Journal Description
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information
is an international, peer-reviewed, open access journal on geo-information. The journal is owned by the International Society for Photogrammetry and Remote Sensing (ISPRS) and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), GeoRef, PubAg, dblp, Astrophysics Data System, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Geography, Physical) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 35.5 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.5 (2022)
Latest Articles
Research on the Geographical Pattern, Evolution Model, and Driving Mechanism of Carbon Emission Density from Urban Industrial Land in the Yangtze River Economic Belt of China
ISPRS Int. J. Geo-Inf. 2024, 13(6), 192; https://doi.org/10.3390/ijgi13060192 (registering DOI) - 8 Jun 2024
Abstract
To achieve the goals of “carbon peaking and carbon neutrality”, this paper puts forward the connotation and measurement method for the carbon emission intensity of urban industrial land and conducts an empirical study with the Yangtze River Economic Belt (YREB) as an example.
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To achieve the goals of “carbon peaking and carbon neutrality”, this paper puts forward the connotation and measurement method for the carbon emission intensity of urban industrial land and conducts an empirical study with the Yangtze River Economic Belt (YREB) as an example. We defined the carbon intensity of urban industrial land as the industrial carbon emissions per unit area of land, which is a spatial mapping of urban industrial economic development and carbon spillover and a key indicator for urban and territorial spatial planning oriented towards the “dual carbon” goal. Findings: The carbon emission density of industrial land in the YREB varied greatly between cities and exhibited significant positive spatial autocorrelation. In addition, the geographical pattern and spatio-temporal evolution model of the urban industrial land carbon emission density had a very complex driving mechanism, and different factors had significant synergistic effects. Therefore, it is suggested that while striving towards the goal of “dual carbon”, the government should incorporate the carbon emission density indicator of urban industrial land into the urban and territorial spatial planning system, and based on the threshold of the medium suitable density, they should design differentiated management policies according to concrete urban policies and encourage cooperation among cities to jointly promote carbon emission management of urban industrial land. In policy design, emphasis should also be placed on highlighting the interactive effects of foreign direct investment, fiscal expenditure, and the number of patent authorizations as well as constructing a combination of policies centered around them to better leverage the impacts of globalization, government intervention, and innovation.
Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
Open AccessArticle
Dry–Wet Changes in a Typical Agriculture and Pasture Ecotone in China between 1540 and 2019
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Xiaodong Wang, Yujia Song, Yu An, Xiaohui Liu and Xiaoqiang Li
ISPRS Int. J. Geo-Inf. 2024, 13(6), 191; https://doi.org/10.3390/ijgi13060191 - 7 Jun 2024
Abstract
Exploring periodic dry–wet changes is an important topic in climate change research due to its impact on drought and flood disasters. The purpose of this research was to determine the occurrence law of dry–wet changes in China on a scale of several hundred
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Exploring periodic dry–wet changes is an important topic in climate change research due to its impact on drought and flood disasters. The purpose of this research was to determine the occurrence law of dry–wet changes in China on a scale of several hundred years, using the example of transitional zones. In this study, we analyzed typical areas of the ecotone between agricultural land and pasture along the Great Wall of China. The ring width index of Carya cathayensis was fitted with the March–August Palmer drought severity index (PDSI38). The PDSI38 was divided into different periods using the stepwise function fitting method. The results indicated that there were two dry periods and one wet period in the region from 1543 to 2019. In each dry and wet period, there were also different temporal periods, including long (decades), intermediate (ten years), and short periods (several years). Drought represents a significant threat to agricultural production in China. In the first dry period (1543–1756), four periods with low PDSI38 values (1633–1635, PDSI38 = −1.71; 1636–1939, PDSI38 = −3.35; 1640–1642, PDSI38 = −4.68; and 1643–1645, PDSI38 = −2.92) occurred, during which severe droughts (PDSI38 < −4) lasted for 13 years. The dry–wet change showed the characteristics of a 12-year or multiple 12-year cycle. The results can be used to prepare to effectively address extreme drought scenarios worldwide in the future.
Full article
(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)
Open AccessArticle
Analysing the Spatio-Temporal Variations of Urban Street Summer Solar Radiation through Historical Street View Images: A Case Study of Shanghai, China
by
Lei Wang, Longhao Zhang and Jie He
ISPRS Int. J. Geo-Inf. 2024, 13(6), 190; https://doi.org/10.3390/ijgi13060190 - 7 Jun 2024
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Understanding solar radiation in urban street spaces is crucial for comprehending residents’ environmental experiences and enhancing their quality of life. However, existing studies rarely focus on the patterns of urban street solar radiation over time and across different urban and suburban areas. In
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Understanding solar radiation in urban street spaces is crucial for comprehending residents’ environmental experiences and enhancing their quality of life. However, existing studies rarely focus on the patterns of urban street solar radiation over time and across different urban and suburban areas. In this study, street view images from the summers of 2013 and 2019 in Shanghai were used to calculate solar radiation in urban street spaces. The results show a general decrease in street solar radiation in 2019 compared to 2013, with an average drop of 12.34%. The decrease was most significant in October (13.47%) and least in May (11.71%). In terms of solar radiation data gathered from street view sampling points, 76.57% showed a decrease, while 23.43% showed an increase. Spatially, solar radiation decreased by 79.66% for every additional 1.5 km from the city centre. In summary, solar radiation generally shows a decreasing trend, with significant variations between different areas. These findings are vitally important for guiding urban planning, optimising green infrastructure, and enhancing the urban ecological environment, further promoting sustainable urban development and improving residents’ quality of life.
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Open AccessArticle
Research on Rural Environments’ Effects on Well-Being: The Huizhou Area in China
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Xingmeng Ma, Xin Su, Yanlong Guo and Linfu Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(6), 189; https://doi.org/10.3390/ijgi13060189 - 6 Jun 2024
Abstract
The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat
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The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat Environment (RHES) Indicator Program is based on the concept of Socio-Economic-Natural Complex Ecosystems (SENCE) and constructs 18 metrics in three dimensions. Trends and influencing factors were analyzed using entropy weight TOPSIS and a Grey Relational Analysis (GRA) for the years 2013–2022, and spatial and temporal evolution was measured using Geographic Information Systems (GISs). The findings show that the composite index for the Huizhou region grew from 2013 (0.3197) to 2022 (0.6806). Second, the Tunxi District belongs to the high index–high economy category. The Shexian, Xiuning, and Qimen counties belong to the high index–low economy category. Huizhou District and Huangshan District belong to the low index–high economy category. Yixian County belongs to the low index–low economy category. Third, all districts (counties) show an upward trend, and Huangshan District has the best RHES condition. Shexian County ranks relatively low in the comprehensive index.
Full article
(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
Open AccessArticle
Identifying the Hierarchical Structure of Nighttime Economic Agglomerations Based on the Fusion of Multisource Data
by
Weijie Wan, Hongfei Chen, Xiping Yang, Renda Li, Yuzheng Cui and Yiyang Hu
ISPRS Int. J. Geo-Inf. 2024, 13(6), 188; https://doi.org/10.3390/ijgi13060188 - 6 Jun 2024
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Nighttime economic development is an important driving force in urban economic development, and identification of the levels and boundary ranges of nighttime economic agglomerations is an important part of the management of the nighttime economy. Previous studies have been limited by the use
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Nighttime economic development is an important driving force in urban economic development, and identification of the levels and boundary ranges of nighttime economic agglomerations is an important part of the management of the nighttime economy. Previous studies have been limited by the use of a single data source to identify nighttime economic agglomerations. To address this limitation, multisource data fusion was used in this study to integrate nighttime lighting data, point of interest data, and check-in data and to assess the nighttime economy more comprehensively from the perspectives of both providers and receivers in the nighttime economy. To identify the hierarchical structure and boundaries of nighttime economic agglomerations accurately, a two-step method was used to identify local hotspots of the nighttime economy, divide the nighttime economic agglomerations into levels, and explore the spatial distribution and functional characteristics of different levels of nighttime economic zones. Comparative experiments showed the method used in this study to be rational and accurate. The methods and results of this study can provide a more comprehensive approach to the precise identification of nighttime economic agglomerations and guidance for the future planning, rational development, and management of nighttime economic agglomerations.
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Open AccessArticle
Simplifying Land Cover-Geoprocessing-Model Migration with a PAMC-LC Containerization Strategy in the Open Web Environment
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Huaqiao Xing, Haihang Wang, Denghai Gao, Dongyang Hou and Huayi Wu
ISPRS Int. J. Geo-Inf. 2024, 13(6), 187; https://doi.org/10.3390/ijgi13060187 - 3 Jun 2024
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Land cover and its changes over time are significant for better understanding the Earth’s fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different
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Land cover and its changes over time are significant for better understanding the Earth’s fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs.
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Open AccessArticle
Interpretation of Hot Spots in Wuhan New Town Development and Analysis of Influencing Factors Based on Spatio-Temporal Pattern Mining
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Haijuan Zhao, Yan Long, Nina Wang, Shiqi Luo, Xi Liu, Tianyue Luo, Guoen Wang and Xuejun Liu
ISPRS Int. J. Geo-Inf. 2024, 13(6), 186; https://doi.org/10.3390/ijgi13060186 - 3 Jun 2024
Abstract
The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of
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The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan’s new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran’s index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
A Novel Rock Mass Discontinuity Detection Approach with CNNs and Multi-View Image Augmentation
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Ilyas Yalcin, Recep Can, Candan Gokceoglu and Sultan Kocaman
ISPRS Int. J. Geo-Inf. 2024, 13(6), 185; https://doi.org/10.3390/ijgi13060185 - 31 May 2024
Abstract
Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities
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Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities without direct contact with rock masses. This study proposes a new approach to detect discontinuities using close-range photogrammetric techniques and convolutional neural networks (CNNs) trained on a small amount of data. Investigations were conducted on basalts in Bala, Ankara, Türkiye. A total of 34 multi-view images were collected with a remotely piloted aircraft system (RPAS), and discontinuity lines were manually delineated on a point cloud generated from these images. The lines were back-projected onto the raw images to increase the amount of data, a process we call multi-view (3D) augmentation. We further evaluated radiometric and geometric augmentation methods, the contribution of multi-view augmentation to the proposed model, and the transfer learning performance of six different CNN architectures. The highest performance was achieved with U-Net + SE-ResNeXt-50 with an F1-score of 90.6%. The CNN model trained from scratch with local features also yielded a similar F1-score (91.7%), which is the highest performance reported in the literature.
Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)
Open AccessArticle
Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data
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Xudong Zheng, Mengyu Ma, Zhinong Zhong, Anran Yang, Luo Chen and Ning Jing
ISPRS Int. J. Geo-Inf. 2024, 13(6), 184; https://doi.org/10.3390/ijgi13060184 - 31 May 2024
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In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path
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In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path planning algorithm. First, an improved Probabilistic Roadmap (PRM) algorithm is used to quickly and roughly determine the initial path. Then, the morphological dilation is applied to process the grid points of the initial path, retaining the surrounding area of the initial path for a precise positioning of the search range. Finally, the idea of the A∗ algorithm is applied to achieve precise path planning in the refined search range. During the process of constructing the topology map, we utilized parallelization acceleration strategies to expedite the graph construction. In order to verify the effectiveness of the algorithm, we used terrain data to construct a wilderness environment model, and tests were conducted on off-road path planning tasks with different terrains and distances. The experimental results show a substantial enhancement in the computational efficiency of the proposed algorithm relative to the conventional A∗ algorithm by 30 to 60 times.
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Open AccessArticle
A New Method Based on Lattice Boltzmann Method and Unsupervised Clustering for Identification of Urban-Scale Ventilation Corridors
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Tianyu Li and Peng Xie
ISPRS Int. J. Geo-Inf. 2024, 13(6), 183; https://doi.org/10.3390/ijgi13060183 - 31 May 2024
Abstract
With the increase in urban development intensity, the urban climate has become an important factor affecting sustainable development. The role of urban ventilation corridors in improving urban climate has received widespread attention. Urban ventilation identification and planning based on morphological methods have been
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With the increase in urban development intensity, the urban climate has become an important factor affecting sustainable development. The role of urban ventilation corridors in improving urban climate has received widespread attention. Urban ventilation identification and planning based on morphological methods have been initially applied. Traditional morphological methods do not adequately consider the dynamic process of air flow, resulting in a rough evaluation of urban ventilation patterns. This study proposes a new urban-scale ventilation corridor identification method that integrates the Lattice Boltzmann method and the K-means algorithm. Taking Wuhan, China as the research area, an empirical study in different wind directions was conducted on a 20 m grid. The results showed that three levels of ventilation corridors ( ) and two levels of ventilation obstruction areas ( ) were identified to depict the ventilation pattern of Wuhan’s central urban area. The method proposed in this study can meet the needs of urban-scale ventilation corridor identification in terms of spatial coverage, spatial distribution rate and dynamic analysis. Compared with the classic least cumulative ventilation cost method, the method proposed in this study can provide more morphologic details of the ventilation corridors. This plays a very important role in urban planning based on urban ventilation theory.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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Open AccessArticle
Exploring the Effects of Light and Dark on Crime in London
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Ezgi Erturk, Peter Raynham and Jemima Unwin Teji
ISPRS Int. J. Geo-Inf. 2024, 13(6), 182; https://doi.org/10.3390/ijgi13060182 - 30 May 2024
Abstract
Safety from crime is a fundamental human need. In Maslow’s hierarchy, safety is one of the foundational needs of well-being. The built environment should be safe to use at all times of the day and for all groups of people. After dark, the
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Safety from crime is a fundamental human need. In Maslow’s hierarchy, safety is one of the foundational needs of well-being. The built environment should be safe to use at all times of the day and for all groups of people. After dark, the appearance of the outdoor environment changes dramatically, and this could impact the opportunities for crime. This study investigated the impact of daylight on the rates of different types of crime by comparing the crime rates during selected periods of daylight and darkness. The study used records of crime data from the Metropolitan Police Service. By studying crimes in the week on either side of the twice-yearly clock change, it is possible to compare periods that are dark in one week and light in the other at the same clock time. Where the time at which the crime took place was known, and using the GPS coordinates of the specific crime, the solar altitude was calculated and used to determine if it was light or dark at the time of the crime. A similar calculation was used to see if the crime would have been in the dark or light in the week on the other side of the clock change. The headline result is that there was 4.8% (OR 1.07) more crime in the dark periods than the light ones. However, this increase was not uniform across all crime types, and there were some further complications in some results due to potential changes in the behavior of some victims after dark. For the crimes of theft from a person and robbery of personal property, there was a significant increase during the dark period. The availability of light had an impact on the rate of certain crimes. Whilst this does not provide any information about the impact of street lighting on crime, it does provide some idea of by how much crime could be reduced if better lighting was provided.
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(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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Open AccessArticle
Annual and Seasonal Dynamics of CO2 Emissions in Major Cities of China (2019–2022)
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Yue Zhao, Yuning Feng, Mingyi Du and Klaus Fraedrich
ISPRS Int. J. Geo-Inf. 2024, 13(6), 181; https://doi.org/10.3390/ijgi13060181 - 29 May 2024
Abstract
To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator
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To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.
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(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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Open AccessArticle
Supplementary Dam Site Selection Using a Geospatial Approach: A Case Study of Wivenhoe Dam
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Aseel Zytoon, Zahra Gharineiat and Omar Alajarmeh
ISPRS Int. J. Geo-Inf. 2024, 13(6), 180; https://doi.org/10.3390/ijgi13060180 - 29 May 2024
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Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a geospatial approach to assess the feasibility
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Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a geospatial approach to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mitigating floods and preventing overtopping failure at Wivenhoe Dam. Using QGIS software and a 25 m resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied on several dam wall options to identify the cost-effective and optimal one based on the dam wall dimensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria yielding the optimal choice. The study provides insights into the feasibility and effectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation plans for the asset owners.
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Open AccessArticle
Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou City
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Wenda Wang, Xiao Li, Ting Wang, Shaohua Wang, Runqiao Wang, Dachuan Xu and Junyuan Zhou
ISPRS Int. J. Geo-Inf. 2024, 13(6), 179; https://doi.org/10.3390/ijgi13060179 - 29 May 2024
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With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study
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With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions.
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Open AccessArticle
Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace
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Shiyuan Cheng, Jianchen Zhang, Guangxia Wang, Zheng Zhou, Jin Du, Lijun Wang, Ning Li and Jiayao Wang
ISPRS Int. J. Geo-Inf. 2024, 13(6), 178; https://doi.org/10.3390/ijgi13060178 - 29 May 2024
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Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps.
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Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013–2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies’ origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations.
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Open AccessArticle
Analyzing the Problems of a District-Based Administration Using Monte Carlo Simulation: The Case of Sex Offender Notifications in Korea
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Hyemin Kim, Suyun Lee and Chulmin Jun
ISPRS Int. J. Geo-Inf. 2024, 13(6), 177; https://doi.org/10.3390/ijgi13060177 - 29 May 2024
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The problems of administrations based simply on administrative units that do not consider the operational purposes of the system have been consistently discussed. For example, in the Republic of Korea, sex offenders’ information is distributed via physical mail only in a few regions,
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The problems of administrations based simply on administrative units that do not consider the operational purposes of the system have been consistently discussed. For example, in the Republic of Korea, sex offenders’ information is distributed via physical mail only in a few regions, a practice that is too rigidly based on the boundaries of the administrative ‘Dong’ of the offender’s residence. This implies that citizens in an adjacent building will not be notified if their Dong is different. Therefore, this study analyzed the problems of an administrative system that does not consider its realistic scope by using the case study of sex offender notifications. By expanding the distance from children and youth grids, we ascertained the extent of the problems with sex offender notifications. Additionally, to determine whether these problems have occurred by chance at a specific point in time or if there has been a fundamental limitation in the system, the Monte Carlo simulation was applied to compare the actual and random data of residences.
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Open AccessArticle
Detecting Road Intersections from Crowdsourced Trajectory Data Based on Improved YOLOv5 Model
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Yunfei Zhang, Gengbiao Tang and Naisi Sun
ISPRS Int. J. Geo-Inf. 2024, 13(6), 176; https://doi.org/10.3390/ijgi13060176 - 28 May 2024
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In recent years, the rapid development of autonomous driving and intelligent driver assistance has brought about urgent demands on high-precision road maps. However, traditional road map production methods mainly rely on professional survey technologies, such as remote sensing and mobile mapping, which suffer
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In recent years, the rapid development of autonomous driving and intelligent driver assistance has brought about urgent demands on high-precision road maps. However, traditional road map production methods mainly rely on professional survey technologies, such as remote sensing and mobile mapping, which suffer from high costs, object occlusions, and long updating cycles. In the era of ubiquitous mapping, crowdsourced trajectory data offer a new and low-cost data resource for the production and updating of high-precision road maps. Meanwhile, as key nodes in the transportation network, maintaining the currency and integrity of road intersection data is the primary task in enhancing map updates. In this paper, we propose a novel approach for detecting road intersections based on crowdsourced trajectory data by introducing an attention mechanism and modifying the loss function in the YOLOv5 model. The proposed method encompasses two key steps of training data preparation and improved YOLOv5s model construction. Multi-scale training processing is first adopted to prepare a rich and diverse sample dataset, including various kinds and different sizes of road intersections. Particularly to enhance the model’s detection performance, we inserted convolutional attention mechanism modules into the original YOLOv5 and integrated other alternative confidence loss functions and localization loss functions. The experimental results demonstrate that the improved YOLOv5 model achieves detection accuracy, precision, and recall rates as high as 97.46%, 99.57%, and 97.87%, respectively, outperforming other object detection models.
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Open AccessReview
A Comprehensive Overview Regarding the Impact of GIS on Property Valuation
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Gabriela Droj, Anita Kwartnik-Pruc and Laurențiu Droj
ISPRS Int. J. Geo-Inf. 2024, 13(6), 175; https://doi.org/10.3390/ijgi13060175 - 25 May 2024
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In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for
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In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for proactive urban planning strategies capable of navigating dynamic and unpredictable futures. In this context, the use of geographic information systems (GIS) offers researchers and decision makers a distinct advantage in the study of spatial data and enables the comprehensive study of spatial and temporal patterns in various disciplines, including real estate valuation. Central to the integration of modern technology into real estate valuation is the need to mitigate the inherent subjectivity of traditional valuation methods while increasing efficiency through the use of mass appraisal techniques. This study draws on extensive academic literature comprising 103 research articles published between 1993 and January 2024 to shed light on the multifaceted application of GISs in real estate valuation. In particular, three main areas are addressed: (1) hedonic models, (2) artificial intelligence (AI), and mathematical appraisal models. This synthesis emphasizes the interdependence of numerous societal challenges and highlights the need for interdisciplinary collaboration to address them effectively. In addition, this study provides a repertoire of methodologies that underscores the potential of advanced technologies, including artificial intelligence, GISs, and satellite imagery, to improve the subjectivity of traditional valuation approaches and thereby promote greater accuracy and productivity in real estate valuation. By integrating GISs into real estate valuation methodologies, stakeholders can navigate the complexity of urban landscapes with greater precision and promote equitable valuation practices that are conducive to sustainable urban development.
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Open AccessArticle
Data-Driven Geofencing Design for Point-Of-Interest Notifiers Utilizing Genetic Algorithm
by
Iori Sasaki, Masatoshi Arikawa, Min Lu, Tomihiro Utsumi and Ryo Sato
ISPRS Int. J. Geo-Inf. 2024, 13(6), 174; https://doi.org/10.3390/ijgi13060174 - 25 May 2024
Abstract
This study proposes a method for generating geofences driven by GPS trajectory data to realize scalable point-of-interest (POI) notifiers, encouraging walking tourists to discover new local spots. The case study revealed that manual geofence settings degrade the location relevance and user coverage—key objectives
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This study proposes a method for generating geofences driven by GPS trajectory data to realize scalable point-of-interest (POI) notifiers, encouraging walking tourists to discover new local spots. The case study revealed that manual geofence settings degrade the location relevance and user coverage—key objectives of POI notifiers—and hinder the scalability and reliability of services. The formalization presented computationally equips geofence designers with practical solutions through two implementations based on prior GPS trajectory logs: (1) a multiobjective genetic algorithm that suggests cost-effective geofences by providing trade-off visualizations and (2) a user coverage-penalized genetic algorithm that determines an optimal geofence based on the designers’ expectations. The feasibility and stability of the proposed implementations were tested in areas with varying tourist flow patterns. A comparative survey among manual settings, settings incorporating a reliability simulation, and data-driven settings demonstrates significant performance improvements for geofence services.
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Open AccessArticle
Evaluating School Location Based on a Territorial Spatial Planning Knowledge Graph
by
Xiankang Xu, Jian Hao and Jingwei Shen
ISPRS Int. J. Geo-Inf. 2024, 13(6), 173; https://doi.org/10.3390/ijgi13060173 - 24 May 2024
Abstract
The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many
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The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning.
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(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)
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