ISPRS International Journal of Geo-Information

19 pages, 13729 KiB

Open AccessArticle

Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India

by Asheesh Sharma, Mandeep Poonia, Ankush Rai, Rajesh B. Biniwale, Franziska Tügel, Ekkehard Holzbecher and Reinhard Hinkelmann

ISPRS Int. J. Geo-Inf. 2024, 13(8), 297; https://doi.org/10.3390/ijgi13080297 - 22 Aug 2024

Viewed by 1057

Abstract

Flooding poses a significant threat as a prevalent natural disaster. To mitigate its impact, identifying flood-prone areas through susceptibility mapping is essential for effective flood risk management. This study conducted flood susceptibility mapping (FSM) in Chandrapur district, Maharashtra, India, using geographic information system [...] Read more.

Flooding poses a significant threat as a prevalent natural disaster. To mitigate its impact, identifying flood-prone areas through susceptibility mapping is essential for effective flood risk management. This study conducted flood susceptibility mapping (FSM) in Chandrapur district, Maharashtra, India, using geographic information system (GIS)-based frequency ratio (FR) and Shannon’s entropy index (SEI) models. Seven flood-contributing factors were considered, and historical flood data were utilized for model training and testing. Model performance was evaluated using the area under the curve (AUC) metric. The AUC values of 0.982 for the SEI model and 0.966 for the FR model in the test dataset underscore the robust performance of both models. The results revealed that 5.4% and 8.1% (FR model) and 3.8% and 7.6% (SEI model) of the study area face very high and high risks of flooding, respectively. Comparative analysis indicated the superiority of the SEI model. The key limitations of the models are discussed. This study attempted to simplify the process for the easy and straightforward implementation of FR and SEI statistical flood susceptibility models along with key insights into the flood vulnerability of the study region. Full article

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

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25 pages, 10370 KiB

Open AccessArticle

Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China

by Moyan Wang, Zhengyuan Liang and Zhiming Li

ISPRS Int. J. Geo-Inf. 2024, 13(8), 296; https://doi.org/10.3390/ijgi13080296 - 20 Aug 2024

Viewed by 1045

Abstract

With the increasing demand for electric vehicle public charging infrastructure (EVPCI), optimizing the charging network to ensure equal access is crucial to promote the sustainable development of the electric vehicle market and clean energy. Due to limited urban land space and the large-scale [...] Read more.

With the increasing demand for electric vehicle public charging infrastructure (EVPCI), optimizing the charging network to ensure equal access is crucial to promote the sustainable development of the electric vehicle market and clean energy. Due to limited urban land space and the large-scale expansion of charging infrastructure, determining where to begin optimization is the first step in improving its layout. This paper uses a multidimensional assessment framework to identify spatial disparities in the distribution of EVPCI in Nanjing Central Districts, China. We construct a scientific evaluation system of the public charging infrastructure (PCI) layout from four spatial indicators: accessibility, availability, convenience, and affordability. Through univariate and bivariate local indicators of spatial autocorrelation (LISA), the spatial agglomeration pattern of the EVPCI service level and its spatial correlation with social factors are revealed. The results of this study not only identify areas in Nanjing where the distribution of PCI is uneven and where there is a shortage but also identify areas down to the community level where there are signs of potential wastage of PCI resources. The results demonstrate that (1) urban planners and policymakers need to expand the focus of PCI construction from the main city to the three sub-cities; (2) it is necessary to increase the deployment of PCI in Nanjing’s old residential communities; and (3) the expansion of PCI in Nanjing must be incremental and optimized in terms of allocation, or else it should be reduced and recycled in areas where there are signs of resource wastage. This study provides targeted and implementable deployment strategies for the optimization of the spatial layout of EVPCI. Full article

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27 pages, 20774 KiB

Open AccessEditor’s ChoiceArticle

Genetic Programming to Optimize 3D Trajectories

by André Kotze, Moritz Jan Hildemann, Vítor Santos and Carlos Granell

ISPRS Int. J. Geo-Inf. 2024, 13(8), 295; https://doi.org/10.3390/ijgi13080295 - 20 Aug 2024

Viewed by 1006

Abstract

Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal [...] Read more.

Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)

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21 pages, 13694 KiB

Open AccessArticle

An Improved ANN-Based Label Placement Method Considering Surrounding Features for Schematic Metro Maps

by Zhiwei Wu, Tian Lan, Chenzhen Sun, Donglin Cheng, Xing Shi, Meisheng Chen and Guangjun Zeng

ISPRS Int. J. Geo-Inf. 2024, 13(8), 294; https://doi.org/10.3390/ijgi13080294 - 19 Aug 2024

Cited by 1 | Viewed by 691

Abstract

On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only [...] Read more.

On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps. Full article

(This article belongs to the Topic Geocomputation and Artificial Intelligence for Mapping)

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20 pages, 1305 KiB

Open AccessArticle

Analysis of the Impact of the Digital Economy on Carbon Emission Reduction and Its Spatial Spillover Effect—The Case of Eastern Coastal Cities in China

by Juanjuan Zhong, Ye Duan, Caizhi Sun and Hongye Wang

ISPRS Int. J. Geo-Inf. 2024, 13(8), 293; https://doi.org/10.3390/ijgi13080293 - 18 Aug 2024

Viewed by 1002

Abstract

The expansion of the digital economy is crucial for halting climate change, as carbon emissions from urban energy use contribute significantly to global warming. This study uses the Difference-in-Differences Model and the Spatial Durbin Model determine whether the digital economy may support the [...] Read more.

The expansion of the digital economy is crucial for halting climate change, as carbon emissions from urban energy use contribute significantly to global warming. This study uses the Difference-in-Differences Model and the Spatial Durbin Model determine whether the digital economy may support the development of reducing carbon emissions and its geographic spillover effects in Chinese cities on the east coast. In addition, it looks more closely at the effects of lowering carbon emissions in space by separating them into direct, indirect, and spatial impact parts. The findings show that (1) from 2012 to 2021, the digital economy favored carbon emission reductions in China’s eastern coastline cities, as supported by the robustness test. (2) The link between digital economy growth and carbon emissions is highly variable, with smart city development and urban agglomeration expansion both cutting city carbon emissions considerably. Successful digital economy strategies can lower CO₂ emissions from nearby cities. (3) Eastern coastal cities have a considerable spatial spillover impact, and the digital economy mitigates local energy consumption and carbon emissions while simultaneously enhancing environmental quality in nearby urban areas. This analysis proposes that the peak carbon and carbon neutrality targets can be met by increasing the digital economy and enhancing regional environmental governance cooperation. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)

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15 pages, 5666 KiB

Open AccessArticle

Assessing and Predicting Nearshore Seawater Quality with Spatio-Temporal Semivariograms: The Case of Coastal Waters in Fujian Province, China

by Wei Wang, Wenfang Cheng and Jing Chen

ISPRS Int. J. Geo-Inf. 2024, 13(8), 292; https://doi.org/10.3390/ijgi13080292 - 17 Aug 2024

Cited by 1 | Viewed by 803

Abstract

The scientific assessment and prediction of nearshore water quality are crucial for marine environment protection efforts. This study is based on a comprehensive analysis of existing assessment and prediction methods and considers the regular and random characteristics of nearshore seawater quality due to [...] Read more.

The scientific assessment and prediction of nearshore water quality are crucial for marine environment protection efforts. This study is based on a comprehensive analysis of existing assessment and prediction methods and considers the regular and random characteristics of nearshore seawater quality due to both natural and anthropogenic influences. It proposes a new method that applies the kriging interpolation algorithm to empirically generated spatio-temporal semivariograms to assess and predict seawater quality. The application of this method in Fujian coastal areas shows that it is able to flexibly and scientifically estimate the variations in various indicators in the region. Combined with GIS spatial data overlay analysis operations, it can be used to quantitatively evaluate different qualities of seawater and provide scientific guidance for marine environmental protection. Full article

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24 pages, 4806 KiB

Open AccessArticle

Spatial Semantics for the Evaluation of Administrative Geospatial Ontologies

by Alia I. Abdelmoty, Hanan Muhajab and Abdurauf Satoti

ISPRS Int. J. Geo-Inf. 2024, 13(8), 291; https://doi.org/10.3390/ijgi13080291 - 17 Aug 2024

Viewed by 690

Abstract

Administrative geography is concerned with the hierarchy of areas related to national and local government in a country. They form an important dataset in the country’s open data provision and act as the geo-referencing backdrop for many types of geospatial data. Proprietary ontologies [...] Read more.

Administrative geography is concerned with the hierarchy of areas related to national and local government in a country. They form an important dataset in the country’s open data provision and act as the geo-referencing backdrop for many types of geospatial data. Proprietary ontologies are built to model and represent these data with little focus on spatial semantics. Studying the quality of these ontologies and developing methods for their evaluation are needed. This paper addresses these problems by studying the spatial semantics of administrative geography data and proposes a uniform set of qualitative semantics that encapsulates the inherent spatial structure of the administrative divisions and allows for the application of spatial reasoning. Topological and proximity semantics are defined and combined into a single measure of spatial completeness and used for defining a set of competency questions to be used in the evaluation process. The significance of the novel measure of completeness and competency questions is demonstrated on four prominent real world administrative geography ontologies. It is shown how these can provide an objective measure of quality of the geospatial ontologies and gaps in their definition. The proposed approach to defining spatial completeness complements the established methods in the literature, that primarily focus on the syntactical and structural dimensions of the ontologies, and offers a novel approach to ontology evaluation in the geospatial domain. Full article

(This article belongs to the Special Issue Unlocking the Power of Geospatial Data: Semantic Information Extraction, Ontology Engineering, and Deep Learning for Knowledge Discovery)

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20 pages, 5211 KiB

Open AccessArticle

Spatial Planning Data Structure Based on Blockchain Technology

by Minwen Tang, Wujiao Dai, Changlin Yin, Bing Hu, Jun Chen and Haoming Liu

ISPRS Int. J. Geo-Inf. 2024, 13(8), 290; https://doi.org/10.3390/ijgi13080290 - 17 Aug 2024

Viewed by 715

Abstract

Spatial planning requires ensuring the legality, uniformity, authority, and relevance of data. Blockchain technology, characterized by tamper-proofing, complete record-keeping, and process traceability, may effectively organize and manage spatial planning data. This study introduces blockchain technology to address common spatial planning problems, such as [...] Read more.

Spatial planning requires ensuring the legality, uniformity, authority, and relevance of data. Blockchain technology, characterized by tamper-proofing, complete record-keeping, and process traceability, may effectively organize and manage spatial planning data. This study introduces blockchain technology to address common spatial planning problems, such as planning overlaps and conflicts. We developed a block structure, chain structure, and consensus algorithms tailored for spatial planning. To meet the data management requirements of these structures, we devised a primary unit division method based on the space and population standards of the 15 min life circle, using the Point Cloud Density Tiler. The validation experiments were conducted using the Hyperledger Fabric 2.0 technology framework in Changsha City, Hunan Province, China, with the division method validated against the number and distribution of public service facilities. The validation results show that during the data storage process, the block size remains below 1.00 MB, the data redundancy is up to 21.30%, the consensus verification rate is 150.33 times per second, the block generation rate is 20.83 blocks per minute, and the equivalent data throughput is 12.21 transactions per second. This demonstrates that the proposed method effectively addresses the challenges of block size, data redundancy, consensus algorithm efficiency, and data throughput in blockchain technology. The findings demonstrate that the structures ensure legal, uniform, and authoritative spatial planning, and advance the application of blockchain technology in relevant fields. Additionally, we explored the application of a blockchain data structure in spatial planning monitoring and early warning. This technology can be further studied and applied in related fields. Full article

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16 pages, 14561 KiB

Open AccessArticle

SAMPLID: A New Supervised Approach for Meaningful Place Identification Using Call Detail Records as an Alternative to Classical Unsupervised Clustering Techniques

by Manuel Mendoza-Hurtado, Juan A. Romero-del-Castillo and Domingo Ortiz-Boyer

ISPRS Int. J. Geo-Inf. 2024, 13(8), 289; https://doi.org/10.3390/ijgi13080289 - 17 Aug 2024

Viewed by 677

Abstract

Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we [...] Read more.

Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we aim to solve (e.g., home/work identification). This approach allows to tackle place identification from a supervised perspective, offering an alternative to unsupervised clustering techniques. These clustering techniques rely on data characteristics that may not always be directly related to classification objectives. Our results, using mobility data provided by call detail records (CDRs) from Milan, demonstrate superior performance compared to applying clustering techniques. For all types of CDRs, the best results are obtained with the 20 × 20 subgrid, indicating that the model performs better when supplied with information from neighboring cells with a close spatial relationship, establishing neighborhood relationships that allow the model to clearly learn to identify transitions between cells of different types. Considering that it is common for a place or cell to be labeled in multiple categories at once, this supervised approach opens the door to addressing the identification of meaningful places from a multi-label perspective, which is difficult to achieve using classical unsupervised methods. Full article

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19 pages, 3545 KiB

Open AccessArticle

Isochrone-Based Accessibility Analysis of Pre-Hospital Emergency Medical Facilities: A Case Study of Central Districts of Beijing

by Yuan Zhao and Ying Zhou

ISPRS Int. J. Geo-Inf. 2024, 13(8), 288; https://doi.org/10.3390/ijgi13080288 - 16 Aug 2024

Viewed by 677

Abstract

Pre-hospital emergency medical service (PHEMS) is critical for the treatment outcomes of life-threatening injuries and time-sensitive illnesses. Response time, influenced by traffic conditions and the site planning of pre-hospital emergency medical facilities (PHEMFs), is the main indicator for evaluating PHEMS. In 2020, the [...] Read more.

Pre-hospital emergency medical service (PHEMS) is critical for the treatment outcomes of life-threatening injuries and time-sensitive illnesses. Response time, influenced by traffic conditions and the site planning of pre-hospital emergency medical facilities (PHEMFs), is the main indicator for evaluating PHEMS. In 2020, the Beijing government released the “Special Plan for Spatial Layout of Pre-hospital Emergency Medical Facilities in Beijing (2020–2022)”. This paper evaluates the functional efficiency and spatial equity of this plan within Beijing’s central six districts using isochrone measures to assess the accessibility of the planned PHEMFs. The isochrone coverages of the area and population were calculated, and the temporal-spatial characteristics of isochrones were concluded. The analysis revealed that while the current planning meets several objectives, challenges in service availability and equity persist. Although 10-min isochrone coverage was high, 8-min coverage was insufficient, particularly during peak hours. This highlights gaps in service accessibility that necessitate additional emergency stations in underserved areas. The current planning approach leads to significant overlap at administrative boundaries, causing service oversupply and increased costs, which calls for a city-wide planning perspective that breaks administrative boundaries to optimize resource allocation. Traffic conditions significantly impact service coverage, with congestion reducing coverage in central areas and better coverage near traffic hubs. Future planning should strategically place stations based on traffic patterns and population distribution to enhance emergency medical service accessibility and equity in urban areas. Full article

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

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28 pages, 11615 KiB

Open AccessArticle

Identifying the Nonlinear Impacts of Road Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China

by Jiannan Zhao, Changwei Yuan, Xinhua Mao, Ningyuan Ma, Yaxin Duan, Jinrui Zhu, Hujun Wang and Beisi Tian

ISPRS Int. J. Geo-Inf. 2024, 13(8), 287; https://doi.org/10.3390/ijgi13080287 - 16 Aug 2024

Viewed by 776

Abstract

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built [...] Read more.

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. Full article

(This article belongs to the Special Issue Harnessing the Geospatial Data Revolution for Promoting Sustainable Transport Systems)

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20 pages, 6385 KiB

Open AccessArticle

Grid Density Algorithm-Based Second-Hand Housing Transaction Activity and Spatio-Temporal Characterization: The Case of Shenyang City, China

by Jiaqiang Ren and Xiaomeng Gao

ISPRS Int. J. Geo-Inf. 2024, 13(8), 286; https://doi.org/10.3390/ijgi13080286 - 16 Aug 2024

Viewed by 822

Abstract

Second-hand housing transactions constitute a significant segment of the real estate market and are vital for its robust development. The dynamics of these transactions mirror the housing preferences of buyers, and their spatial and temporal analysis elucidates evolving market patterns and buyer behavior. [...] Read more.

Second-hand housing transactions constitute a significant segment of the real estate market and are vital for its robust development. The dynamics of these transactions mirror the housing preferences of buyers, and their spatial and temporal analysis elucidates evolving market patterns and buyer behavior. This study introduces an innovative grid density clustering algorithm, dubbed the RScan algorithm, which integrates Bayesian optimization with grid density techniques. This composite methodology is employed to assess clustering outcomes, optimize hyperparameters, and facilitate detailed visualization and analysis of transaction activity across various regions. Focusing on Shenyang, a major urban center in Northeast China, the research spans from 2018 to 2023, exploring the second-hand housing transaction activity and its spatio-temporal attributes. The results reveal temporal fluctuations in transaction intensity across different Shenyang regions, although core areas of high activity remain constant. These regions display a heterogeneous pattern of irregularly stepped and clustered distributions, with a notable absence of uniformly high-activity zones. This study pioneers a novel methodological framework for investigating second-hand housing transactions, offering crucial insights for market development and policy formulation in Shenyang. Full article

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21 pages, 28451 KiB

Open AccessArticle

Automatic Functional Classification of Buildings Supported by a POI Semantic Characterization Knowledge Graph

by Youneng Su, Qing Xu, Xinming Zhu, Fubing Zhang and Yi Liu

ISPRS Int. J. Geo-Inf. 2024, 13(8), 285; https://doi.org/10.3390/ijgi13080285 - 15 Aug 2024

Viewed by 959

Abstract

The division of urban functional zones is crucial for understanding urban characteristics and aiding in urban management and planning. Traditional methods, like dividing based on blocks and grids, are insufficient for modern demands. To address this, a knowledge-graph-supported method for building functional category [...] Read more.

The division of urban functional zones is crucial for understanding urban characteristics and aiding in urban management and planning. Traditional methods, like dividing based on blocks and grids, are insufficient for modern demands. To address this, a knowledge-graph-supported method for building functional category division is proposed. Firstly, the associations between points of interest (POI) and buildings are established using triangulation and buffer zones. Then, a knowledge graph of buildings is constructed through entity and relationship extraction. A functional category classification model supported by the Z-score is designed using the semantic characterizations of surrounding POIs for inference rules. The results demonstrate high accuracy in building functional category division, supporting the refinement and intelligent expression of urban functional zones for urban construction, planning, and management. Full article

(This article belongs to the Topic Geospatial Knowledge Graph)

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2 pages, 613 KiB

Open AccessCorrection

Correction: Agriesti et al. Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data. ISPRS Int. J. Geo-Inf. 2022, 11, 148

by Serio Agriesti, Claudio Roncoli and Bat-hen Nahmias-Biran

ISPRS Int. J. Geo-Inf. 2024, 13(8), 284; https://doi.org/10.3390/ijgi13080284 - 13 Aug 2024

Viewed by 497

Abstract

In the original publication [...] Full article

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22 pages, 5978 KiB

Open AccessArticle

City Transmission Networks: Unraveling Disease Spread Dynamics

by Hend Alrasheed, Norah Alballa, Isra Al-Turaiki, Fahad Almutlaq and Reham Alabduljabbar

ISPRS Int. J. Geo-Inf. 2024, 13(8), 283; https://doi.org/10.3390/ijgi13080283 - 12 Aug 2024

Viewed by 1022

Abstract

In the midst of global efforts to curb the spread of infectious diseases, researchers worldwide are striving to unravel the intricate spatial and temporal patterns of disease transmission dynamics. Mathematical models are indispensable tools for understanding the dissemination of emerging pathogens and elucidating [...] Read more.

In the midst of global efforts to curb the spread of infectious diseases, researchers worldwide are striving to unravel the intricate spatial and temporal patterns of disease transmission dynamics. Mathematical models are indispensable tools for understanding the dissemination of emerging pathogens and elucidating the evolution of epidemics. This paper introduces a novel approach by investigating city transmission networks as a framework for analyzing disease spread. In this network, major cities are depicted as nodes interconnected by edges representing disease transmission pathways. Subsequent network analysis employs various epidemiological and structural metrics to delineate the distinct roles played by cities in disease transmission. The primary objective is to identify superspreader cities. Illustratively, we apply this methodology to study COVID-19 transmission in Saudi Arabian cities, shedding light on the specific dynamics within this context. These insights offer valuable guidance for decision-making processes and the formulation of effective intervention strategies, carrying significant implications for managing public health crises. Full article

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

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21 pages, 5848 KiB

Open AccessArticle

What Factors Revitalize the Street Vitality of Old Cities? A Case Study in Nanjing, China

by Yan Zheng, Ruhai Ye, Xiaojun Hong, Yiming Tao and Zherui Li

ISPRS Int. J. Geo-Inf. 2024, 13(8), 282; https://doi.org/10.3390/ijgi13080282 - 12 Aug 2024

Cited by 1 | Viewed by 1033

Abstract

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) [...] Read more.

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities. Full article

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

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19 pages, 1891 KiB

Open AccessArticle

Efficient and Verifiable Range Query Scheme for Encrypted Geographical Information in Untrusted Cloud Environments

by Zhuolin Mei, Jing Zeng, Caicai Zhang, Shimao Yao, Shunli Zhang, Haibin Wang, Hongbo Li and Jiaoli Shi

ISPRS Int. J. Geo-Inf. 2024, 13(8), 281; https://doi.org/10.3390/ijgi13080281 - 11 Aug 2024

Viewed by 784

Abstract

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners [...] Read more.

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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20 pages, 553 KiB

Open AccessArticle

Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems

by Mingjia Han, Xinyi Yang, Huachang Su, Yekang Zhao, Ding Huang and Yongjun Ren

ISPRS Int. J. Geo-Inf. 2024, 13(8), 280; https://doi.org/10.3390/ijgi13080280 - 10 Aug 2024

Viewed by 780

Abstract

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an [...] Read more.

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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22 pages, 33758 KiB

Open AccessArticle

Ecological Network Construction Based on Red, Green and Blue Space: A Case Study of Dali City, China

by Rong Chen, Shunmin Zhang, Xiaoyuan Huang, Xiang Li and Jiansong Peng

ISPRS Int. J. Geo-Inf. 2024, 13(8), 279; https://doi.org/10.3390/ijgi13080279 - 7 Aug 2024

Viewed by 1229

Abstract

Rapid urbanization leads to fragmentation and reduced connectivity of urban landscapes, endangering regional biodiversity conservation and sustainable development. Constructing a red, green, and blue spatial ecological network is an effective way to alleviate ecological pressure and promote economic development. Using circuit theory, hydrological [...] Read more.

Rapid urbanization leads to fragmentation and reduced connectivity of urban landscapes, endangering regional biodiversity conservation and sustainable development. Constructing a red, green, and blue spatial ecological network is an effective way to alleviate ecological pressure and promote economic development. Using circuit theory, hydrological analysis, and suitability analysis, this study constructs a composite ecological network under urban–rural integration. The results show the following: (1) A total of 22 ecological corridors with a length of 349.20 km, 22 ecological pinch points, and 22 ecological barrier points are identified in the municipal area, mainly distributed in Haidong Town. There are 504 stormwater corridors, which are more evenly distributed, 502 riverfront landscape corridors, and 130 slow-moving landscape corridors. (2) A total of 20 ecological corridors, with a length of 99.23 km, 19 ecological pinch points, and 25 barrier points were identified in the main urban area, and most of them are located in the ecological corridors. There are 71 stormwater corridors, mainly located in the northwestern forest area, 71 riverfront recreation corridors, and 50 slow-moving recreation corridors. (3) Two scales of superimposed ecological source area of 3.65 km², and eleven ecological corridors, are primarily distributed between Erhai Lake and Xiaguan Town. There are two superimposed stormwater corridors and fourteen recreational corridors. The eco-nodes are mostly distributed in the east and south of Dali City; wetland nodes are mainly situated in the eighteen streams of Cangshan Mountain; and landscape nodes are more balanced in spatial distribution. The study results can provide a reference for composite ecological network construction. Full article

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

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16 pages, 19703 KiB

Open AccessArticle

A Maximal Multimodal Accessibility Equality Model to Optimize the Equality of Healthcare Services

by Zhuolin Tao, Qianyu Zhong and Yinuo Dang

ISPRS Int. J. Geo-Inf. 2024, 13(8), 278; https://doi.org/10.3390/ijgi13080278 - 7 Aug 2024

Viewed by 859

Abstract

The equality of healthcare services has been a focus among researchers and policymakers. The maximal accessibility equality (MAE) model is a widely used location-allocation model for the optimization of the accessibility equality of facilities. However, it might produce biased results due to the [...] Read more.

The equality of healthcare services has been a focus among researchers and policymakers. The maximal accessibility equality (MAE) model is a widely used location-allocation model for the optimization of the accessibility equality of facilities. However, it might produce biased results due to the overlooking of multiple transport mode options for urban residents. This study develops a maximal multimodal accessibility equality (MMAE) model by incorporating the multimodal two-step floating catchment area (2SFCA) accessibility model. It reflects the multimodal context in cities and aims to maximize the equality of multimodal accessibility. A case study of healthcare facilities in Shenzhen demonstrates that the proposed MMAE model can significantly improve the equality of multimodal accessibility. However, the traditional single-modal MAE model generates unequal multimodal accessibility, which might yield biased planning recommendations in multimodal contexts. The findings highlight the superiority of the MMAE model against the traditional single-modal MAE model in terms of pursuing equal accessibility for all residents. The MMAE model can serve as a scientific tool to support the rational planning of healthcare facilities or other types of public facilities in multimodal contexts. Full article

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29 pages, 19449 KiB

Open AccessArticle

Influencing Factors of Street Vitality in Historic Districts Based on Multisource Data: Evidence from China

by Bing Yu, Jing Sun, Zhaoxing Wang and Sanfeng Jin

ISPRS Int. J. Geo-Inf. 2024, 13(8), 277; https://doi.org/10.3390/ijgi13080277 - 5 Aug 2024

Viewed by 1312

Abstract

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside [...] Read more.

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts. Full article

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

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21 pages, 6444 KiB

Open AccessArticle

DCPMS: A Large-Scale Raster Layer Serving Method for Custom Online Calculation and Rendering

by Anbang Yang, Feng Zhang, Jie Feng, Luoqi Wang, Enjiang Yue, Xinhua Fan, Jingyi Zhang, Linshu Hu and Sensen Wu

ISPRS Int. J. Geo-Inf. 2024, 13(8), 276; https://doi.org/10.3390/ijgi13080276 - 1 Aug 2024

Viewed by 892

Abstract

Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of [...] Read more.

Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of the key issues in the development of GIS technology is to determine how to make large-scale raster data better to provide computation, visualization, and analysis services in the Internet environment. This paper proposes a decentralized COG-pyramid-based map service method (DCPMS). In comparison to traditional raster data online service technology, such as GIS servers and static tiles, DCPMS employs virtual mapping to reduce data storage costs and combines tile technology with a cloud-native storage scheme to enhance the concurrency of supportable requests. Furthermore, the band calculation process is shifted to the client, thereby effectively resolving the issue of efficient customized band calculation and data rendering in the context of a large-scale raster data online service. The results indicate DCPMS delivers commendable performance. Its decentralized architecture significantly enhances performance in high concurrency scenarios. With a thousand concurrent requests, the response time of DCPMS is reduced by 74% compared to the GIS server. Moreover, this service exhibits considerable strengths in data preprocessing and storage, suggesting a novel pathway for future technical improvement of large-scale raster data map services. Full article

(This article belongs to the Topic Remote Sensing and Visualization Methods: Monitoring, Modeling, Simulations and Interaction of Forest Resource)

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17 pages, 5460 KiB

Open AccessArticle

Factors Influencing the Efficiency of Demand-Responsive Transport Services in Rural Areas: A GIS-Based Method for Optimising and Evaluating Potential Services

by Carlos Tejero-Beteta, Amparo Moyano and Santos Sánchez-Cambronero

ISPRS Int. J. Geo-Inf. 2024, 13(8), 275; https://doi.org/10.3390/ijgi13080275 - 1 Aug 2024

Viewed by 886

Abstract

Demand-responsive transport (DRT) could be an alternative for extending the accessibility of high-speed rail (HSR) servicing cities in rural environments, where fixed public transport does not provide efficient services. This paper proposes a method to analyse the factors that influence the implementation of [...] Read more.

Demand-responsive transport (DRT) could be an alternative for extending the accessibility of high-speed rail (HSR) servicing cities in rural environments, where fixed public transport does not provide efficient services. This paper proposes a method to analyse the factors that influence the implementation of DRT systems for inter-urban mobility, connecting and integrating towns in rural areas. Methodologically, a vehicle routing problem analysis in a GIS-based environment is applied to a theoretical case study to evaluate the factors that influence DRT efficiency in different scenarios, considering the specific singularities of this kind of inter-urban long-distance mobility. The results suggest the optimal DRT solutions in these rural contexts to be those that, after adjusting the fleet to specific demands, use low-capacity vehicles, which are much better adapted to the geography of sparsely populated areas. Moreover, in adapting DRT systems to HSR travellers’ needs, windows catering to these needs should incorporate the option of setting the pickup or arrival times. This paper demonstrates that DRT systems could reach significant levels of service in rural areas compared with fixed lines and even private vehicles, especially when evaluating key aspects of the system’s efficiency for its implementation. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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21 pages, 2408 KiB

Open AccessArticle

BS-GeoEduNet 1.0: Blockchain-Assisted Serverless Framework for Geospatial Educational Information Networks

by Meenakshi Kandpal, Veena Goswami, Yash Pritwani, Rabindra K. Barik and Manob Jyoti Saikia

ISPRS Int. J. Geo-Inf. 2024, 13(8), 274; https://doi.org/10.3390/ijgi13080274 - 1 Aug 2024

Viewed by 864

Abstract

The integration of a blockchain-supported serverless computing framework enhances the performance of computational and analytical operations and the provision of services within internet-based data centers, rather than depending on independent desktop computers. Therefore, in the present research paper, a blockchain-assisted serverless framework for [...] Read more.

The integration of a blockchain-supported serverless computing framework enhances the performance of computational and analytical operations and the provision of services within internet-based data centers, rather than depending on independent desktop computers. Therefore, in the present research paper, a blockchain-assisted serverless framework for geospatial data visualizations is implemented. The proposed BS-GeoEduNet 1.0 framework leverages the capabilities of AWS Lambda for serverless computing, providing a reliable and efficient solution for data storage, analysis, and distribution. The proposed framework incorporates AES encryption, decryption layers, and queue implementation to achieve a scalable approach for handling larger files. It implements a queueing mechanism during the heavier input/output processes of file processing by using Apache KAFKA, enabling the system to handle large volumes of data efficiently. It concludes with the visualization of all geospatial-enabled NIT/IIT details on the proposed framework, which utilizes the data fetched from MongoDB. The experimental findings validate the reliability and efficiency of the proposed system, demonstrating its efficacy in geospatial data storage and processing. Full article

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23 pages, 4406 KiB

Open AccessArticle

The Influence of Proximity on the Evolution of Urban Innovation Networks in Nanjing Metropolitan Area, China: A Comparative Analysis of Knowledge and Technological Innovations

by Yu Shi, Wei Zhai, Yiran Yan and Xingping Wang

ISPRS Int. J. Geo-Inf. 2024, 13(8), 273; https://doi.org/10.3390/ijgi13080273 - 1 Aug 2024

Viewed by 1092

Abstract

This study investigates the dynamics of innovation element flows among metropolitan areas and examines the underlying proximity mechanisms that are crucial for elevating urban agglomerations’ innovation levels and spurring their development. Utilizing collaborative publication and patent data, this research constructs knowledge and technological [...] Read more.

This study investigates the dynamics of innovation element flows among metropolitan areas and examines the underlying proximity mechanisms that are crucial for elevating urban agglomerations’ innovation levels and spurring their development. Utilizing collaborative publication and patent data, this research constructs knowledge and technological innovation networks within the Nanjing metropolitan area (NMA) from 2013 to 2020. It analyzes the evolution of network structures and applies the Multiple Regression Quadratic Assignment Procedure to discern the proximity mechanisms driving the urban innovation networks’ evolution in NMA. The main findings are as follows: (1) The knowledge collaborations within NMA cities remain largely confined to cities within Jiangsu province, whereas the technological collaborations are shifting from intra-province to cross-province cooperation. (2) Both knowledge and technological innovation networks display a “core-periphery” configuration, with Nanjing maintaining a dominant central position. The scale of the KIN surpasses that of the TIN, while the latter’s growth rate outpaces the former’s. Technological collaborations demonstrate more pronounced spillover effects than their knowledge counterparts. (3) At the metropolitan area level, organizational, social, cognitive, and technological proximities exert varying degrees of influence on innovation cooperation among different innovation entities across various years. Cognitive proximity exhibits the most substantial explanatory power. Based on these findings, the study proposes relevant policy recommendations for constructing an innovative NMA and promoting collaborative innovation development among cities within the NMA. Full article

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23 pages, 8631 KiB

Open AccessArticle

Analysis of Road Safety Perception and Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example

by Xinyu Hou and Peng Chen

ISPRS Int. J. Geo-Inf. 2024, 13(8), 272; https://doi.org/10.3390/ijgi13080272 - 31 Jul 2024

Cited by 1 | Viewed by 939

Abstract

Measuring human perception of environmental safety and quantifying the street view elements that affect human perception of environmental safety are of great significance for improving the urban environment and residents’ safety perception. However, domestic large-scale quantitative research on the safety perception of Chinese [...] Read more.

Measuring human perception of environmental safety and quantifying the street view elements that affect human perception of environmental safety are of great significance for improving the urban environment and residents’ safety perception. However, domestic large-scale quantitative research on the safety perception of Chinese local cities needs to be deepened. Therefore, this paper chooses Chaoyang District in Beijing as the research area. Firstly, the network safety perception distribution of Chaoyang District is calculated and presented through the CNN model trained based on the perception dataset constructed by Chinese local cities. Then, the street view elements are extracted from the street view images using image semantic segmentation and target detection technology. Finally, the street view elements that affect the road safety perception are identified and analyzed based on LightGBM and SHAP interpretation framework. The results show the following: (1) the overall safety perception level of Chaoyang District in Beijing is high; (2) the number of motor vehicles and the proportion of the area of roads, skies, and sidewalks are the four factors that have the greatest impact on environmental safety perception; (3) there is an interaction between different street view elements on safety perception, and the proportion and number of street view elements have interaction on safety perception; (4) in the sections with the lowest, moderate, and highest levels of safety perception, the influence of street view elements on safety perception is inconsistent. Finally, this paper summarizes the results and points out the shortcomings of the research. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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19 pages, 2205 KiB

Open AccessArticle

A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

by Shuguang Deng, Wei Liu, Ying Peng and Binglin Liu

ISPRS Int. J. Geo-Inf. 2024, 13(8), 271; https://doi.org/10.3390/ijgi13080271 - 31 Jul 2024

Viewed by 914

Abstract

Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), [...] Read more.

Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy. Full article

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29 pages, 6065 KiB

Open AccessArticle

Challenges to Viticulture in Montenegro under Climate Change

by António Fernandes, Nataša Kovač, Hélder Fraga, André Fonseca, Sanja Šućur Radonjić, Marko Simeunović, Kruna Ratković, Christoph Menz, Sergi Costafreda-Aumedes and João A. Santos

ISPRS Int. J. Geo-Inf. 2024, 13(8), 270; https://doi.org/10.3390/ijgi13080270 - 30 Jul 2024

Cited by 1 | Viewed by 996

Abstract

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. [...] Read more.

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)

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15 pages, 7593 KiB

Open AccessArticle

Multi-Criteria Decision Analysis to Evaluate the Geographic Potential of Alternative Photovoltaic Types

by Franziska Hübl, Franz Welscher and Johannes Scholz

ISPRS Int. J. Geo-Inf. 2024, 13(8), 269; https://doi.org/10.3390/ijgi13080269 - 30 Jul 2024

Viewed by 1239

Abstract

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context [...] Read more.

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km². The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential. Full article

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24 pages, 8859 KiB

Open AccessArticle

The Spatial Equilibrium Model of Elderly Care Facilities with High Spatiotemporal Sensitivity and Its Economic Associations Study

by Hongyan Li, Rui Li, Jing Cai and Shunli Wang

ISPRS Int. J. Geo-Inf. 2024, 13(8), 268; https://doi.org/10.3390/ijgi13080268 - 27 Jul 2024

Viewed by 833

Abstract

The global population aging poses new challenges in allocating care facilities for the elderly. This demographic trend also influences economic development and the quality of urban life. However, current research focuses on the supply of elderly care facilities and primarily uses administrative divisions [...] Read more.

The global population aging poses new challenges in allocating care facilities for the elderly. This demographic trend also influences economic development and the quality of urban life. However, current research focuses on the supply of elderly care facilities and primarily uses administrative divisions as a scale, resulting in low spatiotemporal sensitivity in evaluating the spatial equilibrium of elderly care facilities (SEECF). The relationship between the SEECF and economic development is not clear. In response to these problems, we proposed a spatial equilibrium model of elderly care facilities with high spatiotemporal sensitivity (SEM-HSTS) and explored the spatiotemporal associations between the SEECF and economic development. Considering the spatial accessibility rate of elderly care services (SARecs) and the spatiotemporal supply–demand ratio for elderly care services (STSDRecs), two types of supply–demand relationship factors were constructed. Then, a spatiotemporal accessibility of medical services (STAms) factor was obtained based on a modified two-step floating catchment area (M2SFCA) method. On this basis, the SEM-HSTS was constructed based on the theory of coordinated development. Further, a panel threshold model was employed to evaluate the influence relationships among population aging, SEECF, and gross domestic product (GDP) in different phases. Finally, spatial autocorrelation and Geodetector explored the spatial associations between SEECF and GDP across complex urban land use categories (ULUC). The experimental results at a 100-m grid scale showed that the SEM-HSTS exhibited higher spatiotemporal heterogeneity than the classical accessibility method, with elevated spatiotemporal sensitivity. Effectively identified various spatial imbalances, such as undersupply and resource waste. The panel model captured phased relationship changes, showing that SEECF had inhibitory and promotional effects on GDP in pre- and post-aging societies, with stronger effects as balance approached. Moreover, the combined interaction of ULUC and GDP had a more significant influence on SEECF than any individual factor, with GDP exerting a more significant influence. This study provides an empirical basis for creating resource-efficient elderly care facility systems and optimizing layouts. Full article

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ISPRS Int. J. Geo-Inf., Volume 13, Issue 8 (August 2024) – 35 articles

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