowing for the establishment of multimodal routes using different means of transportation to reach the destination. Full article

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

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25 pages, 24262 KiB

Open AccessArticle

Dynamic Load Balancing Based on Hypergraph Partitioning for Parallel Geospatial Cellular Automata Models

by Wei Xia, Qingfeng Guan, Yuanyuan Li, Hanqiu Yue, Xue Yang and Huan Gao

ISPRS Int. J. Geo-Inf. 2025, 14(3), 109; https://doi.org/10.3390/ijgi14030109 - 1 Mar 2025

Abstract

Parallel computing techniques have been adopted in geospatial cellular automata (CA) models to improve computational efficiency, enabling large-scale complex simulations of land use and land cover (LULC) changes at fine scales. However, the spatial distribution of computational intensity often changes along with the [...] Read more.

Parallel computing techniques have been adopted in geospatial cellular automata (CA) models to improve computational efficiency, enabling large-scale complex simulations of land use and land cover (LULC) changes at fine scales. However, the spatial distribution of computational intensity often changes along with the spatiotemporal dynamics of LULC during the simulation, leading to an increase in load imbalance among computing units and degradation of the computational performance of a parallel CA. This paper presents a dynamic load balancing method based on hypergraph partitioning for multi-process parallel geospatial CA models. During the simulation, the sub-domains are dynamically reassigned to computing processes through hypergraph partitioning according to the spatial variation in computational workloads to restore load balance. In addition, a novel mechanism called Migrated-SubCellspaces-First (MSCF) is proposed to reduce the cost of workload migration by employing a non-blocking communication technique to further improve computational performance. To demonstrate and evaluate the effectiveness of our method, a parallel geospatial CA model with hypergraph-based dynamic load balancing is developed. Experiments using a dataset from California showed that the proposed dynamic load balancing method achieved a computational performance enhancement of 62.59% by using 16 processes compared with a parallel CA with static load balancing. Full article

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22 pages, 12032 KiB

Open AccessArticle

Urban Flood Resilience Assessment of Prefecture-Level Cities in Yangtze River Delta

by Mingru Zhou, Qisheng He, Yuhan Gu, Ke Wang and Zhihao Shen

ISPRS Int. J. Geo-Inf. 2025, 14(3), 108; https://doi.org/10.3390/ijgi14030108 - 1 Mar 2025

Abstract

The frequent occurrence of flooding disasters threatens urban public safety and sustainable development, making enhancing urban ecological resilience crucial for flood prevention and disaster reduction. This study, focusing on the Yangtze River Delta urban agglomeration (YRD) in China, constructs an evaluation framework based [...] Read more.

The frequent occurrence of flooding disasters threatens urban public safety and sustainable development, making enhancing urban ecological resilience crucial for flood prevention and disaster reduction. This study, focusing on the Yangtze River Delta urban agglomeration (YRD) in China, constructs an evaluation framework based on three subsystems: the hazard, disaster-formative environment, and exposure. Using the entropy weight method, Geographic Information Systems (GIS), along with spatial autocorrelation analysis, the spatial distribution and trend of resilience indices are obtained. Based on stepwise regression analysis, the factors influencing the resilience distribution are discussed. The results show an overall increase in resilience levels in the YRD urban agglomeration, reflecting improvements in the urban emergency response and recovery capabilities. However, significant differences exist between cities, with a trend of decreasing resilience from first-tier cities to surrounding areas. Among these, indicators such as per capita disposable income and the number of people covered by social insurance have a significant positive impact on resilience clustering, highlighting the key role of socioeconomic vitality in urban resilience. This study is of great significance for differentiated and scientific flood disaster management in urban agglomerations. Full article

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20 pages, 17194 KiB

Open AccessArticle

Understanding the Carbon Footprint of Tile Transfer for Web Maps

by Guillaume Touya, Azelle Courtial, Jérémy Kalsron, Justin Berli, Bérénice Le Mao and Laura Wenclik

ISPRS Int. J. Geo-Inf. 2025, 14(3), 107; https://doi.org/10.3390/ijgi14030107 - 1 Mar 2025

Abstract

As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first [...] Read more.

As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first need to better understand the different sources of energy consumption for web maps. Among these sources, this paper focuses on the tiles that are stored on servers and then constantly transferred each time a user explores the map. This paper presents several experiments carried out with current web maps to assess this energy consumption. We first try to assess the number of map tiles that are loaded through the web when users explore web maps, and we determine which types of interaction are used with the maps, and a similar amount of tiles is loaded. Then, we try to assess which zoom levels are the most loaded by users; it appears that the medium–large scales are the most used (between zoom levels 11 and 17). Then, we explore the size of the map tiles and try to assess which ones are larger and thus require more energy to load over the web; we can find clear differences between zoom levels. Finally, we discuss how map generalization could be used to reduce energy consumption by creating lighter tiles. These experiments show that the current web maps are suboptimal regarding energy consumption, with many tiles loaded at zoom levels where the tiles are larger than necessary. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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30 pages, 9877 KiB

Open AccessArticle

A Methodological Framework for Assessing Overtourism in Insular Territories—Case Study of Santorini Island, Greece

by Akrivi Leka, Apostolos Lagarias, Anastasia Stratigea and Panayiotis Prekas

ISPRS Int. J. Geo-Inf. 2025, 14(3), 106; https://doi.org/10.3390/ijgi14030106 - 1 Mar 2025

Abstract

This paper aims at addressing sustainability concerns in vulnerable insular territories. Such concerns are due to the rising overtourism phenomenon that affects islands at a rapidly escalating pace and renders sustainable local development of these outstanding areas—from a natural and cultural viewpoint—at stake. [...] Read more.

This paper aims at addressing sustainability concerns in vulnerable insular territories. Such concerns are due to the rising overtourism phenomenon that affects islands at a rapidly escalating pace and renders sustainable local development of these outstanding areas—from a natural and cultural viewpoint—at stake. Towards this end, this work capitalizes on current literature and attempts to structure a methodological framework and a respective set of indicators’ groups that are capable of assessing dimensions of overtourism in each single tourism destination, thus providing evidence-based and more robust guidelines for articulating policy decisions that can remedy incidents of overtourism. The proposed methodological framework follows a place-based approach and combines tourism demand and supply data with environmental, social, economic and spatial data and respective indicators for assessing the tourism density and intensity of each destination’s tourism pattern and related multi-dimensional impacts. Validation of both the proposed framework and indicators’ groups is conducted in Santorini Island, Greece, i.e., an island that lies at top positions of many lists of destinations, marked as suffering by overtourism. Results show that Santorini Island is confronted with severe overtourism impacts, which are highly affecting its identity, productive model and spatial pattern, while endangering its natural and cultural wealth. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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27 pages, 11341 KiB

Open AccessArticle

Spatial Prediction of High-Risk Areas for Asthma in Metropolitan Areas: A Machine Learning Approach Applied to Tehran, Iran

by Alireza Mohammadi, Elahe Pishgar and Juan Aguilera

ISPRS Int. J. Geo-Inf. 2025, 14(3), 105; https://doi.org/10.3390/ijgi14030105 - 1 Mar 2025

Abstract

Asthma prevalence in large urban areas of developing countries is a significant public health concern, with increased rates driven by various socioeconomic and environmental factors. This study aims to predict asthma risk in Tehran, a major urban center in Iran. Data from 1473 [...] Read more.

Asthma prevalence in large urban areas of developing countries is a significant public health concern, with increased rates driven by various socioeconomic and environmental factors. This study aims to predict asthma risk in Tehran, a major urban center in Iran. Data from 1473 asthma patients, alongside demographic, socioeconomic, air quality, environmental, weather, and healthcare access variables, were analyzed using geographic information systems (GIS) and remote sensing techniques. Three ensemble machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were applied to model and predict asthma risk. A Negative Binomial Regression Model (NBRM) identified seven key predictors: population density, unemployment rate, particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), neighborhood deprivation index, and road intersection density. Among the algorithms, GBM outperformed the others, with a training RMSE of 0.56 and a test RMSE of 1.07, demonstrating strong generalization. Additionally, GBM achieved the highest R-squared values (0.95 for training and 0.76 for testing) and lower MAE values (0.43 for training and 0.88 for testing). Effective pattern recognition was confirmed by EV values of 0.95 for training and 0.75 for testing, along with a Moran’s I value of 0.17, indicating minimal spatial autocorrelation. Full article

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

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16 pages, 6724 KiB

Open AccessArticle

New Quality Productivity of Agriculture and Rural Areas at the Provincial Scale in China: Indicator Construction and Spatiotemporal Evolution

by Xiangyang Cao, Jiahui Lei, Donghui Shi, Wenlong Yu, Tianhui Tao, Xun Zhang and An Wang

ISPRS Int. J. Geo-Inf. 2025, 14(3), 104; https://doi.org/10.3390/ijgi14030104 - 27 Feb 2025

Abstract

New quality productivity in agriculture and rural areas serves as a critical foundation for addressing the development needs of the times, advancing the comprehensive revitalization of rural regions, and overcoming the urban–rural dual structure. This paper studies the spatial changes and evolutionary trends [...] Read more.

New quality productivity in agriculture and rural areas serves as a critical foundation for addressing the development needs of the times, advancing the comprehensive revitalization of rural regions, and overcoming the urban–rural dual structure. This paper studies the spatial changes and evolutionary trends of new quality productivity in agriculture and rural areas in 31 provinces of mainland China. The main conclusions are as follows: (1) At the provincial level, the development of China’s new quality productivity exhibits a spatial gradient, with a decreasing trend from east to west. (2) At the national scale, while significant spatial autocorrelation exists in the new quality productivity of agriculture and rural areas, internal disparities are gradually narrowing. (3) The eastern region demonstrates significant advantages, the central region is making steady progress, the western region is rapidly catching up, and the northeastern region faces increasingly significant development pressures. This paper extends the research boundary of new quality productivity to the field of agriculture and rural areas, and we introduce a variety of spatial analysis methods to depict its distribution characteristics. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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22 pages, 39141 KiB

Open AccessArticle

A Progressive Clustering Approach for Buildings Using MST and SOM with Feature Factors

by Tianliang Zhang, Xiaoji Lan and Jianhua Feng

ISPRS Int. J. Geo-Inf. 2025, 14(3), 103; https://doi.org/10.3390/ijgi14030103 - 25 Feb 2025

Abstract

To address the challenges in current research on spatial clustering algorithms for buildings in topographic maps—namely, their limited ability to effectively accommodate diverse application scenarios, including dense and regular urban environments, sparsely and irregularly distributed rural areas, and urban villages with complex structures—this [...] Read more.

To address the challenges in current research on spatial clustering algorithms for buildings in topographic maps—namely, their limited ability to effectively accommodate diverse application scenarios, including dense and regular urban environments, sparsely and irregularly distributed rural areas, and urban villages with complex structures—this paper introduces an innovative progressive clustering algorithm framework. The proposed framework operates in a hierarchical manner, progressing from macro to micro levels, thereby enhancing its adaptability and practical versatility. Specifically, it employs the minimum spanning tree (MST) technique for macro-level clustering analysis. Subsequently, a self-organizing map (SOM) neural network is utilized to perform micro-level clustering, enabling a more refined and detailed classification. Within this framework, the minimum spanning tree effectively captures the macroscopic distribution patterns of the building population. The macroscopic clustering results are then utilized as the initial weight configurations for the SOM neural network. This approach ensures that the overall spatial structural integrity is preserved during the subsequent micro-level clustering process. Moreover, the SOM neural network achieves refined optimization of micro-clustering details by incorporating building feature factors. To validate the effectiveness of the proposed algorithm, this study conducts an empirical analysis and comparative testing using building data from Futian District, Shenzhen City. The results indicate that the proposed algorithm exhibits superior recognition capabilities when applied to complex and variable spatial distribution patterns of buildings. Furthermore, the clustering outcomes align closely with the principles of Gestalt visual perception and outperform the comparison algorithms in overall performance. Full article

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