Pub Date : 2023-05-08DOI: 10.1080/13658816.2023.2209803
I. Nilsson, E. Delmelle
Abstract In this article, we introduce an approach for studying micro-geographic housing dynamics using an embedding-based, semi-supervised text classification approach on longitudinal, point-level property listing data. Based on the text used to describe properties for sale and a set of predefined classes and keywords, listings are classified according to their lifecycle of investment or disinvestment. The mixture of property types within 1 × 1 mile grid cells are then calculated and used as input in a clustering algorithm to develop a place-based classification that enables us to examine patterns of change over time. In a case study on Mecklenburg County, North Carolina using 158,253 real estate listings between 2001 and 2020, we demonstrate how this approach has the potential to further our understanding of housing and neighborhood dynamics by grounding our analysis in theoretical concepts around the housing lifecycle and its relationship to neighborhood change.
{"title":"An embedding-based text classification approach for understanding micro-geographic housing dynamics","authors":"I. Nilsson, E. Delmelle","doi":"10.1080/13658816.2023.2209803","DOIUrl":"https://doi.org/10.1080/13658816.2023.2209803","url":null,"abstract":"Abstract In this article, we introduce an approach for studying micro-geographic housing dynamics using an embedding-based, semi-supervised text classification approach on longitudinal, point-level property listing data. Based on the text used to describe properties for sale and a set of predefined classes and keywords, listings are classified according to their lifecycle of investment or disinvestment. The mixture of property types within 1 × 1 mile grid cells are then calculated and used as input in a clustering algorithm to develop a place-based classification that enables us to examine patterns of change over time. In a case study on Mecklenburg County, North Carolina using 158,253 real estate listings between 2001 and 2020, we demonstrate how this approach has the potential to further our understanding of housing and neighborhood dynamics by grounding our analysis in theoretical concepts around the housing lifecycle and its relationship to neighborhood change.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"1 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42095827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-28DOI: 10.1080/13658816.2023.2193829
Xiaoya Ma, Xiaoyu Zhang, Xiang Zhao
Abstract The reliable service coverage of many facilities or sensors used in smart city infrastructure is highly susceptible to obstructions in urban environments. Optimizing the line-of-sight (LOS) service coverage is essential to locating these facilities for smarter city services. Despite progression in the maximal coverage location problem (MCLP) model for locating facilities, maximizing the LOS service coverage in continuous demand space for facility location problems remains challenging. This study defined the LOS-constrained MCLPs (LOS-MCLPs) and proposed a service coverage optimization model to solve these LOS-MCLPs. We employed a computational geometry algorithm named the visibility polygon (VP) algorithm to simulate the LOS coverage in two-dimensional (2D) continuous demand space. We then coupled this algorithm with a robust heuristic algorithm to search for the optimal solutions to maximize effective LOS service coverage. An experiment applied the developed model to a Wi-Fi hotspot planning problem. The experimental results demonstrated that the proposed model can obtain optimal solutions for LOS-MCLPs according to the distribution of obstacles. Comparative results show that ignoring the LOS effect in the optimization of LOS-MCLPs might lead to large areas of service dead zones.
{"title":"Service coverage optimization for facility location: considering line-of-sight coverage in continuous demand space","authors":"Xiaoya Ma, Xiaoyu Zhang, Xiang Zhao","doi":"10.1080/13658816.2023.2193829","DOIUrl":"https://doi.org/10.1080/13658816.2023.2193829","url":null,"abstract":"Abstract The reliable service coverage of many facilities or sensors used in smart city infrastructure is highly susceptible to obstructions in urban environments. Optimizing the line-of-sight (LOS) service coverage is essential to locating these facilities for smarter city services. Despite progression in the maximal coverage location problem (MCLP) model for locating facilities, maximizing the LOS service coverage in continuous demand space for facility location problems remains challenging. This study defined the LOS-constrained MCLPs (LOS-MCLPs) and proposed a service coverage optimization model to solve these LOS-MCLPs. We employed a computational geometry algorithm named the visibility polygon (VP) algorithm to simulate the LOS coverage in two-dimensional (2D) continuous demand space. We then coupled this algorithm with a robust heuristic algorithm to search for the optimal solutions to maximize effective LOS service coverage. An experiment applied the developed model to a Wi-Fi hotspot planning problem. The experimental results demonstrated that the proposed model can obtain optimal solutions for LOS-MCLPs according to the distribution of obstacles. Comparative results show that ignoring the LOS effect in the optimization of LOS-MCLPs might lead to large areas of service dead zones.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1496 - 1519"},"PeriodicalIF":5.7,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47855189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-28DOI: 10.1080/13658816.2023.2204347
Filip Juricev-Martincev, Bernadette Giuffrida, Helen Thompson, Gentry White
Abstract Data regionalisation allows spatial inference over a population. The statistical regions must be updated to account for population changes, but this update process is more restrictive and iterative than ab initio regionalisation. This creates a need for an algorithmic solution that minimises human-in-the-loop involvement in population-driven regionalisation. The new method must address the basic regionalisation criteria – contiguity, compactness, homogeneity, equinumeriosity, and temporal consistency. We present a novel validation metric to assess the quality of partition based on these criteria. We have developed a novel hybrid aggregation algorithm (HeLP), combining elements of hierarchical and graph-theoretic approaches, for the primary purpose of repartitioning. This algorithm operates in average computational time complexity. HeLP was tested on simulated data and the Australian Statistical Geography Standard. The method can emulate the human operator successfully, providing statistically significant results in repartitioning parcel-based systems, such as the Cadastre.
{"title":"A novel hierarchical aggregation algorithm for optimal repartitioning of statistical regions","authors":"Filip Juricev-Martincev, Bernadette Giuffrida, Helen Thompson, Gentry White","doi":"10.1080/13658816.2023.2204347","DOIUrl":"https://doi.org/10.1080/13658816.2023.2204347","url":null,"abstract":"Abstract Data regionalisation allows spatial inference over a population. The statistical regions must be updated to account for population changes, but this update process is more restrictive and iterative than ab initio regionalisation. This creates a need for an algorithmic solution that minimises human-in-the-loop involvement in population-driven regionalisation. The new method must address the basic regionalisation criteria – contiguity, compactness, homogeneity, equinumeriosity, and temporal consistency. We present a novel validation metric to assess the quality of partition based on these criteria. We have developed a novel hybrid aggregation algorithm (HeLP), combining elements of hierarchical and graph-theoretic approaches, for the primary purpose of repartitioning. This algorithm operates in average computational time complexity. HeLP was tested on simulated data and the Australian Statistical Geography Standard. The method can emulate the human operator successfully, providing statistically significant results in repartitioning parcel-based systems, such as the Cadastre.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1640 - 1666"},"PeriodicalIF":5.7,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47740948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-26DOI: 10.1080/13658816.2023.2203218
Tianhong Zhao, Zhengdong Huang, Wei Tu, F. Biljecki, Long Chen
Abstract The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus’s spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model’s performance.
{"title":"Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus","authors":"Tianhong Zhao, Zhengdong Huang, Wei Tu, F. Biljecki, Long Chen","doi":"10.1080/13658816.2023.2203218","DOIUrl":"https://doi.org/10.1080/13658816.2023.2203218","url":null,"abstract":"Abstract The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus’s spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model’s performance.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1555 - 1581"},"PeriodicalIF":5.7,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43128705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1080/13658816.2023.2203199
Yuxuan Su, Yanfei Zhong, Yinhe Liu, Zhendong Zheng
Abstract Urban land-use types, such as residential and administration, can be inferred through semantic objects and their relationships. Point of interest (POI) data can serve as the semantic objects for urban land-use mapping. However, the previous POI-based approaches have rarely considered the relationships between the semantic objects in the urban land-use mapping, and three main challenges remain: 1) the lack of paired semantic object/land-use samples; 2) the lack of a unified model for semantic objects and the relationships between sematic objects and urban land use; and 3) the difficulty of automatically learning semantic object/land-use mapping relationships. In this paper, to address these issues, a graph-based urban land-use mapping framework integrating semantic object/land-use relationships (GOLR) is proposed. Based on open-source area of interest (AOI) and POI data, an urban object/land-use (UOLU) dataset covering 34 cities in China was built. To model the spatial and mapping relationships, the semantic objects and their relationships are used to jointly build an urban land-use graph. The mapping from semantic objects to urban land use can then be learned by the urban land-use graph isomorphic network (ULGIN) model. Finally, the GOLR framework was applied to obtain accurate land-use mapping results for multiple Chinese cities.
{"title":"A graph-based framework to integrate semantic object/land-use relationships for urban land-use mapping with case studies of Chinese cities","authors":"Yuxuan Su, Yanfei Zhong, Yinhe Liu, Zhendong Zheng","doi":"10.1080/13658816.2023.2203199","DOIUrl":"https://doi.org/10.1080/13658816.2023.2203199","url":null,"abstract":"Abstract Urban land-use types, such as residential and administration, can be inferred through semantic objects and their relationships. Point of interest (POI) data can serve as the semantic objects for urban land-use mapping. However, the previous POI-based approaches have rarely considered the relationships between the semantic objects in the urban land-use mapping, and three main challenges remain: 1) the lack of paired semantic object/land-use samples; 2) the lack of a unified model for semantic objects and the relationships between sematic objects and urban land use; and 3) the difficulty of automatically learning semantic object/land-use mapping relationships. In this paper, to address these issues, a graph-based urban land-use mapping framework integrating semantic object/land-use relationships (GOLR) is proposed. Based on open-source area of interest (AOI) and POI data, an urban object/land-use (UOLU) dataset covering 34 cities in China was built. To model the spatial and mapping relationships, the semantic objects and their relationships are used to jointly build an urban land-use graph. The mapping from semantic objects to urban land use can then be learned by the urban land-use graph isomorphic network (ULGIN) model. Finally, the GOLR framework was applied to obtain accurate land-use mapping results for multiple Chinese cities.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1582 - 1614"},"PeriodicalIF":5.7,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43514041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.1080/13658816.2023.2203212
Zehua Zhang, Yong-Soo Song, Peng Luo, Peng Wu
Abstract The explanation of spatial errors in geospatial modelling has long been a challenge. This study introduces an index that captures the complexity of local spatial distribution, which can partially provide insight into spatial errors. While previous studies have explored the complexity of geographical data from various perspectives, there is limited knowledge on assessing the complexity while taking spatial dependence into account. This study proposes a measure of geocomplexity, i.e. the spatial local complexity indicator, which characterizes the complexity of local spatial patterns while considering spatial neighbor dependence. We used both aspatial and spatial models to estimate the economic inequality in Australia, and applied the spatial local complexity indicator to explain spatial errors in these models. Results show that the developed geocomplexity indicator, using a binary spatial matrix, can effectively explain spatial errors arising from models, including 17%-47% of errors in aspatial models and 14% in a spatial model. The experiments in this study support our hypothesis that geocomplexity is an essential component in explaining spatial errors. The proposed geocomplexity indicator, along with our hypothesis, has the potential for advancing the understanding complex geospatial systems and enabling applications in various fields related to spatial data analysis.
{"title":"Geocomplexity explains spatial errors","authors":"Zehua Zhang, Yong-Soo Song, Peng Luo, Peng Wu","doi":"10.1080/13658816.2023.2203212","DOIUrl":"https://doi.org/10.1080/13658816.2023.2203212","url":null,"abstract":"Abstract The explanation of spatial errors in geospatial modelling has long been a challenge. This study introduces an index that captures the complexity of local spatial distribution, which can partially provide insight into spatial errors. While previous studies have explored the complexity of geographical data from various perspectives, there is limited knowledge on assessing the complexity while taking spatial dependence into account. This study proposes a measure of geocomplexity, i.e. the spatial local complexity indicator, which characterizes the complexity of local spatial patterns while considering spatial neighbor dependence. We used both aspatial and spatial models to estimate the economic inequality in Australia, and applied the spatial local complexity indicator to explain spatial errors in these models. Results show that the developed geocomplexity indicator, using a binary spatial matrix, can effectively explain spatial errors arising from models, including 17%-47% of errors in aspatial models and 14% in a spatial model. The experiments in this study support our hypothesis that geocomplexity is an essential component in explaining spatial errors. The proposed geocomplexity indicator, along with our hypothesis, has the potential for advancing the understanding complex geospatial systems and enabling applications in various fields related to spatial data analysis.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1449 - 1469"},"PeriodicalIF":5.7,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41760720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1080/13658816.2023.2189724
Matthias Weigand, Simon Worbis, M. Sapena, H. Taubenböck
Abstract In the past decade, the number of refugees and internally displaced people (IDP) has doubled. This prompted the construction of more refugee camps and the proliferation of existing camps with diverse structural morphologies. Satellite imagery and machine learning (ML) are increasingly utilized to map these camps. However, there exists no standardized inventory that systemizes the built-up structures of these camps. In this study, we conceptualize the settlement morphology of refugee and IDP camps from satellite images and create a structure catalogue. Using visual image interpretation (VII) of very-high-resolution and multitemporal imagery, we compile a global database of settlement structures from 285 camps across 1,053 observations. This catalogue is subsequently used to synthesize patterns in camp structures and temporal dynamics. The results show stark variations in settlement structures across camps. Despite some similar regional patterns, stark differences in morphologies are a testament to the global heterogeneous landscape of refugee and IDP camp structures. These findings highlight the importance of considering morphological differences in image analyses across camps in future designs of ML-based automated detection and monitoring efforts. Therein, the Structure Catalogue serves as an important foundation for future earth observation for humanitarian applications.
{"title":"A structural catalogue of the settlement morphology in refugee and IDP camps","authors":"Matthias Weigand, Simon Worbis, M. Sapena, H. Taubenböck","doi":"10.1080/13658816.2023.2189724","DOIUrl":"https://doi.org/10.1080/13658816.2023.2189724","url":null,"abstract":"Abstract In the past decade, the number of refugees and internally displaced people (IDP) has doubled. This prompted the construction of more refugee camps and the proliferation of existing camps with diverse structural morphologies. Satellite imagery and machine learning (ML) are increasingly utilized to map these camps. However, there exists no standardized inventory that systemizes the built-up structures of these camps. In this study, we conceptualize the settlement morphology of refugee and IDP camps from satellite images and create a structure catalogue. Using visual image interpretation (VII) of very-high-resolution and multitemporal imagery, we compile a global database of settlement structures from 285 camps across 1,053 observations. This catalogue is subsequently used to synthesize patterns in camp structures and temporal dynamics. The results show stark variations in settlement structures across camps. Despite some similar regional patterns, stark differences in morphologies are a testament to the global heterogeneous landscape of refugee and IDP camp structures. These findings highlight the importance of considering morphological differences in image analyses across camps in future designs of ML-based automated detection and monitoring efforts. Therein, the Structure Catalogue serves as an important foundation for future earth observation for humanitarian applications.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1338 - 1364"},"PeriodicalIF":5.7,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47865787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1080/13658816.2023.2178001
Haowen Luo, Bo Huang
Abstract Land use planning seeks to outline the future location and type of development activity. The planning process should reconcile development with environmental conservation and other concerns pertaining to sustainability; hence multi-objective spatial optimization is considered an effective tool to serve this purpose. However, as the number of social, economic, and environmental objectives increases, especially when numerous spatial units exist, the curse of dimensionality becomes a serious problem, making previous methods unsuitable. In this paper, we formulate a probabilistic framework based on the gradient descent algorithm (GDA) to search for Pareto optimal solutions more effectively and efficiently. Under this framework, land use as decision parameter(s) in each cell is represented as a probability vector instead of an integer value. Thus, the objectives can be designed as differentiable functions such that the GDA can be used for multi-objective optimization. An initial experiment is conducted using simulation data to compare the GDA with the genetic algorithm, with the results showing that the GDA outperforms the genetic algorithm, especially for large-scale problems. Furthermore, the outcomes in a real-world case study of Shenzhen demonstrate that the proposed framework is capable of generating effective optimal scenarios more efficiently, rendering it a pragmatic tool for planning practices.
{"title":"A probabilistic framework with the gradient-based method for multi-objective land use optimization","authors":"Haowen Luo, Bo Huang","doi":"10.1080/13658816.2023.2178001","DOIUrl":"https://doi.org/10.1080/13658816.2023.2178001","url":null,"abstract":"Abstract Land use planning seeks to outline the future location and type of development activity. The planning process should reconcile development with environmental conservation and other concerns pertaining to sustainability; hence multi-objective spatial optimization is considered an effective tool to serve this purpose. However, as the number of social, economic, and environmental objectives increases, especially when numerous spatial units exist, the curse of dimensionality becomes a serious problem, making previous methods unsuitable. In this paper, we formulate a probabilistic framework based on the gradient descent algorithm (GDA) to search for Pareto optimal solutions more effectively and efficiently. Under this framework, land use as decision parameter(s) in each cell is represented as a probability vector instead of an integer value. Thus, the objectives can be designed as differentiable functions such that the GDA can be used for multi-objective optimization. An initial experiment is conducted using simulation data to compare the GDA with the genetic algorithm, with the results showing that the GDA outperforms the genetic algorithm, especially for large-scale problems. Furthermore, the outcomes in a real-world case study of Shenzhen demonstrate that the proposed framework is capable of generating effective optimal scenarios more efficiently, rendering it a pragmatic tool for planning practices.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1128 - 1156"},"PeriodicalIF":5.7,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44542226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1080/13658816.2023.2191674
A. Bassiri, Terence Lines, Miguel Fidel Pereira
Abstract Three-dimensional (3D) maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This paper advances that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution.
{"title":"Scalable 3D mapping of cities using computer vision and signals of opportunity","authors":"A. Bassiri, Terence Lines, Miguel Fidel Pereira","doi":"10.1080/13658816.2023.2191674","DOIUrl":"https://doi.org/10.1080/13658816.2023.2191674","url":null,"abstract":"Abstract Three-dimensional (3D) maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This paper advances that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1470 - 1495"},"PeriodicalIF":5.7,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47909728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-24DOI: 10.1080/13658816.2023.2190371
Yao Yao, Siqi Lei, Zijin Guo, Yuanyuan Li, Shuliang Ren, Zhihang Liu, Qingfeng Guan, Peng Luo
Abstract Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we propose a hybrid sparrow search algorithm (SA-SSA) by combining the sparrow search algorithm with fast computational speed and the simulated annealing algorithm with the ability to get the global optimum solution. Wuhan city was selected for logistics optimization experiments. The results show that the SA-SSA can optimize large-scale urban logistics with guaranteed efficiency and solution quality. Compared with simulated annealing, sparrow search, and genetic algorithm, the cost of SA-SSA was reduced by 17.12, 18.62, and 14.72%, respectively. Although the cost of SS-SSA was 11.50% higher than the ant colony algorithm, its computation time was reduced by 99.06%. In addition, the simulation experiments were conducted to explore the impact of spatial elements on the algorithm performance. The SA-SSA can provide high-quality solutions with high efficiency, considering the constraints of many customers and complex road networks. It can support realizing the scientific scheduling of distribution vehicles by logistics enterprises.
{"title":"Fast optimization for large scale logistics in complex urban systems using the hybrid sparrow search algorithm","authors":"Yao Yao, Siqi Lei, Zijin Guo, Yuanyuan Li, Shuliang Ren, Zhihang Liu, Qingfeng Guan, Peng Luo","doi":"10.1080/13658816.2023.2190371","DOIUrl":"https://doi.org/10.1080/13658816.2023.2190371","url":null,"abstract":"Abstract Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we propose a hybrid sparrow search algorithm (SA-SSA) by combining the sparrow search algorithm with fast computational speed and the simulated annealing algorithm with the ability to get the global optimum solution. Wuhan city was selected for logistics optimization experiments. The results show that the SA-SSA can optimize large-scale urban logistics with guaranteed efficiency and solution quality. Compared with simulated annealing, sparrow search, and genetic algorithm, the cost of SA-SSA was reduced by 17.12, 18.62, and 14.72%, respectively. Although the cost of SS-SSA was 11.50% higher than the ant colony algorithm, its computation time was reduced by 99.06%. In addition, the simulation experiments were conducted to explore the impact of spatial elements on the algorithm performance. The SA-SSA can provide high-quality solutions with high efficiency, considering the constraints of many customers and complex road networks. It can support realizing the scientific scheduling of distribution vehicles by logistics enterprises.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1420 - 1448"},"PeriodicalIF":5.7,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42415101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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