This paper investigated the surface length of roads from both horizontal and vertical perspectives using the theory of fractal dimension of surfaces and curves. Three progressive experiments were conducted. The first demonstrated the magnitude of the differences between the planar road length and the DTM-derived surface road length and assessed its correlation with the DTM-calculated road slope. The second investigated the road distance complexity through the fractal dimension in both planar and vertical dimensions. The third related the vertical with the horizontal fractal dimension of roads across a range of distinct physiographic regions. The study contributed theoretically by linking the planimetric complexity to vertical complexity, with clear applications for advanced transportation studies and network analyses. The core methodology used geographic information systems (GIS) to integrate a high resolution (1 × 1 m) digital terrain model (DTM) with a road network layer. A novel concept, the vertical fractal dimension of roads was introduced. Both the vertical and horizontal fractal dimensions of the roads were calculated using the box-counting methodology. We conducted an investigation into the relationship between the two fractal dimensions using fourteen study areas within four distinct physiographic regions across Slovenia. We found that the average slope of a three-dimensional (3D) road was directly related to the length difference between 3D and two-dimensional (2D) roads. The calculated values for the vertical fractal dimension in the study areas were only slightly above 1, while the maximum horizontal fractal dimension of 1.1837 reflected the more sinuous properties of the road in plan. Variations in the vertical and horizontal fractal dimensions of the roads varied between the different physiographic regions.
{"title":"Vertical vs. Horizontal Fractal Dimensions of Roads in Relation to Relief Characteristics","authors":"K. Prah, Ashton M. Shortridge","doi":"10.3390/ijgi12120487","DOIUrl":"https://doi.org/10.3390/ijgi12120487","url":null,"abstract":"This paper investigated the surface length of roads from both horizontal and vertical perspectives using the theory of fractal dimension of surfaces and curves. Three progressive experiments were conducted. The first demonstrated the magnitude of the differences between the planar road length and the DTM-derived surface road length and assessed its correlation with the DTM-calculated road slope. The second investigated the road distance complexity through the fractal dimension in both planar and vertical dimensions. The third related the vertical with the horizontal fractal dimension of roads across a range of distinct physiographic regions. The study contributed theoretically by linking the planimetric complexity to vertical complexity, with clear applications for advanced transportation studies and network analyses. The core methodology used geographic information systems (GIS) to integrate a high resolution (1 × 1 m) digital terrain model (DTM) with a road network layer. A novel concept, the vertical fractal dimension of roads was introduced. Both the vertical and horizontal fractal dimensions of the roads were calculated using the box-counting methodology. We conducted an investigation into the relationship between the two fractal dimensions using fourteen study areas within four distinct physiographic regions across Slovenia. We found that the average slope of a three-dimensional (3D) road was directly related to the length difference between 3D and two-dimensional (2D) roads. The calculated values for the vertical fractal dimension in the study areas were only slightly above 1, while the maximum horizontal fractal dimension of 1.1837 reflected the more sinuous properties of the road in plan. Variations in the vertical and horizontal fractal dimensions of the roads varied between the different physiographic regions.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139209087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies.
{"title":"A Head/Tail Breaks-Based Approach to Characterizing Space-Time Risks of COVID-19 Epidemic in China's Cities","authors":"Tingting Wu, Bisong Hu, Jin Luo, Shuhua Qi","doi":"10.3390/ijgi12120485","DOIUrl":"https://doi.org/10.3390/ijgi12120485","url":null,"abstract":"The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139210843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Jin, Yong Ge, Haoyu Fan, Zeshuo Li, Yaojie Liu, Yan Jia
Accurate spatial distribution of gridded gross domestic product (GDP) data is crucial for revealing regional disparities within administrative units, thus facilitating a deeper understanding of regional economic dynamics, industrial distribution, and urbanization trends. The existing GDP spatial models often rely on prediction residuals for model evaluation or utilize residual distribution to improve the final accuracy, frequently overlooking the modifiable areal unit problem within residual distribution. This paper introduces a hybrid downscaling model that combines random forest and area-to-area kriging to map gridded GDP. Employing Thailand as a case study, GDP distribution maps were generated at a 1 km spatial resolution for the year 2015 and compared with five alternative downscaling methods and an existing GDP product. The results demonstrate that the proposed approach yields higher accuracy and greater precision in detailing GDP distribution, as evidenced by the smallest mean absolute error and root mean squared error values, which stand at USD 256.458 and 699.348 ten million, respectively. Among the four different sets of auxiliary variables considered, one consistently exhibited a higher prediction accuracy. This particular set of auxiliary variables integrated classification-based variables, illustrating the advantages of incorporating such integrated variables into modeling while accounting for classification characteristics.
网格化国内生产总值（GDP）数据的精确空间分布对于揭示行政单元内的区域差异至关重要，从而有助于深入了解区域经济动态、产业分布和城市化趋势。现有的 GDP 空间模型往往依赖预测残差来评价模型，或利用残差分布来提高最终精度，往往忽视了残差分布中的可修改面积单位问题。本文介绍了一种混合降尺度模型，该模型结合了随机森林和区域到区域克里格法，用于绘制网格 GDP 图。本文以泰国为例，以 1 公里的空间分辨率生成了 2015 年的国内生产总值分布图，并与其他五种降尺度方法和现有的国内生产总值产品进行了比较。结果表明，所建议的方法在详细绘制国内生产总值分布图方面具有更高的准确度和精确度，这体现在平均绝对误差和均方根误差值最小，分别为 2.56458 亿美元和 6.99348 亿美元。在所考虑的四组不同的辅助变量中，有一组始终表现出较高的预测精度。这组特定的辅助变量整合了基于分类的变量，说明了在考虑分类特征的同时将此类整合变量纳入建模的优势。
{"title":"Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model","authors":"Yan Jin, Yong Ge, Haoyu Fan, Zeshuo Li, Yaojie Liu, Yan Jia","doi":"10.3390/ijgi12120481","DOIUrl":"https://doi.org/10.3390/ijgi12120481","url":null,"abstract":"Accurate spatial distribution of gridded gross domestic product (GDP) data is crucial for revealing regional disparities within administrative units, thus facilitating a deeper understanding of regional economic dynamics, industrial distribution, and urbanization trends. The existing GDP spatial models often rely on prediction residuals for model evaluation or utilize residual distribution to improve the final accuracy, frequently overlooking the modifiable areal unit problem within residual distribution. This paper introduces a hybrid downscaling model that combines random forest and area-to-area kriging to map gridded GDP. Employing Thailand as a case study, GDP distribution maps were generated at a 1 km spatial resolution for the year 2015 and compared with five alternative downscaling methods and an existing GDP product. The results demonstrate that the proposed approach yields higher accuracy and greater precision in detailing GDP distribution, as evidenced by the smallest mean absolute error and root mean squared error values, which stand at USD 256.458 and 699.348 ten million, respectively. Among the four different sets of auxiliary variables considered, one consistently exhibited a higher prediction accuracy. This particular set of auxiliary variables integrated classification-based variables, illustrating the advantages of incorporating such integrated variables into modeling while accounting for classification characteristics.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139230770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How can the interactive mode of a map be optimized to facilitate efficient positioning and improve cognitive efficiency? This paper addresses this crucial aspect of map design. It explores the impact of spatial reference frames, map dimensionality, and navigation modes on spatial orientation efficiency, as well as their interactions, through empirical eye-movement experiments. The results demonstrate the following: (1) When using a 2D fixed map in an allocentric reference frame, participants exhibit a high correct rate, a low cognitive load, and a short reaction time. In contrast, when operating within an egocentric reference frame using a 2D rotating map, participants demonstrate a higher correct rate, a reduced cognitive load, and a quicker reaction time. (2) The simplicity of 2D maps, despite their reduced authenticity compared to 3D maps, diminishes users’ cognitive load and enhances positioning efficiency. (3) The fixed map aligns more closely with the cognitive habits of participants in the allocentric reference frame, while the rotating map corresponds better to the cognitive habits of participants in the egocentric reference frame, thereby improving their cognitive efficiency. This study offers insights that can inform the optimization design of spatial orientation efficiency.
{"title":"Effects of Spatial Reference Frames, Map Dimensionality, and Navigation Modes on Spatial Orientation Efficiency","authors":"Hongyun Guo, Nai Yang, Zhong Wang, Hao Fang","doi":"10.3390/ijgi12120476","DOIUrl":"https://doi.org/10.3390/ijgi12120476","url":null,"abstract":"How can the interactive mode of a map be optimized to facilitate efficient positioning and improve cognitive efficiency? This paper addresses this crucial aspect of map design. It explores the impact of spatial reference frames, map dimensionality, and navigation modes on spatial orientation efficiency, as well as their interactions, through empirical eye-movement experiments. The results demonstrate the following: (1) When using a 2D fixed map in an allocentric reference frame, participants exhibit a high correct rate, a low cognitive load, and a short reaction time. In contrast, when operating within an egocentric reference frame using a 2D rotating map, participants demonstrate a higher correct rate, a reduced cognitive load, and a quicker reaction time. (2) The simplicity of 2D maps, despite their reduced authenticity compared to 3D maps, diminishes users’ cognitive load and enhances positioning efficiency. (3) The fixed map aligns more closely with the cognitive habits of participants in the allocentric reference frame, while the rotating map corresponds better to the cognitive habits of participants in the egocentric reference frame, thereby improving their cognitive efficiency. This study offers insights that can inform the optimization design of spatial orientation efficiency.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139243742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ extraction. However, many existing methods only use a single type of data or simply combine the two, failing to take full advantage of the complementary advantages between them. Therefore, we designed a deep-learning framework that integrates the above two types of data to identify urban functional areas. In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. Specifically, we extract visual features from RSI data, while POI data are converted into a distance heatmap tensor that is input into the CNN with gated attention mechanisms to extract social features. Then, we use a feature fusion module (FFM) with adaptive weights to fuse the two types of features. The second part is the spatial-relationship-modeling module. We designed a new spatial-relationship-learning network based on a vision transformer model with long- and short-distance attention, which can simultaneously learn the global and local spatial relationships of the urban functional zones. Finally, a feature aggregation module (FGM) utilizes the two spatial relationships efficiently. The experimental results show that the proposed model can fully extract visual features, social features, and spatial relationship features from RSIs and POIs for more accurate UFZ recognition.
准确、高效的城市功能区（UFZ）分类图对于城市规划、管理和决策至关重要。由于城市功能区具有复杂的社会经济属性，仅使用遥感图像（RSI）来识别城市功能区越来越具有挑战性。兴趣点（POI）数据和遥感图像数据在 UFZ 提取中发挥着重要作用。然而，现有的许多方法只使用单一类型的数据或简单地将两者结合起来，未能充分利用两者之间的互补优势。因此，我们设计了一种深度学习框架，将上述两类数据整合在一起，以识别城市功能区。在互补特征学习与融合模块的第一部分，我们使用卷积神经网络（CNN）来提取视觉特征和社会特征。具体来说，我们从 RSI 数据中提取视觉特征，同时将 POI 数据转换为距离热图张量，并将其输入具有门控注意机制的 CNN，以提取社会特征。然后，我们使用具有自适应权重的特征融合模块（FFM）来融合这两类特征。第二部分是空间关系建模模块。我们设计了一种新的空间关系学习网络，该网络基于视觉转换器模型，具有远距离和近距离注意力，可以同时学习城市功能区的全局和局部空间关系。最后，一个特征聚合模块（FGM）有效地利用了这两种空间关系。实验结果表明，所提出的模型可以从 RSI 和 POI 中充分提取视觉特征、社会特征和空间关系特征，从而实现更准确的 UFZ 识别。
{"title":"A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition","authors":"Mingyang Yu, Haiqing Xu, Fangliang Zhou, Shuai Xu, Hongling Yin","doi":"10.3390/ijgi12120468","DOIUrl":"https://doi.org/10.3390/ijgi12120468","url":null,"abstract":"Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ extraction. However, many existing methods only use a single type of data or simply combine the two, failing to take full advantage of the complementary advantages between them. Therefore, we designed a deep-learning framework that integrates the above two types of data to identify urban functional areas. In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. Specifically, we extract visual features from RSI data, while POI data are converted into a distance heatmap tensor that is input into the CNN with gated attention mechanisms to extract social features. Then, we use a feature fusion module (FFM) with adaptive weights to fuse the two types of features. The second part is the spatial-relationship-modeling module. We designed a new spatial-relationship-learning network based on a vision transformer model with long- and short-distance attention, which can simultaneously learn the global and local spatial relationships of the urban functional zones. Finally, a feature aggregation module (FGM) utilizes the two spatial relationships efficiently. The experimental results show that the proposed model can fully extract visual features, social features, and spatial relationship features from RSIs and POIs for more accurate UFZ recognition.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular boundaries, redundant points, inaccurate positions, and unclear turns arising from variations in the image quality, the complexity of the surrounding environment, and the extraction methods used, impeding their direct utility. Therefore, this study proposes a simplification and regularization algorithm for right-angled polygon building outlines with jagged edges. First, the minimum bounding rectangle of the building outlines is established and populated with a square grid based on the smallest visible length principle. Overlay analysis is then applied to the grid and original buildings to extract the turning points of the outlines. Finally, the building orientation is used as a reference axis to sort the turning points and reconstruct the simplified building outlines. Experimentally, the proposed simplification method enhances the morphological characteristics of building outlines, such as parallelism and orthogonality, while considering simplification principles, such as the preservation of the direction, position, area, and shape of the building. The proposed algorithm provides a new simplification and regularization method for right-angled polygon building outlines with jagged edges.
{"title":"Simplification and Regularization Algorithm for Right-Angled Polygon Building Outlines with Jagged Edges","authors":"Linghui Kong, Haizhong Qian, Yuqing Wu, Xinyu Niu, Di Wang, Zhekun Huang","doi":"10.3390/ijgi12120469","DOIUrl":"https://doi.org/10.3390/ijgi12120469","url":null,"abstract":"Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular boundaries, redundant points, inaccurate positions, and unclear turns arising from variations in the image quality, the complexity of the surrounding environment, and the extraction methods used, impeding their direct utility. Therefore, this study proposes a simplification and regularization algorithm for right-angled polygon building outlines with jagged edges. First, the minimum bounding rectangle of the building outlines is established and populated with a square grid based on the smallest visible length principle. Overlay analysis is then applied to the grid and original buildings to extract the turning points of the outlines. Finally, the building orientation is used as a reference axis to sort the turning points and reconstruct the simplified building outlines. Experimentally, the proposed simplification method enhances the morphological characteristics of building outlines, such as parallelism and orthogonality, while considering simplification principles, such as the preservation of the direction, position, area, and shape of the building. The proposed algorithm provides a new simplification and regularization method for right-angled polygon building outlines with jagged edges.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Zhao, Ning Liu, Junhui Li, Xi Guo, Hongtao Deng, Jinshan Sun
The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue.
{"title":"A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents","authors":"Jing Zhao, Ning Liu, Junhui Li, Xi Guo, Hongtao Deng, Jinshan Sun","doi":"10.3390/ijgi12120470","DOIUrl":"https://doi.org/10.3390/ijgi12120470","url":null,"abstract":"The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lei Liu, Yong Sun, Min Ji, Huimeng Wang, Jiantao Liu
The three-dimensional (3D) geological voxel model is essential for numerical simulation and resource calculation. However, it can be challenging due to the point in polygon test in 3D voxel modeling. The commonly used Winding number algorithm requires the manual setting of observation points and uses their relative positions to restrict the positive and negative solid angles. Therefore, we proposed the Winding number with triangle network coding (WNTC) algorithm and applied it to automatically construct a 3D voxel model of the ore body. The proposed WNTC algorithm encodes the stratum model by using the Delaunay triangulation network to constrain the index order of each vertex of the triangular plane unit. GPU parallel computing was used to optimize its computational speed. Our results demonstrated that the WNTC algorithm can greatly improve the efficiency and automation of 3D ore body modeling. Compared to the Ray casting method, it can compensate for a voxel loss of about 0.7%. We found the GPU to be 99.96% faster than the CPU, significantly improving voxel model construction speed. Additionally, this method is less affected by the complexity of the stratum model. Our study has substantial potential for similar work in 3D geological modeling and other relevant fields.
{"title":"Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing","authors":"Lei Liu, Yong Sun, Min Ji, Huimeng Wang, Jiantao Liu","doi":"10.3390/ijgi12120473","DOIUrl":"https://doi.org/10.3390/ijgi12120473","url":null,"abstract":"The three-dimensional (3D) geological voxel model is essential for numerical simulation and resource calculation. However, it can be challenging due to the point in polygon test in 3D voxel modeling. The commonly used Winding number algorithm requires the manual setting of observation points and uses their relative positions to restrict the positive and negative solid angles. Therefore, we proposed the Winding number with triangle network coding (WNTC) algorithm and applied it to automatically construct a 3D voxel model of the ore body. The proposed WNTC algorithm encodes the stratum model by using the Delaunay triangulation network to constrain the index order of each vertex of the triangular plane unit. GPU parallel computing was used to optimize its computational speed. Our results demonstrated that the WNTC algorithm can greatly improve the efficiency and automation of 3D ore body modeling. Compared to the Ray casting method, it can compensate for a voxel loss of about 0.7%. We found the GPU to be 99.96% faster than the CPU, significantly improving voxel model construction speed. Additionally, this method is less affected by the complexity of the stratum model. Our study has substantial potential for similar work in 3D geological modeling and other relevant fields.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139250603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Li, Guozhou Wu, Shuai Zhang, Manchun Li, Beidou Nie, Zhenjie Chen
Wildfires spread rapidly and cause considerable ecological and socioeconomic losses. Inner Mongolia is among the regions in China that suffer the most from wildfires. A simple, effective model that uses fewer parameters to simulate wildfire spread is crucial for rapid decision-making. This study presents a region-specific technological process that requires a few meteorological parameters and limited grassland vegetation data to predict fire spreading dynamics in Inner Mongolia, based on cellular automata that emphasize the numeric evaluation of both heat sinks and sources. The proposed method considers a case that occurred in 2021 near the East Ujimqin Banner border between China and Mongolia. Three hypothetical grassland wildfires were developed using GIS technology to test and demonstrate the proposed model. The simulation results suggest that the model agrees well with real-world experience and can facilitate real-time decision-making to enhance the effectiveness of firefighting, fire control, and simulation-based training for firefighters.
{"title":"A Novel Method of Modeling Grassland Wildfire Dynamics Based on Cellular Automata: A Case Study in Inner Mongolia, China","authors":"Yan Li, Guozhou Wu, Shuai Zhang, Manchun Li, Beidou Nie, Zhenjie Chen","doi":"10.3390/ijgi12120474","DOIUrl":"https://doi.org/10.3390/ijgi12120474","url":null,"abstract":"Wildfires spread rapidly and cause considerable ecological and socioeconomic losses. Inner Mongolia is among the regions in China that suffer the most from wildfires. A simple, effective model that uses fewer parameters to simulate wildfire spread is crucial for rapid decision-making. This study presents a region-specific technological process that requires a few meteorological parameters and limited grassland vegetation data to predict fire spreading dynamics in Inner Mongolia, based on cellular automata that emphasize the numeric evaluation of both heat sinks and sources. The proposed method considers a case that occurred in 2021 near the East Ujimqin Banner border between China and Mongolia. Three hypothetical grassland wildfires were developed using GIS technology to test and demonstrate the proposed model. The simulation results suggest that the model agrees well with real-world experience and can facilitate real-time decision-making to enhance the effectiveness of firefighting, fire control, and simulation-based training for firefighters.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liang Kong, Qinglin Wu, Jie Deng, Leichao Bai, Zhongsheng Chen, Zhong Du, Mingliang Luo
With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China’s population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf’s law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies.
{"title":"Assessing Regional Development Balance Based on Zipf's Law: The Case of Chinese Urban Agglomerations","authors":"Liang Kong, Qinglin Wu, Jie Deng, Leichao Bai, Zhongsheng Chen, Zhong Du, Mingliang Luo","doi":"10.3390/ijgi12120472","DOIUrl":"https://doi.org/10.3390/ijgi12120472","url":null,"abstract":"With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China’s population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf’s law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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