Zahra Maserrat, Ali Asghar Alesheikh, Ali Jafari, Neda Kaffash Charandabi, Javad Shahidinejad
Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the “worn-out urban texture” criterion and a meager weight for “noise pollution”. Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process.
{"title":"A Dempster–Shafer Enhanced Framework for Urban Road Planning Using a Model-Based Digital Twin and MCDM Techniques","authors":"Zahra Maserrat, Ali Asghar Alesheikh, Ali Jafari, Neda Kaffash Charandabi, Javad Shahidinejad","doi":"10.3390/ijgi13090302","DOIUrl":"https://doi.org/10.3390/ijgi13090302","url":null,"abstract":"Rapid urbanization in developing countries presents a critical challenge in the need for extensive and appropriate road expansion, which in turn contributes to traffic congestion and air pollution. Urban areas are economic engines, but their efficiency and livability rely on well-designed road networks. This study proposes a novel approach to urban road planning that leverages the power of several innovative techniques. The cornerstone of this approach is a digital twin model of the urban environment. This digital twin model facilitates the evaluation and comparison of road development proposals. To support informed decision-making, a multi-criteria decision-making (MCDM) framework is used, enabling planners to consider various factors such as traffic flow, environmental impact, and economic considerations. Spatial data and 3D visualizations are also provided to enrich the analysis. Finally, the Dempster–Shafer theory (DST) provides a robust mathematical framework to address uncertainties inherent in the weighting process. The proposed approach was applied to planning for both new road constructions and existing road expansions. By combining these elements, the model offers a sustainable and knowledge-based approach to optimize urban road planning. Results from integrating weights obtained through two weighting methods, the Analytic Hierarchy Process (AHP) and the Bayesian best–worst Method (B-BWM), showed a very high weight for the “worn-out urban texture” criterion and a meager weight for “noise pollution”. Finally, the cost path algorithm was used to evaluate the results from all three methods (AHP, B-BWM, and DST). The high degree of similarity in the results from these methods suggests a stable outcome for the proposed approach. Analysis of the study area revealed the following significant challenge for road planning: 35% of the area was deemed unsuitable, with only a tiny portion (4%) being suitable for road development based on the selected criteria. This highlights the need to explore alternative approaches or significantly adjust the current planning process.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"46 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatma Zohra Chaabane, Salim Lamine, Mohamed Said Guettouche, Nour El Islam Bachari, Nassim Hallal
Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.
{"title":"Landslide Risk Assessments through Multicriteria Analysis","authors":"Fatma Zohra Chaabane, Salim Lamine, Mohamed Said Guettouche, Nour El Islam Bachari, Nassim Hallal","doi":"10.3390/ijgi13090303","DOIUrl":"https://doi.org/10.3390/ijgi13090303","url":null,"abstract":"Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"25 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.
在地图概括中选择道路网络一直是一项艰巨的挑战，推动着研究向智能模型的应用方向发展。尽管之前做出了很多努力，但这些研究的准确性和连通性仍有待提高，尤其是在处理样本量相似的道路类型时。为了解决这些不足，我们引入了一种用于道路选择的异构图注意力网络（HAN），在该网络中，最初利用特征掩蔽方法来评估道路特征的重要性。在 HAN 框架内，我们将注意力集中在最相关的特征上，并引入了两条元路径：一条是在一阶邻域内聚合相同道路类型的特征，强调局部连通性；另一条是将这种聚合扩展到二阶邻域，捕捉更广泛的空间背景。为了进行综合评估，我们使用了一套同时考虑道路网络定量和定性方面的指标。在样本量相近的道路类型上，HAN 模型在传导型和归纳型任务中的表现都优于其他模型。其准确率（ACC）分别高出 1.62% 和 0.67%，F1 分数分别高出 1.43% 和 0.81%。此外，它还增强了所选网络的整体连通性。总之，我们基于 HAN 的方法为道路网络选择提供了一种先进的解决方案，在准确性和连通性保护方面超越了以往的方法。
{"title":"Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network","authors":"Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang","doi":"10.3390/ijgi13090300","DOIUrl":"https://doi.org/10.3390/ijgi13090300","url":null,"abstract":"Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"22 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group.
{"title":"The Impact of Airbnb on Long-Term Rental Markets in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression","authors":"Dongkeun Hur, Seonjin Lee, Hany Kim","doi":"10.3390/ijgi13090298","DOIUrl":"https://doi.org/10.3390/ijgi13090298","url":null,"abstract":"The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"10 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lili Wu, Di Cao, Jinjin Yang, Ruoyi Zhang, Xinran Yan
In the context of the information age, the symbolization of regional elements has become a crucial component in modern cartographic practice. The targeted identification of regional elements and the design of map symbols are prerequisites for realizing the symbolization of regional elements. Therefore, we propose a method to symbolize regional elements by combining textual analysis and image processing. Firstly, local chronicles are used as the textual information source, and regional elements are extracted through textual data mining. Second, the real image data of the elements are selected, and the image segmentation algorithm, clustering algorithm, etc., are used to extract contours and colors from the images and carry out corresponding symbol simplification and color matching, to create highly recognizable symbols. Finally, we apply the symbols to two map types: the thematic map and the tourist map, and design a questionnaire to evaluate the outcomes of the symbol design. After a thorough review, it has been found that the method is superior to related symbolization studies in terms of data source authority, symbol generation efficiency, and symbol information carrying. In conclusion, guided by interdisciplinary thinking, this study effectively combines theoretical analysis and design practice, proposes a new idea of symbolization, and opens up a new way for geographic information visualization.
{"title":"The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction","authors":"Lili Wu, Di Cao, Jinjin Yang, Ruoyi Zhang, Xinran Yan","doi":"10.3390/ijgi13090299","DOIUrl":"https://doi.org/10.3390/ijgi13090299","url":null,"abstract":"In the context of the information age, the symbolization of regional elements has become a crucial component in modern cartographic practice. The targeted identification of regional elements and the design of map symbols are prerequisites for realizing the symbolization of regional elements. Therefore, we propose a method to symbolize regional elements by combining textual analysis and image processing. Firstly, local chronicles are used as the textual information source, and regional elements are extracted through textual data mining. Second, the real image data of the elements are selected, and the image segmentation algorithm, clustering algorithm, etc., are used to extract contours and colors from the images and carry out corresponding symbol simplification and color matching, to create highly recognizable symbols. Finally, we apply the symbols to two map types: the thematic map and the tourist map, and design a questionnaire to evaluate the outcomes of the symbol design. After a thorough review, it has been found that the method is superior to related symbolization studies in terms of data source authority, symbol generation efficiency, and symbol information carrying. In conclusion, guided by interdisciplinary thinking, this study effectively combines theoretical analysis and design practice, proposes a new idea of symbolization, and opens up a new way for geographic information visualization.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"52 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India","authors":"Asheesh Sharma, Mandeep Poonia, Ankush Rai, Rajesh B. Biniwale, Franziska Tügel, Ekkehard Holzbecher, Reinhard Hinkelmann","doi":"10.3390/ijgi13080297","DOIUrl":"https://doi.org/10.3390/ijgi13080297","url":null,"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 (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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"154 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China","authors":"Moyan Wang, Zhengyuan Liang, Zhiming Li","doi":"10.3390/ijgi13080296","DOIUrl":"https://doi.org/10.3390/ijgi13080296","url":null,"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 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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"95 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
André Kotze, Moritz Jan Hildemann, Vítor Santos, Carlos Granell
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.
轨迹优化是一种寻找连接起点和终点的最佳路线的方法。轨迹是否合适取决于是否与任何障碍物相交，以及预定义的性能指标。就无人驾驶飞行器（UAV）而言，其目标是在避开飞行禁区的同时，以能量或时间为单位最大限度地降低路径成本。包括进化计算在内的人工智能技术已被应用于轨迹优化，并取得了不同程度的成功。本研究通过开发一种新型 GP 算法，将三维地理轨迹编码为函数树，从而优化三维空间中的轨迹，探索了遗传编程（GP）在三维轨迹优化中的应用。该研究还探讨和讨论了参数化的影响，展示了自定义参数设置和其他进化计算技术的优缺点。研究结果表明了所提算法的有效性，该算法在速度、自动性和鲁棒性方面均优于现有方法，突出了基于 GP 的算法应用于科学和工程领域其他复杂优化问题的潜力。
{"title":"Genetic Programming to Optimize 3D Trajectories","authors":"André Kotze, Moritz Jan Hildemann, Vítor Santos, Carlos Granell","doi":"10.3390/ijgi13080295","DOIUrl":"https://doi.org/10.3390/ijgi13080295","url":null,"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 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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"4 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
在示意性地铁地图上，高质量的标签放置有助于乘客完成路线规划和定位任务。据报道，人工神经网络（ANN）可以利用学习到的标签知识来放置标签。然而，之前基于人工神经网络的方法只考虑了车站点及其连接边的影响。事实上，周围未连接的特征（点、边和标签）也会极大地影响标签放置的质量。为此，我们提出了一种改进的方法。首先，基于标注的自然智能（即制图师建立的标注经验、知识和规则）对标注位置与连接特征和周围特征之间的关系进行建模。然后，利用 ANN 学习这些关系。定量评估结果表明，与基准值（分别为 4.17%、14.29% 和 35.11%）相比，我们的方法达到了较低的标签点重叠率（0.00%）、标签边缘重叠率（4.12%）和标签与标签重叠率（20.58%）。另一方面，我们的方法有效地避免了模糊标签，并确保同一行的标签被放置在同一侧。定性评估显示，约 75% 的用户更喜欢我们的结果。这种新方法有望推动地铁示意图的自动生成。
{"title":"An Improved ANN-Based Label Placement Method Considering Surrounding Features for Schematic Metro Maps","authors":"Zhiwei Wu, Tian Lan, Chenzhen Sun, Donglin Cheng, Xing Shi, Meisheng Chen, Guangjun Zeng","doi":"10.3390/ijgi13080294","DOIUrl":"https://doi.org/10.3390/ijgi13080294","url":null,"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 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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"1 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"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","authors":"Juanjuan Zhong, Ye Duan, Caizhi Sun, Hongye Wang","doi":"10.3390/ijgi13080293","DOIUrl":"https://doi.org/10.3390/ijgi13080293","url":null,"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 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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"3 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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