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ISPRS Int. J. Geo-Inf., Volume 13, Issue 11 (November 2024) – 46 articles

Cover Story (view full-size image): Discrete Global Grid Systems (DGGSs) have emerged as a promising approach to enhance modern GIS capabilities. Current DGGSs use low-res simple spherical meshes for efficient operations but require projection between the Earth’s surface and mesh faces, causing unavoidable distortion. High-res spherical meshes offer an opportunity to reduce distortion, albeit with more expensive operations. This paper introduces a novel mesh-based DGGS (MBD) that generalizes efficient operations over spherical meshes. MBD allows high-res meshes to be used as the base polyhedron of a DGGS, notably reducing distortion. To address operational efficiency, this paper introduces novel methods for efficient spatial and hierarchical traversal. Several new base meshes with lower distortion are presented, achieving constant-time operations for high-res mesh-based DGGSs. View this paper
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23 pages, 13091 KiB  
Article
Spatial Equity Disparities of Work Commuting Based on Job Accessibility in Chengdu, China
by Zhuoyu Wang, Tao Wang, Linlin Zang, Li Wang and Yi Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 417; https://doi.org/10.3390/ijgi13110417 - 20 Nov 2024
Viewed by 511
Abstract
Recently, urban spatial equity has become a research hotspot, but research focuses on the equity of work commuting from different dimensions. This paper aims to determine the fairness difference of work commuting in Chengdu from three different dimensions by analyzing job accessibility in [...] Read more.
Recently, urban spatial equity has become a research hotspot, but research focuses on the equity of work commuting from different dimensions. This paper aims to determine the fairness difference of work commuting in Chengdu from three different dimensions by analyzing job accessibility in Chengdu. Firstly, population residence and employment data are obtained by using mobile phone signaling data, real-time travel data are obtained by using Amap API, and regional housing information is obtained from a real estate website. Secondly, the differences in time and cost of job accessibility in different regions are calculated under different time thresholds. Finally, the equity of job accessibility is evaluated by using the Theil index and the Gini coefficient from three new perspectives: transport mode, house price economy, and spatial region. The experimental results show that (1) when time threshold increases, public transport in Chengdu is more equitable, while car traffic is opposite; (2) regions with higher prices are generally fairer; and (3) Chengdu’s equality disparities are more between areas than within areas. In addition to proposing a new accessibility formula based on travel impedance, this study suggests a new method for analyzing equity differences in Chinese cities that can serve as a reference for future researchers. At the same time, the results provide a scientific basis for optimizing the social spatial distribution of public transport services in Chengdu. Full article
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24 pages, 27951 KiB  
Article
A Geographic Information System-Based Model and Analytic Hierarchy Process for Wind Farm Site Selection in the Red Sea
by Lamya Albraheem and Fahad Almutlaq
ISPRS Int. J. Geo-Inf. 2024, 13(11), 416; https://doi.org/10.3390/ijgi13110416 - 20 Nov 2024
Viewed by 992
Abstract
The wind is one of the most important sources of renewable energy. However, it is associated with many challenges, with one of the most notable being determining suitable locations for wind power farms based on different evaluation criteria. In this study, we investigated [...] Read more.
The wind is one of the most important sources of renewable energy. However, it is associated with many challenges, with one of the most notable being determining suitable locations for wind power farms based on different evaluation criteria. In this study, we investigated the suitability of wind farm sites in the Red Sea off the coast of Saudi Arabia using the analytical hierarchy process (AHP) and a Geographic Information System (GIS). We assessed the suitability of offshore locations for wind energy projects, differentiating between fixed and floating turbines, and identified a 4180 km2 area as less suitable, whereas the 33,094 km2, 20,618 km2, and 11,077 km2 areas were deemed suitable, very suitable, and extremely suitable, respectively. These findings highlight the differences in suitability levels based on specific geographical features. Moreover, the extremely suitable location, which has the largest area of 3032 km2, has the capacity to generate an annual energy output of 56,965,410 MWh/year. Full article
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22 pages, 6029 KiB  
Article
Integrated Assessment of Security Risk Considering Police Resources
by Jieying Chen, Weihong Li, Yaxing Li and Yebin Chen
ISPRS Int. J. Geo-Inf. 2024, 13(11), 415; https://doi.org/10.3390/ijgi13110415 - 16 Nov 2024
Viewed by 562
Abstract
The existing research on security risk often focuses on specific types of crime, overlooking an integrated assessment of security risk by leveraging existing police resources. Thus, we draw on crime geography theories, integrating public security business data, socioeconomic data, and spatial analysis techniques, [...] Read more.
The existing research on security risk often focuses on specific types of crime, overlooking an integrated assessment of security risk by leveraging existing police resources. Thus, we draw on crime geography theories, integrating public security business data, socioeconomic data, and spatial analysis techniques, to identify integrated risk points and areas by examining the distribution of police resources and related factors and their influence on security risk. The findings indicate that security risk areas encompass high-incidence areas of public security issues, locations with concentrations of dangerous individuals and key facilities, and regions with a limited police presence, characterized by dense populations, diverse urban functions, high crime probabilities, and inadequate supervision. While both police resources and security risk are concentrated in urban areas, the latter exhibits a more scattered distribution on the urban periphery, suggesting opportunities to optimize resource allocation by extending police coverage to risk hotspots lacking patrol stations. Notably, Level 1 security risk areas often coincide with areas lacking a police presence, underscoring the need for strategic resource allocation. By comprehensively assessing the impact of police resources and public security data on spatial risk distribution, this study provides valuable insights for public security management and police operations. Full article
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23 pages, 19328 KiB  
Article
TravelRAG: A Tourist Attraction Retrieval Framework Based on Multi-Layer Knowledge Graph
by Sihan Song, Chuncheng Yang, Li Xu, Haibin Shang, Zhuo Li and Yinghui Chang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 414; https://doi.org/10.3390/ijgi13110414 - 16 Nov 2024
Viewed by 838
Abstract
A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address [...] Read more.
A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address the challenges LLMs face in providing precise and contextually appropriate responses to domain-specific queries in the tourism field. TravelRAG extracts information related to tourist attractions from User-Generated Content (UGC) on social media platforms and organizes it into a multi-layer knowledge graph. The travel knowledge graph serves as the core retrieval source for the LLM, enhancing the accuracy of information retrieval and significantly reducing the generation of erroneous or fabricated responses, often termed as “hallucinations”. As a result, the accuracy of the LLM’s output is enhanced. Comparative analyses with traditional RAG pipelines indicate that TravelRAG significantly boosts both the retrieval efficiency and accuracy, while also greatly reducing the computational cost of model fine-tuning. The experimental results show that TravelRAG not only outperforms traditional methods in terms of retrieval accuracy but also better meets user needs for content generation. Full article
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17 pages, 5464 KiB  
Article
Geographically-Informed Modeling and Analysis of Platform Attitude Jitter in GF-7 Sub-Meter Stereo Mapping Satellite
by Haoran Xia, Xinming Tang, Fan Mo, Junfeng Xie and Xiang Li
ISPRS Int. J. Geo-Inf. 2024, 13(11), 413; https://doi.org/10.3390/ijgi13110413 - 15 Nov 2024
Viewed by 580
Abstract
The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are [...] Read more.
The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article
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23 pages, 7975 KiB  
Article
Spatial-Temporal Changes in Ecosystem Service Value and Its Overlap with Coal Mining Intensity in the Yellow River Basin, China, During 2000–2030
by Yongjun Yang, Renjie Gong, Qinyu Wu and Fu Chen
ISPRS Int. J. Geo-Inf. 2024, 13(11), 412; https://doi.org/10.3390/ijgi13110412 - 14 Nov 2024
Viewed by 680
Abstract
Understanding the ecosystem services and their interaction with coal resource development is crucial for formulating sustainable development policies. In this study, we focused on the Yellow River Basin, characterized by both rich coal resources and ecological fragility. The key findings are that (1) [...] Read more.
Understanding the ecosystem services and their interaction with coal resource development is crucial for formulating sustainable development policies. In this study, we focused on the Yellow River Basin, characterized by both rich coal resources and ecological fragility. The key findings are that (1) the ecosystem service value (ESV) in the Yellow River Basin exhibited significant spatial heterogeneity during 2000–2030, decreasing from the southeast to northwest, and decreasing the most notably in the southern part of the upper reaches of the river basin; (2) the high-high clustering area of the ESV shifted from the upper-middle reaches in 2000 to the middle-lower reaches in 2020, while the low-low clustering area remained within Inner Mongolia. By 2030, the high-high clustering area is expected to stabilize in southern Shaanxi Province, and the low-low area will potentially spread eastward; (3) the overall ESV is low, and it experienced a significant decline from 2000 to 2020, with water supply emerging as a major limiting factor, although some policy-supported counties had better ecological service values and trends. (4) From 2000 to 2020, the coal mining intensity (CMI) was concentrated in the upper and middle reaches, particularly at the junctions of Shanxi, Shaanxi, and Inner Mongolia, and the pattern remained stable, but local areas experienced increased mining intensity; (5) the overlap of the CMI and ESV primarily exhibited a low-high clustering pattern in the middle and upper reaches of the Yellow River Basin and eastern Ordos City, and a high-high clustering pattern in the middle reaches of the basin in Shanxi Province, which remained stable and slightly expanded from 2000 to 2030; (6) the trade-off between the ecosystem services in the overlap area intensified, especially between the provisioning and support services, and was significantly impacted by the coal mining activities. The findings indicate that the area that overlaps with the coal mining area in the Yellow River Basin has expanded and has had an increasing negative impact on the ESV. It is also essential to address the trade-offs between the provisioning and support services and to implement ecological restoration measures to mitigate the risk of ESV loss. Future efforts should focus on the regions where the CMI and ESV overlap and have poor coordination and the adverse effects of resource extraction on ecosystem services are becoming more pronounced. The results of this study demonstrate that spatial overlap analysis is effective in identifying the hotspots and provides a foundation for developing sustainable and high-quality policies for ecologically fragile basins. Full article
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17 pages, 17173 KiB  
Article
Classifying the Shapes of Buildings by Combining Distance Field Enhancement and a Convolution Neural Network
by Xinyan Zou, Min Yang, Siyu Li and Hai Hu
ISPRS Int. J. Geo-Inf. 2024, 13(11), 411; https://doi.org/10.3390/ijgi13110411 - 14 Nov 2024
Viewed by 590
Abstract
The shape classification of building objects is crucial in fields such as map generalization and spatial queries. Recently, convolutional neural networks (CNNs) have been used to capture high-level features and classify building shape patterns based on raster representations. However, this raster-based deep learning [...] Read more.
The shape classification of building objects is crucial in fields such as map generalization and spatial queries. Recently, convolutional neural networks (CNNs) have been used to capture high-level features and classify building shape patterns based on raster representations. However, this raster-based deep learning method binarizes the areas into building and non-building zones and does not account for the distance information between these areas, potentially leading to the loss of shape feature information. To address this limitation, this study introduces a building shape classification method that incorporates distance field enhancement with a CNN. In this approach, the distance from various pixels to the building boundary is fused into the image data through distance field enhancement computation. The CNN model, specifically InceptionV3, is then employed to learn and classify building shapes using these enhanced images. The experimental results indicate that the accuracy of building shape classification improved by more than 2.5% following distance field enhancement. Notably, the classification accuracies for F-shaped and T-shaped buildings increased significantly by 4.34% and 11.76%, respectively. Moreover, the proposed method demonstrated a strong performance in classifying other building datasets, suggesting its substantial potential for enhancing shape classification in various applications. Full article
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20 pages, 4625 KiB  
Article
Delineations for Police Patrolling on Street Network Segments with p-Median Location Models
by Changho Lee, Hyun Kim, Yongwan Chun and Daniel A. Griffith
ISPRS Int. J. Geo-Inf. 2024, 13(11), 410; https://doi.org/10.3390/ijgi13110410 - 13 Nov 2024
Viewed by 662
Abstract
Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget [...] Read more.
Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This paper considers two p-median location models using segments on a street network as observational units on which traffic issues such as vehicle crashes occur. It also uses two weight sets to construct an enhanced delineation of police patrol areas in the City of Plano, Texas. The first model for the standard p-median formulation gives attention to the cumulative number of motor vehicle crashes from 2011 to 2021 on the major transportation networks in Plano. The second model, an extension of this first p-median one, uses balancing constraints to achieve balanced spatial coverage across patrol areas. These two models are also solved with network kernel density count estimates (NKDCE) instead of crash counts. These smoothed densities on a network enable consideration of uncertainty affiliated with this aggregation. The analysis results of this paper suggest that the p-median models provide effective specifications, including their capability to define patrol areas that encompass the entire study region while minimizing distance costs. The inclusion of balancing constraints ensures a more equitable distribution of workloads among patrol areas, improving overall efficiency. Additionally, the model with NKDCE results in an improved workload balance among delineated areas for police patrolling activities, thus supporting more informed spatial decision-making processes for public safety. Full article
(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)
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26 pages, 9635 KiB  
Article
A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting
by Loukas Katikas, Themistoklis Kontos, Panayiotis Dimitriadis and Marinos Kavouras
ISPRS Int. J. Geo-Inf. 2024, 13(11), 409; https://doi.org/10.3390/ijgi13110409 - 13 Nov 2024
Viewed by 731
Abstract
Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based [...] Read more.
Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea. Full article
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18 pages, 3875 KiB  
Article
Spatiotemporal Dynamics of Water Quality: Long-Term Assessment Using Water Quality Indices and GIS
by Dániel Balla, Emőke Kiss, Marianna Zichar and Tamás Mester
ISPRS Int. J. Geo-Inf. 2024, 13(11), 408; https://doi.org/10.3390/ijgi13110408 - 12 Nov 2024
Viewed by 802
Abstract
The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local [...] Read more.
The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local water quality changes following the construction of the sewerage network, under the framework of long-term monitoring (2011–2022) in Báránd, Hungary, using water quality indices and GIS (Geographic Information System) techniques. In order to understand the purification processes and spatial and temporal changes, three periods were determined: the pre-sewerage period (2011–2014), the transitional period (2015–2018), and the post-sewerage period (2019–2022). Forty monitoring wells were included in the study, ensuring complete coverage of the municipality. The results revealed a high level of pollution in the area in the pre-sewerage period. Based on the calculated indices, an average of 80% of the wells were ranked in categories 4–5, indicating poor water quality, while less than 8% were classified in categories 1–2, indicating good water quality. No significant purification process was detected in the transitional period. However, marked changes were observed in the post-sewerage period as a result of the elimination of local sources of pollution. In the post-sewerage period, the number of monitoring wells ranked as excellent and good increased significantly. Additionally, the number of wells assigned to category 5 decreased markedly, compared to the reference period. The significant difference between the three periods was confirmed by the Wilcoxon test as well (p < 0.05). Based on interpolated maps, it was found that, in the post-sewerage period, an increasing section of the settlement had good or excellent water quality. In addition to an assessment of long-term tendencies, the annual fluctuations in the water quality of the wells were also examined. This showed that the purification processes do not occur in a linear pattern but are influenced by various factors (e.g., precipitation). Our results highlight the importance of protecting and improving groundwater resources in municipal areas and the relevance of long-term monitoring of water adequate management policy. Full article
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13 pages, 3353 KiB  
Article
Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents
by Yunfei Zhang, Zhengrui Pan, Fangqi Zhu, Chaoyang Shi and Xue Yang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 407; https://doi.org/10.3390/ijgi13110407 - 12 Nov 2024
Viewed by 517
Abstract
Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. [...] Read more.
Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks. Full article
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25 pages, 15332 KiB  
Article
Identification and Causes of Neighborhood Commercial Areas: Focusing on the Development of Daily Life Circles in Urban Built Environments
by Tianyi Feng and Ying Zhou
ISPRS Int. J. Geo-Inf. 2024, 13(11), 406; https://doi.org/10.3390/ijgi13110406 - 11 Nov 2024
Viewed by 767
Abstract
Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, [...] Read more.
Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, the widely applicable methods for delineating DLCs remain lacking. This study presents a strategy for delineating DLCs centered on neighborhood commercial areas that aggregate essential daily life services. Correspondingly, a method is proposed for identifying neighborhood commercial areas based on residents’ actual usage of facilities. The method was applied in Qinhuai District, Nanjing, where neighborhood commercial areas were identified and the factors influencing their formation and types were quantitatively analyzed. The results indicate the following: (1) the proposed method accurately identifies neighborhood commercial areas that can serve as DLC central areas; (2) commercial diversity, public transportation stops, and parking spots are the three most influential factors in neighborhood commercial area formation, exhibiting non-linear and threshold effects; and (3) the type of neighborhood commercial areas varies by population density, housing prices, and street betweenness, with betweenness being the most significant factor. These findings provide methods and indicators for DLC delineation and planning, contributing to the realization of the DLC construction concept. Full article
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20 pages, 575 KiB  
Article
Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework
by Diya Li, Yue Zhao, Zhifang Wang, Calvin Jung and Zhe Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 405; https://doi.org/10.3390/ijgi13110405 - 10 Nov 2024
Viewed by 1364
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a [...] Read more.
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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17 pages, 2157 KiB  
Article
Analysis of Decoupling Effects and Influence Factors in Transportation: Evidence from Guangdong Province, China
by Hualing Bi, Shiying Zhang and Fuqiang Lu
ISPRS Int. J. Geo-Inf. 2024, 13(11), 404; https://doi.org/10.3390/ijgi13110404 - 8 Nov 2024
Viewed by 680
Abstract
In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem [...] Read more.
In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem within this industry. In light of the Tapio decoupling model, an analysis of the correlation between traffic carbon emissions and economic development in Guangdong province during 1999–2019 was carried out. With the aim of encouraging Guangdong province’s low-carbon transportation development, the factors affecting the transportation industry are analyzed utilizing the generalized Divisia index model (GDIM). We also introduced passenger and freight turnover as an influencing factor for analysis. The findings indicate that (1) Guangdong province’s traffic carbon emissions increased from 1999 to 2019; (2) the traffic carbon emissions’ decoupling effect is mainly “weakly decoupled”, and the overall decoupling effect is not strong in Guangdong province; (3) among the traffic carbon emissions’ factors, the effects of the production value of traffic and the turnover volume are at the forefront, and the effect of turnover volume has gradually exceeded the production value of traffic in recent years. The suppression of the intensity of carbon emissions is relatively large, while the suppression of the intensity of energy consumption and transport is relatively weak. Based on this, strategies were proposed to promote a cleaner energy mix, improve energy use efficiency, create energy savings, develop green technologies, and foster the restructuring of transportation. Full article
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23 pages, 6550 KiB  
Article
Examining Spatial Accessibility and Equity of Public Hospitals for Older Adults in Songjiang District, Shanghai
by Mirkamiljan Mahmut, Pei Yin, Bozhezi Peng, Jiani Wu, Tao Wang, Shengqiang Yuan and Yi Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 403; https://doi.org/10.3390/ijgi13110403 - 7 Nov 2024
Viewed by 765
Abstract
In developing countries, aging is rapid and new towns in suburban and rural districts are emerging. However, the spatial accessibility and equity of healthcare services for older adults in new towns is rarely examined. This study is among the earliest attempts to evaluate [...] Read more.
In developing countries, aging is rapid and new towns in suburban and rural districts are emerging. However, the spatial accessibility and equity of healthcare services for older adults in new towns is rarely examined. This study is among the earliest attempts to evaluate the spatial accessibility and equity of public hospitals for older adults, using data from Songjiang District, Shanghai, China. A modified Gaussian Huff-based three-step floating catchment area (GH3SFCA) method was adopted based on the real-time travel costs of public transit, driving, cycling, and walking. The Gini coefficient and Bivariate Moran’s Index were integrated to estimate spatial equity. The results showed that the spatial accessibility of high-tier hospitals decreases from the central areas to the outskirts for older adults in Songjiang. Meanwhile, the accessibility of low-tier hospitals varies substantially across areas. Although the low-tier hospitals are distributed evenly, their Gini coefficient showed less equitable spatial accessibility than the high-tier hospitals. Furthermore, driving and cycling lead to more equitable spatial accessibility than public transit or walking. Finally, communities with a low-supply–high-demand mismatch for public hospitals were suggested to be improved preferentially. These findings will facilitate planning strategies for public hospitals for older adults in developing new towns. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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20 pages, 3724 KiB  
Article
Unveiling Urban River Visual Features Through Immersive Virtual Reality: Analyzing Youth Perceptions with UAV Panoramic Imagery
by Yunlei Shou, Zexin Lei, Jiaying Li and Junjie Luo
ISPRS Int. J. Geo-Inf. 2024, 13(11), 402; https://doi.org/10.3390/ijgi13110402 - 7 Nov 2024
Viewed by 859
Abstract
The visual evaluation and characteristic analysis of urban rivers are pivotal for advancing our understanding of urban waterscapes and their surrounding environments. Unmanned aerial vehicles (UAVs) offer significant advantages over traditional satellite remote sensing, including flexible aerial surveying, diverse perspectives, and high-resolution imagery. [...] Read more.
The visual evaluation and characteristic analysis of urban rivers are pivotal for advancing our understanding of urban waterscapes and their surrounding environments. Unmanned aerial vehicles (UAVs) offer significant advantages over traditional satellite remote sensing, including flexible aerial surveying, diverse perspectives, and high-resolution imagery. This study centers on the Haihe River, South Canal, and North Canal in Tianjin China, employing UAVs to capture continuous panoramic image data. Through immersive virtual reality (VR) technology, visual evaluations of these panoramic images were obtained from a cohort of young participants. These evaluations encompassed assessments of scenic beauty, color richness, vitality, and historical sense. Subsequently, computer vision techniques were utilized to quantitatively analyze the proportions of various landscape elements (e.g., trees, grass, buildings) within the images. Clustering analysis of visual evaluation results and semantic segmentation outcomes from different study points facilitated the effective identification and grouping of river visual features. The findings reveal significant differences in scenic beauty, color richness, and vitality among the Haihe River, South Canal, and North Canal, whereas the South and North Canals exhibited a limited sense of history. Six landscape elements—water bodies, buildings, trees, etc.—comprised over 90% of the images, forming the primary visual characteristics of the three rivers. Nonetheless, the uneven spatial distribution of these elements resulted in notable variations in the visual features of the rivers. This study demonstrates that the visual feature analysis method based on UAV panoramic images can achieve a quantitative evaluation of multi-scene urban 3D landscapes, thereby providing a robust scientific foundation for the optimization of urban river environments. Full article
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17 pages, 2894 KiB  
Article
Open Data for Transparency of Government Tenders: A State Analysis in Croatian Agriculture Land Lease
by Larisa Hrustek, Karlo Kević and Filip Varga
ISPRS Int. J. Geo-Inf. 2024, 13(11), 401; https://doi.org/10.3390/ijgi13110401 - 7 Nov 2024
Viewed by 735
Abstract
State-owned agricultural land is an asset that the state must manage in a responsible and transparent manner. Agricultural land is extremely important for farmers as it enables them to carry out agricultural activities. Due to its importance to farmers, it is often the [...] Read more.
State-owned agricultural land is an asset that the state must manage in a responsible and transparent manner. Agricultural land is extremely important for farmers as it enables them to carry out agricultural activities. Due to its importance to farmers, it is often the subject of debate as stakeholders are often dissatisfied with the allocation and management of state-owned agricultural land. Qualitative research of the process of state agricultural land lease and the associated legislation in the Republic of Croatia enabled the analysis of the existing business model, with the results pointing to shortcomings in the Initial and Evaluation phases of the process. A steady rise in the number of tenders published in 2015–2022 was recorded. Local administrative units in the Continental region scored higher than those in the Adriatic region (both cities and municipalities) in terms of transparency. Unfortunately, the response rate from the local authorities was below 50% across both region and unit, further indicating low transparency. Based on the findings, a proposal of changes in the tendering process was made utilizing a digital platform as an environment for all stakeholders, which provides functionalities essential for the transparent implementation of tenders for the agricultural land lease in Croatia. Full article
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14 pages, 6699 KiB  
Article
TPTrans: Vessel Trajectory Prediction Model Based on Transformer Using AIS Data
by Wentao Wang, Wei Xiong, Xue Ouyang and Luo Chen
ISPRS Int. J. Geo-Inf. 2024, 13(11), 400; https://doi.org/10.3390/ijgi13110400 - 7 Nov 2024
Viewed by 827
Abstract
The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to [...] Read more.
The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6×104 compared with the baseline model. Full article
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22 pages, 9430 KiB  
Article
Using Space Syntax and GIS to Determine Future Growth Routes of Cities: The Case of the Kyrenia White Zone
by Cem Doğu and Cemil Atakara
ISPRS Int. J. Geo-Inf. 2024, 13(11), 399; https://doi.org/10.3390/ijgi13110399 - 7 Nov 2024
Viewed by 595
Abstract
Cities are in constant development, both structurally and demographically, necessitating careful planning to enhance their orderliness and livability. This research focuses on identifying development directions and routes for the Kyrenia White Zone, situated between the sea and the mountains in northern Cyprus, a [...] Read more.
Cities are in constant development, both structurally and demographically, necessitating careful planning to enhance their orderliness and livability. This research focuses on identifying development directions and routes for the Kyrenia White Zone, situated between the sea and the mountains in northern Cyprus, a significant tourist area. The rapid implementation of zoning laws over different periods has led to swift development and population growth, resulting in various infrastructure challenges, particularly related to transportation. The primary aim of this study is to assess the current infrastructure issues within the zone, understand user perceptions, and identify key factors influencing future growth. Based on the collected data, we propose an alternative growth area for the future development plan of the city. Additionally, this research seeks to explore irregular urban developments and make informed design decisions for their future. Utilizing Space Syntax and GIS as core methodologies, the study employs Space Syntax, a research method developed by Bill Hillier and Julienne Hanson in the 1970s, to analyze human movement and perception. The existing map system of the Kyrenia White Zone was digitized, and essential geographical information was gathered. This data were analyzed using GIS and evaluated through the Space Syntax method. The analysis yielded alternative growth routes that address current challenges within the zone, accompanied by recommendations for enhancing its future development. Full article
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20 pages, 11747 KiB  
Article
An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements
by Wei Zhu, Qingsheng Guo, Nai Yang, Ying Tong and Chuanbang Zheng
ISPRS Int. J. Geo-Inf. 2024, 13(11), 398; https://doi.org/10.3390/ijgi13110398 - 7 Nov 2024
Viewed by 644
Abstract
Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning [...] Read more.
Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning multi-scale electronic map tile generation is needed to meet cartographic requirements. We designed a multi-scale electronic map tile generative adversarial network (MsM-GAN), which consisted of several GANs and could generate map tiles at different map scales sequentially. Road network data and building footprint data from OSM (Open Street Map) were used as auxiliary information to provide the MsM-GAN with cartographic knowledge about spatial shapes and spatial relationships when generating electronic map tiles from remote sensing images. The map objects which should be deleted or retained at the next map scale according to cartographic standards are encoded as auxiliary information in the MsM-GAN when generating electronic map tiles at smaller map scales. In addition, in order to ensure the consistency of the features learned by several GANs, the density maps constructed from specific map objects are used as global conditions in the MsM-GAN. A multi-scale map tile dataset was collected from MapWorld, and experiments on this dataset were conducted using the MsM-GAN. The results showed that compared to other image-to-image translation models (Pix2Pix and CycleGAN), the MsM-GAN shows average increases of 10.47% in PSNR and 9.92% in SSIM and has the minimum MSE values at all four map scales. The MsM-GAN also performs better in visual evaluation. In addition, several comparative experiments were completed to verify the effect of the proposed improvements. Full article
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15 pages, 2134 KiB  
Article
Space–Time Analysis of the COVID-19 Pandemic and Its Relationship with Socioeconomic and Demographic Variables in the Metropolitan Region of São Paulo, Brazil
by Keila Valente de Souza de Santana, Aluízio Marino, Gabriela Rosa Martins, Pedro Henrique Barbosa Muniz Lima, Pedro Henrique Rezende Mendonça and Raquel Rolnik
ISPRS Int. J. Geo-Inf. 2024, 13(11), 397; https://doi.org/10.3390/ijgi13110397 - 7 Nov 2024
Viewed by 751
Abstract
This study sought to identify clusters of a high and low risk of incidence and mortality from COVID-19 throughout the pandemic period, from 2020 to 2022, in the Metropolitan Region of São Paulo (MRSP), analyzing their relationship with socioeconomic and demographic variables. Spatiotemporal [...] Read more.
This study sought to identify clusters of a high and low risk of incidence and mortality from COVID-19 throughout the pandemic period, from 2020 to 2022, in the Metropolitan Region of São Paulo (MRSP), analyzing their relationship with socioeconomic and demographic variables. Spatiotemporal and temporal variations in the clusters were determined using scan statistics, a multidimensional point process that performs multiple tests for each geographic point analyzed, in SaTScan v10.0. Socioeconomic and demographic differences were analyzed using the nonparametric Mann–Whitney and Kruskal–Wallis tests. Temporal clusters of high incidence and high mortality were observed in May 2020 and March to June 2021. In the spatiotemporal analysis, the clusters of high incidence and high mortality were concentrated in the city of São Paulo and neighboring cities, indicating that the capital was an area of influence and convergence at all times during the COVID-19 pandemic. Clusters of low mortality were found in the central region of the capital, which concentrates the highest incomes and the lowest percentages of Black, mixed-race, and Indigenous people in the MRSP. All clusters were identified in densely occupied areas and point to a pattern of disease spread that is related to income and ethnicity, as well as to the circulation dynamics of a metropolitan region. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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19 pages, 4363 KiB  
Article
Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
by Le Chen, Simin Yu, Shi Shen, You Wan and Changqing Song
ISPRS Int. J. Geo-Inf. 2024, 13(11), 396; https://doi.org/10.3390/ijgi13110396 - 6 Nov 2024
Viewed by 711
Abstract
The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, [...] Read more.
The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, there is a lack of understanding of the impact of environmental conditions and human activity on the vegetation’s carbon sequestration ability, especially in highly urbanized areas. For example, effective vegetation management methods can enhance vegetation Gross Primary Productivity, while emissions of air pollutants like O3, CO, NO2, and PM2.5 can suppress it. This paper mainly explores the factors influencing vegetation carbon sequestration capacity across different regions of Beijing. Based on remote sensing data and site observation data, this paper analyzed the spatiotemporal variation trend of Annual Gross Primary Production (AGPP) and the influence of environmental factors and human activity factors on GPP in Beijing from 2000 to 2020 by using the Theil−Sen’s slope estimator, Mann−Kendall trend test, and comparing Geographically Weighted Regression method (GWR) and Geographically and Temporally Weighted Regression method (GTWR). GWR is a localized multiple regression technique used to estimate variable relationships that vary spatially. GTWR extends GWR by adding temporal analysis, enabling a comprehensive examination of spatiotemporal data variations. Besides, we used land use cover data to discuss the influence of land use cover change on AGPP. The results showed that the spatial distribution pattern of GPP in Beijing was higher in the northwest and lower in the southeast, and it showed an overall upward trend from 2000 to 2020, with an average annual growth rate of 14.39 g C·m−2·a−1. From 2000 to 2020, excluding the core urban areas, the GPP of 95.8% of Beijing increased, and 10.6% of Beijing showed a trend of significant increase, concentrated in Mentougou, Changping, and Miyun. GPP decreased in 4.1% of the regions in Beijing and decreased significantly in 1.4% of the areas within the sixth ring. The areas where AGPP significantly decreased were concentrated in those where land use types were converted to Residential land (impervious land), while AGPP showed an upward trend in other areas. CO and NO2 are the main driving forces of GPP change in Beijing. O3 and land surface temperature (LST) also exert certain influences, while the impact of precipitation (PRE) is relatively minor. O3 and CO have a positive impact on AGPP as a whole, while LST and NO2 generally exhibit negative impacts. PRE has a positive impact in the central area of Beijing, while it has a negative impact in the peripheral areas. This study further discusses opinions on future urbanization and environmental management policies in Beijing, which will promote the carbon peak and carbon neutrality process of ecological space management in Beijing. Besides, this study was conducted at the urban scale rather than at ecological sites, encompassing a variety of factors that influence vegetation AGPP. Consequently, the results also offer fresh insights into the intricate nexus between human activities, pollutants, and the GPP of vegetation. Full article
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19 pages, 3989 KiB  
Article
Population Distribution Forecasting Based on the Fusion of Spatiotemporal Basic and External Features: A Case Study of Lujiazui Financial District
by Xianzhou Cheng, Xiaoming Wang and Renhe Jiang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 395; https://doi.org/10.3390/ijgi13110395 - 6 Nov 2024
Viewed by 583
Abstract
Predicting the distribution of people in the time window approaching a disaster is crucial for post-disaster assistance activities and can be useful for evacuation route selection and shelter planning. However, two major limitations have not yet been addressed: (1) Most spatiotemporal prediction models [...] Read more.
Predicting the distribution of people in the time window approaching a disaster is crucial for post-disaster assistance activities and can be useful for evacuation route selection and shelter planning. However, two major limitations have not yet been addressed: (1) Most spatiotemporal prediction models incorporate spatiotemporal features either directly or indirectly, which results in high information redundancy in the parameters of the prediction model and low computational efficiency. (2) These models usually incorporate certain basic and external features, and they can neither change spatiotemporal addressed features according to spatiotemporal features nor change them in real-time according to spatiotemporal features. The spatiotemporal feature embedding methods for these models are inflexible and difficult to interpret. To overcome these problems, a lightweight population density distribution prediction framework that considers both basic and external spatiotemporal features is proposed. In the study, an autoencoder is used to extract spatiotemporal coded information to form a spatiotemporal attention mechanism, and basic and external spatiotemporal feature attention is fused by a fusion framework with learnable weights. The fused spatiotemporal attention is fused with Resnet as the prediction backbone network to predict the people distribution. Comparison and ablation experimental results show that the computational efficiency and interpretability of the prediction framework are improved by maximizing the scalability of the spatiotemporal features of the model by unleashing the scalability of the spatiotemporal features of the model while enhancing the interpretability of the spatiotemporal information as compared to the classical and popular spatiotemporal prediction frameworks. This study has a multiplier effect and provides a reference solution for predicting population distributions in similar regions around the globe. Full article
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26 pages, 19169 KiB  
Article
Multi-Scale Effects of Supply–Demand Changes in Water-Related Ecosystem Services Across Different Landscapes in River Basin
by Bin Ouyang, Zhigang Yan, Yuncheng Jiang, Chuanjun Deng, Yanhong Chen and Longhua Wu
ISPRS Int. J. Geo-Inf. 2024, 13(11), 394; https://doi.org/10.3390/ijgi13110394 - 5 Nov 2024
Viewed by 668
Abstract
To promote sustainable hierarchical management, it is essential to understand the complex relationships within and underlying causes of supply–demand changes in water-related ecosystem services (WESs) across different spatial scales and landscape patterns. Consequently, the Optimal Parameters-based Geographical Detector (OPGD) and Multi-Scale Geographically Weighted [...] Read more.
To promote sustainable hierarchical management, it is essential to understand the complex relationships within and underlying causes of supply–demand changes in water-related ecosystem services (WESs) across different spatial scales and landscape patterns. Consequently, the Optimal Parameters-based Geographical Detector (OPGD) and Multi-Scale Geographically Weighted Regression (MGWR) are used to analyze the factors influencing changes in WESs supply–demand. The findings indicate that (1) at the macroscale, population size, and economic activity are the main driving factors, while at the microscale, precipitation becomes the primary factor influencing fluctuations in WESs supply–demand. (2) Furthermore, over time, the influence of social factors becomes increasingly significant. (3) The explanatory power of a single factor typically increases as it interacts with other factors. (4) Abundant precipitation helps in the generation and maintenance of WESs, but intense human activities may have negative impacts on them. Therefore, we have made significant progress in identifying and analyzing the natural and human-induced driving forces affecting changes in WESs by deeply integrating long-term multi-source remote sensing data with the OPGD and MGWR models. Full article
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26 pages, 21375 KiB  
Article
A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan
by Ali Muhammad, Donghui Shangguan, Ghulam Rasool, Amjad Ali Khan, Asim Qayyum Butt, Ayesha Hussain and Muhammad Ahsan Mukhtar
ISPRS Int. J. Geo-Inf. 2024, 13(11), 393; https://doi.org/10.3390/ijgi13110393 - 2 Nov 2024
Viewed by 1117
Abstract
Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and [...] Read more.
Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl, SO42, NO3, HCO3), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions. Full article
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25 pages, 24649 KiB  
Article
Power Corridor Safety Hazard Detection Based on Airborne 3D Laser Scanning Technology
by Shuo Wang, Zhigen Zhao and Hang Liu
ISPRS Int. J. Geo-Inf. 2024, 13(11), 392; https://doi.org/10.3390/ijgi13110392 - 1 Nov 2024
Viewed by 851
Abstract
Overhead transmission lines are widely deployed across both mountainous and plain areas and serve as a critical infrastructure for China’s electric power industry. The rapid advancement of three-dimensional (3D) laser scanning technology, with airborne LiDAR at its core, enables high-precision and rapid scanning [...] Read more.
Overhead transmission lines are widely deployed across both mountainous and plain areas and serve as a critical infrastructure for China’s electric power industry. The rapid advancement of three-dimensional (3D) laser scanning technology, with airborne LiDAR at its core, enables high-precision and rapid scanning of the detection area, offering significant value in identifying safety hazards along transmission lines in complex environments. In this paper, five transmission lines, spanning a total of 160 km in the mountainous area of Sanmenxia City, Henan Province, China, serve as the primary research objects and generate several insights. The location and elevation of each power tower pole are determined using an Unmanned Aerial Vehicle (UAV), which assesses the direction and elevation changes in the transmission lines. Moreover, point cloud data of the transmission line corridor are acquired and archived using a UAV equipped with LiDAR during variable-height flight. The data processing of the 3D laser point cloud of the power corridor involves denoising, line repair, thinning, and classification. By calculating the clearance, horizontal, and vertical distances between the power towers, transmission lines, and other surface features, in conjunction with safety distance requirements, information about potential hazards can be generated. The results of detecting these five transmission lines reveal 54 general hazards, 22 major hazards, and an emergency hazard in terms of hazards of the vegetation type. The type of hazard in the current working condition is mainly vegetation, and the types of cross-crossing hazards are power lines and buildings. The detection results are submitted to the local power department in a timely manner, and relevant measures are taken to eliminate hazards and ensure the normal supply of power resources. The research in this paper will provide a basis and an important reference for identifying the potential safety hazards of transmission lines in Henan Province and other complex environments and solving existing problems in the manual inspection of transmission lines. Full article
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20 pages, 14555 KiB  
Article
Mining and Visualization of Tourism Cultural Image Based on the Information Transmission Model of Tourism Cultural Map—Taking Nanjing Xuanwu Lake Tourist Attraction as an Example
by Haoyu Yang, Jie Shen, Shuai Hong and Fengzhen Zhu
ISPRS Int. J. Geo-Inf. 2024, 13(11), 391; https://doi.org/10.3390/ijgi13110391 - 1 Nov 2024
Viewed by 742
Abstract
Tourism cultural image is vital for conveying the cultural essence of a destination, enhancing tourists’ cultural understanding and engagement. However, traditional tourism cultural maps often face challenges in clearly defining cultural themes and effectively communicating cultural and emotional information to users. To address [...] Read more.
Tourism cultural image is vital for conveying the cultural essence of a destination, enhancing tourists’ cultural understanding and engagement. However, traditional tourism cultural maps often face challenges in clearly defining cultural themes and effectively communicating cultural and emotional information to users. To address these issues, we propose an improved information transmission model for a tourism cultural map to optimize the communication pathway between cartographers and map users. Based on this model, we introduce a method for mining and visualizing tourism cultural image using tourist attractions as the focal points. Then, based on the visualization results, we discuss the selection of map expression objects and the framework of map visualization design. Finally, we give an implementation process of a tourism cultural map of tourist attractions based on mining and visualization of tourism cultural image. To verify the method’s feasibility, we developed a mobile interactive tourism cultural map application using Xuanwu Lake in Nanjing, China, as an example. The results demonstrate the method’s effectiveness in tourism cultural image mining and visualization, provide solutions to the problems of traditional tourism cultural maps, and help tourists’ understanding of the culture of tourist attractions. Full article
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15 pages, 16699 KiB  
Article
Spatiotemporal Relationship Between Land Subsidence and Ecological Environmental Quality in Shenfu Mining Area, Loess Plateau, China
by Shuaizhi Kang, Xia Jia, Yonghua Zhao, Yong Ao and Chaoqun Ma
ISPRS Int. J. Geo-Inf. 2024, 13(11), 390; https://doi.org/10.3390/ijgi13110390 - 31 Oct 2024
Viewed by 703
Abstract
The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor [...] Read more.
The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence. Full article
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17 pages, 1720 KiB  
Article
Exploring Georeferenced Augmented Reality for Architectural Visualization with Unmanned Aerial Vehicles
by João Paulo Franco Assumpção and Ana Regina Mizrahy Cuperschmid
ISPRS Int. J. Geo-Inf. 2024, 13(11), 389; https://doi.org/10.3390/ijgi13110389 - 31 Oct 2024
Viewed by 674
Abstract
Unmanned aerial systems (UASs) offer a less invasive solution for accessing remote areas and sites, making them valuable in Architecture, Engineering, Construction, and Operation (AECO). Their ease of use, ability to reach previously inaccessible areas, and sensor integration provide new project perspectives. Augmented [...] Read more.
Unmanned aerial systems (UASs) offer a less invasive solution for accessing remote areas and sites, making them valuable in Architecture, Engineering, Construction, and Operation (AECO). Their ease of use, ability to reach previously inaccessible areas, and sensor integration provide new project perspectives. Augmented Reality (AR), which allows for the real-time insertion of virtual elements into physical spaces, is also being explored in the AECO industry. Recognizing the potential of these technologies, this research aims to integrate them for on-site building model visualization. This article presents the development of resources to visualize building design implementation in AR, which is supported by UASs through georeferencing. The system development process included establishing the software architecture, creating interface prototypes, and constructing the model. It was possible to visualize the building model in AR within the real world; however, limitations were identified regarding the UAS used and its Application Programming Interface, which affected the aircraft’s programmed trajectory. The contribution of this paper lies in exploring the utilization of georeferenced AR enabled by UAS for visualizing architectural designs on site, detailing the steps and strategies employed to achieve this, highlighting the limitations of the chosen approach, and proposing potential solutions to the issues identified in the research. Full article
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26 pages, 4911 KiB  
Article
Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach
by Suresh Mondal, Mst Tania Parveen, Asraful Alam, Rukhsana, Nazrul Islam, Beata Calka, Bashar Bashir and Mohamed Zhran
ISPRS Int. J. Geo-Inf. 2024, 13(11), 388; https://doi.org/10.3390/ijgi13110388 - 31 Oct 2024
Viewed by 1281
Abstract
The rapid urbanization occurring globally has significantly intensified the challenges of waste management in densely populated metropolitan areas. A growing amount of waste has become a major concern for municipal authorities and local governments due to the limited availability of suitable land. Geospatial [...] Read more.
The rapid urbanization occurring globally has significantly intensified the challenges of waste management in densely populated metropolitan areas. A growing amount of waste has become a major concern for municipal authorities and local governments due to the limited availability of suitable land. Geospatial techniques, such as Geographic Information Systems (GISs) and remote sensing, combined with machine learning, play a crucial role in identifying suitable sites for urban waste management. These techniques assist planners in making well-informed decisions that strike a balance between environmental preservation and urban expansion by examining spatial data on land use, population density, and environmental concerns. Geospatial tools provide a data-driven basis for policy and urban planning, ensuring effective land use, reducing ecological hazards, and promoting sustainable urban growth for municipalities such as English Bazar and Old Malda. It can also pose serious threats to the environment, public health, and communities. Focusing on the English Bazar and Old Malda Municipalities in India, this paper examines the use of geospatial technologies to identify suitable sites for waste disposal. The research aims to address the complex processes of waste generation, collection, and disposal in urban environments. Using GIS and a Multi-Criteria Decision Analysis (MCDA) approach, the study employs the Analytic Hierarchy Process (AHP) alongside the Random Forest (RF) model and a machine learning (ML) technique to identify potential waste disposal sites within the English Bazar and Old Malda Municipalities in the Malda district. Eight key criteria were considered in the site selection process: land elevation; distances from surface water, roads, railways, and urban areas; groundwater depth; land use and land cover; and distance from sensitive and restricted areas. AHP analysis showed that 8%, 26%, and 27% of the sites were categorized as very highly suitable, moderately suitable, and unsuitable, respectively. Meanwhile, 38%, 17%, and 13% of the areas were classified as unsuitable, moderately suitable, and very highly suitable according to the RF model. The overall accuracy and Kappa coefficient indicated that the AHP method (overall capacity of 83.83% and Kappa coefficient of 0.7894) was slightly better than the RF model (overall capacity of 80.61% and Kappa coefficient of 0.7474) for site suitability analysis. This research underscores the broad relevance of geospatial technology in creating resilient and environmentally sustainable cities while offering valuable guidance on effectively allocating waste disposal sites. The findings provide crucial insights for urban planners and decision-makers, facilitating the identification of optimal locations for sustainable waste management in urban settings. Full article
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