Marina Georgati, João Monteiro, Bruno Martins, Carsten Keßler, Henning Sten Hansen
Spatially aggregated data on socio-demographic groups often fail to capture the population's spatial heterogeneity in cities. This poses challenges for urban planning, particularly when addressing the needs of groups such as migrants or families with children. Moreover, the commonly provided aggregated units, such as census tracts, vary in size and across data sources. Existing literature on disaggregation typically handles individual subgroups separately, ignoring their interrelations in the downscaling process. This article explores the potentials of multi-output regression models for simultaneous spatial downscaling of multiple groups and conducts a detailed spatial error analysis using individualized neighborhoods. We experiment with self-training gradient-boosting trees and fully convolutional neural networks, assessing the quality of results against ground truth data at the target resolution. We show that the evaluation of the disaggregated results at this detailed resolution requires unconventional methods. The methodology proves convenient and achieves high-accuracy results using input datasets of building features.
{"title":"Modeling population distribution: A visual and quantitative analysis of gradient boosting and deep learning models for multi-output spatial disaggregation","authors":"Marina Georgati, João Monteiro, Bruno Martins, Carsten Keßler, Henning Sten Hansen","doi":"10.1111/tgis.13130","DOIUrl":"https://doi.org/10.1111/tgis.13130","url":null,"abstract":"Spatially aggregated data on socio-demographic groups often fail to capture the population's spatial heterogeneity in cities. This poses challenges for urban planning, particularly when addressing the needs of groups such as migrants or families with children. Moreover, the commonly provided aggregated units, such as census tracts, vary in size and across data sources. Existing literature on disaggregation typically handles individual subgroups separately, ignoring their interrelations in the downscaling process. This article explores the potentials of multi-output regression models for simultaneous spatial downscaling of multiple groups and conducts a detailed spatial error analysis using individualized neighborhoods. We experiment with self-training gradient-boosting trees and fully convolutional neural networks, assessing the quality of results against ground truth data at the target resolution. We show that the evaluation of the disaggregated results at this detailed resolution requires unconventional methods. The methodology proves convenient and achieves high-accuracy results using input datasets of building features.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"59 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139412734","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}
Spatial direction relationships act as links between visually perceived data and natural language in cartography and geographic information systems. Although the existing direction models are able to compute spatial direction relationships, their adaptability to unique users of Wemap, such as individuals with limited spatial orientation skills (ILSOS), is minimal. In particular, the ability to identify locations and directions on maps can pose serious challenges for ILSOS. As such, a Wemap-based direction model was developed in this study to address this deficiency, specifically targeting ILSOS. To address these challenges, a model for calculating spatial direction relationships was constructed. Subsequently, a questionnaire was administered to verify the consistency of the proposed model with the spatial perceptions of ILSOS. The experimental results demonstrate that (1) the proposed model can calculate the spatial direction relationships between a reference object and source target based on absolute reference frames; (2) ILSOS account for a certain proportion of the population and have a superior ability to distinguish spatial over geographic directions; and (3) the calculation results of the proposed model are consistent with the spatial cognition of ILSOS.
{"title":"Wemap-based direction model for individuals with limited spatial orientation skills","authors":"Xiaolong Wang, Haowen Yan, Zhuo Wang, Xiaorong Gao","doi":"10.1111/tgis.13129","DOIUrl":"https://doi.org/10.1111/tgis.13129","url":null,"abstract":"Spatial direction relationships act as links between visually perceived data and natural language in cartography and geographic information systems. Although the existing direction models are able to compute spatial direction relationships, their adaptability to unique users of Wemap, such as individuals with limited spatial orientation skills (ILSOS), is minimal. In particular, the ability to identify locations and directions on maps can pose serious challenges for ILSOS. As such, a Wemap-based direction model was developed in this study to address this deficiency, specifically targeting ILSOS. To address these challenges, a model for calculating spatial direction relationships was constructed. Subsequently, a questionnaire was administered to verify the consistency of the proposed model with the spatial perceptions of ILSOS. The experimental results demonstrate that (1) the proposed model can calculate the spatial direction relationships between a reference object and source target based on absolute reference frames; (2) ILSOS account for a certain proportion of the population and have a superior ability to distinguish spatial over geographic directions; and (3) the calculation results of the proposed model are consistent with the spatial cognition of ILSOS.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"14 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139413353","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}
Measuring trajectory similarity is a fundamental algorithm in trajectory data mining, playing a key role in trajectory clustering, pattern mining, and classification, for instance. However, existing trajectory similarity measures based on vector representation have challenges in achieving both fast and accurate similarity measurements. On one hand, most existing methods have a high computational complexity of O(n × m), resulting in low efficiency. On the other hand, many of them are sensitive to trajectory sampling rates and lack of accuracy. This article proposes QuadGridSIM, a quadrilateral grid‐based method for trajectory similarity analysis, which enables high‐performance trajectory similarity measure without the cost of low effectiveness. Specifically, we first realize the multiscale coding representation of trajectory data based on quadrilateral discrete grids. Then, a novel trajectory similarity measure is defined to reduce the computational complexity of O(n). Several effectiveness properties of QuadGridSIM are further optimized, including the spatial overlap, directionality, symmetry, and robustness to sampling rate variations. Experimental results based on real‐world and simulated taxi trajectory data indicate that QuadGridSIM outperforms most of the other tested algorithms developed previously in terms of effectiveness, particularly in its robustness regarding trajectory sampling rates. Furthermore, QuadGridSIM exhibits superior performance and is approximately one order of magnitude faster than previous methods in the literature. QuadGridSIM provides a solution to the low‐efficiency problem of massive trajectory similarity analysis and can be applied in many application scenarios, such as route recommendation and suspect detection.
{"title":"QuadGridSIM: A quadrilateral grid‐based method for high‐performance and robust trajectory similarity analysis","authors":"Juqing Liu, Jun Li, Linwei Qiao, Mingke Li, Emmanuel Stefanakis, Xuesheng Zhao, Qian Huang, Hao Wang, Chengye Zhang","doi":"10.1111/tgis.13126","DOIUrl":"https://doi.org/10.1111/tgis.13126","url":null,"abstract":"Measuring trajectory similarity is a fundamental algorithm in trajectory data mining, playing a key role in trajectory clustering, pattern mining, and classification, for instance. However, existing trajectory similarity measures based on vector representation have challenges in achieving both fast and accurate similarity measurements. On one hand, most existing methods have a high computational complexity of O(n × m), resulting in low efficiency. On the other hand, many of them are sensitive to trajectory sampling rates and lack of accuracy. This article proposes QuadGridSIM, a quadrilateral grid‐based method for trajectory similarity analysis, which enables high‐performance trajectory similarity measure without the cost of low effectiveness. Specifically, we first realize the multiscale coding representation of trajectory data based on quadrilateral discrete grids. Then, a novel trajectory similarity measure is defined to reduce the computational complexity of O(n). Several effectiveness properties of QuadGridSIM are further optimized, including the spatial overlap, directionality, symmetry, and robustness to sampling rate variations. Experimental results based on real‐world and simulated taxi trajectory data indicate that QuadGridSIM outperforms most of the other tested algorithms developed previously in terms of effectiveness, particularly in its robustness regarding trajectory sampling rates. Furthermore, QuadGridSIM exhibits superior performance and is approximately one order of magnitude faster than previous methods in the literature. QuadGridSIM provides a solution to the low‐efficiency problem of massive trajectory similarity analysis and can be applied in many application scenarios, such as route recommendation and suspect detection.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"35 8","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385170","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}
Studying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS-weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix-projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID-19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID-19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were “spindle-shaped,” meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was “wave-shaped,” indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies.
{"title":"Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial–temporal–semantic features","authors":"Xuehua Han, Juanle Wang, Xiaodong Zhang, Liang Wang, Dandan Xu","doi":"10.1111/tgis.13125","DOIUrl":"https://doi.org/10.1111/tgis.13125","url":null,"abstract":"Studying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS-weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix-projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID-19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID-19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were “spindle-shaped,” meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was “wave-shaped,” indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"26 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139079234","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}
Ling Yang, Xingyu Zhou, Xin Yang, Yang Chen, Haiping Zhang, Jiaming Na
Understanding the 3D spatial structure of a city is increasingly essential for addressing various environmental and management issues, surpassing the importance of traditional 2D analysis. However, existing studies neglect the diversity of building height and still lack a clear description of 3D urban structure. This article proposes a new framework to uncover the 3D urban structure. Firstly, kernel density is employed to reveal the hierarchical spatial structure of buildings and the contour tree method is improved to quantitatively measure the spatial diversity and complexity. Then, the 3D urban structure is abstracted by spatial interpolation after feature filtration. Finally, this framework is applied to the central area of Chengdu City, revealing that: (1) Spatial structures of buildings with different heights exhibit significant diversity, location preference, and complexity; and (2) A globally “depression” 3D urban structure with low center—high periphery is obviously identified. This framework provides an effective way to reveal the 3D urban structure in a more intuitive and clearer way from various disordered urban buildings, which can be transferable to other cities and further facilitate sustainable planning and development of cities.
{"title":"A new hierarchical analysis framework of building heights: Towards a more intuitive understanding of 3D urban structure","authors":"Ling Yang, Xingyu Zhou, Xin Yang, Yang Chen, Haiping Zhang, Jiaming Na","doi":"10.1111/tgis.13123","DOIUrl":"https://doi.org/10.1111/tgis.13123","url":null,"abstract":"Understanding the 3D spatial structure of a city is increasingly essential for addressing various environmental and management issues, surpassing the importance of traditional 2D analysis. However, existing studies neglect the diversity of building height and still lack a clear description of 3D urban structure. This article proposes a new framework to uncover the 3D urban structure. Firstly, kernel density is employed to reveal the hierarchical spatial structure of buildings and the contour tree method is improved to quantitatively measure the spatial diversity and complexity. Then, the 3D urban structure is abstracted by spatial interpolation after feature filtration. Finally, this framework is applied to the central area of Chengdu City, revealing that: (1) Spatial structures of buildings with different heights exhibit significant diversity, location preference, and complexity; and (2) A globally “depression” 3D urban structure with low center—high periphery is obviously identified. This framework provides an effective way to reveal the 3D urban structure in a more intuitive and clearer way from various disordered urban buildings, which can be transferable to other cities and further facilitate sustainable planning and development of cities.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"3 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139057638","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}
Shaonan Zhu, YiXin Jiang, Jun Zhang, Qiang Dai, Xin Yang
Urban flooding is a growing source of natural hazards, significantly threatening the safety of sustainable development in cities. The distribution of flood risks is heterogeneous, so it is crucial to allocate emergency resources reasonably. This article develops an analytical framework to evaluate the effect of urban flooding on emergency responses based on social sensing data. Initially, we designed a Weibo search pattern and used natural language processing technologies to get high-risk flood points. Then, assuming that such high-risk flood points can disrupt traffic, we assessed urban emergency shelter accessibility after flooding events. Finally, we carried out an evaluation of spatial fairness between population and emergency shelter accessibility through spatial correlation analysis. We analyzed the urban area of Nanjing as a case study, extracting 37 high-risk flood points from the past 5 years. The results highlight that existing emergency shelters fall short in accommodating the needs of urban residents under disaster conditions. This disparity is notably amplified in high-risk flood points. By measuring the impact of flooding quantitatively, we expect to promote a more comprehensive management on urban flood risks.
{"title":"Evaluating the effect of urban flooding on spatial accessibility to emergency shelters based on social sensing data","authors":"Shaonan Zhu, YiXin Jiang, Jun Zhang, Qiang Dai, Xin Yang","doi":"10.1111/tgis.13127","DOIUrl":"https://doi.org/10.1111/tgis.13127","url":null,"abstract":"Urban flooding is a growing source of natural hazards, significantly threatening the safety of sustainable development in cities. The distribution of flood risks is heterogeneous, so it is crucial to allocate emergency resources reasonably. This article develops an analytical framework to evaluate the effect of urban flooding on emergency responses based on social sensing data. Initially, we designed a Weibo search pattern and used natural language processing technologies to get high-risk flood points. Then, assuming that such high-risk flood points can disrupt traffic, we assessed urban emergency shelter accessibility after flooding events. Finally, we carried out an evaluation of spatial fairness between population and emergency shelter accessibility through spatial correlation analysis. We analyzed the urban area of Nanjing as a case study, extracting 37 high-risk flood points from the past 5 years. The results highlight that existing emergency shelters fall short in accommodating the needs of urban residents under disaster conditions. This disparity is notably amplified in high-risk flood points. By measuring the impact of flooding quantitatively, we expect to promote a more comprehensive management on urban flood risks.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"258 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139057813","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 utilization of mobile sensing through the deployment of sensors on third-party vehicles that pass by an area offers notable benefits in terms of spatiotemporal coverage, precision, timeliness, and cost-effectiveness for monitoring urban environments. However, the diverse travel behaviors exhibited by different types of vehicle fleets result in varying levels of sensing powers. Drawing inspiration from these phenomena, this study quantifies and compares the travel patterns and sensing powers of bus and taxi fleets. The experimental findings unveiled several noteworthy laws, such as a diminishing rate of increase in sensing power with the stable increasing vehicle number, the high sensing power exhibited by taxis, the comparable spatial coverage between buses, and the relatively low uncertainty associated with buses' sensing power. Furthermore, the impact of travel patterns (i.e., semi-random behavior of taxis and deterministic behavior of buses) on the powers of mobile sensing was revealed. The aforementioned findings provide valuable insights for the development of efficient mobile sensing schemes and hold significant relevance for the implementation of smart city infrastructure.
{"title":"Revealing the mobile sensing powers of semi-random and deterministic “drive-by” vehicle fleets","authors":"Jincheng Jiang, Junjie Wang, Hongming Yao, Zhenxin Zhang, Zhihua Xu","doi":"10.1111/tgis.13124","DOIUrl":"https://doi.org/10.1111/tgis.13124","url":null,"abstract":"The utilization of mobile sensing through the deployment of sensors on third-party vehicles that pass by an area offers notable benefits in terms of spatiotemporal coverage, precision, timeliness, and cost-effectiveness for monitoring urban environments. However, the diverse travel behaviors exhibited by different types of vehicle fleets result in varying levels of sensing powers. Drawing inspiration from these phenomena, this study quantifies and compares the travel patterns and sensing powers of bus and taxi fleets. The experimental findings unveiled several noteworthy laws, such as a diminishing rate of increase in sensing power with the stable increasing vehicle number, the high sensing power exhibited by taxis, the comparable spatial coverage between buses, and the relatively low uncertainty associated with buses' sensing power. Furthermore, the impact of travel patterns (i.e., semi-random behavior of taxis and deterministic behavior of buses) on the powers of mobile sensing was revealed. The aforementioned findings provide valuable insights for the development of efficient mobile sensing schemes and hold significant relevance for the implementation of smart city infrastructure.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"50 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139036116","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}
Yue Fan, Huiwen Wang, Lihong Wang, Shu Guo, Jing Liu
Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node-Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time-Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T-Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.
{"title":"A novel trajectory similarity measurement method based on node-sequence hierarchical digraph","authors":"Yue Fan, Huiwen Wang, Lihong Wang, Shu Guo, Jing Liu","doi":"10.1111/tgis.13121","DOIUrl":"https://doi.org/10.1111/tgis.13121","url":null,"abstract":"Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node-Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time-Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T-Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"231 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575540","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}
Wagner da Silva Billa, Leonardo Bacelar Lima Santos, Rogério Galante Negri
Rainfall is one of the primary triggers for many geological and hydrological natural disasters. While the geological events are related to mass movements in land collapse due to waterlogging, the hydrological ones are usually assigned to runoff or flooding. Studies in the literature propose predicting mass movement events as a function of accumulated rainfall levels recorded at distinct periods. According to these approaches, a two-dimensional rainfall levels feature space is segmented into the occurrence and non-occurrence decision regions by an empirical critical curve (CC). Although this scheme may easily be extended to other purposes and applications, studies in the literature need to discuss its use for flooding prediction. In light of this motivation, the present study is unfolded in (1) verifying that defining CCs in the rainfall levels feature space is a practical approach for flooding prediction and (2) analyzing how geospatial components interact with rainfall levels and flooding prediction. A database containing the rainfall levels recorded for flooding and non-flooding events in São Paulo city, Brazil, regarding the period 2015–2016, was considered in this study. The results indicate good accuracy for flooding prediction using only partial rain, which can be improved by adding physical characteristics of the flooding locations, demonstrating a direct correlation with spatial interactions, and rainfall levels.
降雨是许多地质和水文自然灾害的主要诱因之一。地质事件与内涝导致的土地塌陷大规模移动有关，而水文事件通常与径流或洪水有关。文献研究建议根据不同时期记录的累积降雨量来预测大规模移动事件。根据这些方法，通过经验临界曲线（CC）将二维降雨量特征空间划分为发生和未发生决策区域。虽然这种方法很容易扩展到其他目的和应用中，但文献研究需要讨论其在洪水预测中的应用。有鉴于此，本研究在以下两个方面展开：(1) 验证在降雨量特征空间中定义 CC 是洪水预测的实用方法；(2) 分析地理空间要素如何与降雨量和洪水预测相互作用。本研究考虑了一个数据库，其中包含巴西圣保罗市 2015-2016 年期间洪水和非洪水事件的降雨量记录。研究结果表明，仅使用部分降雨量就能很好地预测洪水的准确性，通过增加洪水地点的物理特征，可以提高洪水预测的准确性，这表明空间相互作用与降雨量直接相关。
{"title":"Analyzing the spatial interactions between rainfall levels and flooding prediction in São Paulo","authors":"Wagner da Silva Billa, Leonardo Bacelar Lima Santos, Rogério Galante Negri","doi":"10.1111/tgis.13116","DOIUrl":"https://doi.org/10.1111/tgis.13116","url":null,"abstract":"Rainfall is one of the primary triggers for many geological and hydrological natural disasters. While the geological events are related to mass movements in land collapse due to waterlogging, the hydrological ones are usually assigned to runoff or flooding. Studies in the literature propose predicting mass movement events as a function of accumulated rainfall levels recorded at distinct periods. According to these approaches, a two-dimensional rainfall levels feature space is segmented into the occurrence and non-occurrence decision regions by an empirical critical curve (CC). Although this scheme may easily be extended to other purposes and applications, studies in the literature need to discuss its use for flooding prediction. In light of this motivation, the present study is unfolded in (1) verifying that defining CCs in the rainfall levels feature space is a practical approach for flooding prediction and (2) analyzing how geospatial components interact with rainfall levels and flooding prediction. A database containing the rainfall levels recorded for flooding and non-flooding events in São Paulo city, Brazil, regarding the period 2015–2016, was considered in this study. The results indicate good accuracy for flooding prediction using only partial rain, which can be improved by adding physical characteristics of the flooding locations, demonstrating a direct correlation with spatial interactions, and rainfall levels.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"195 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138561522","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}
To plan future land use, zoning plans (i.e., spatial plans) are prepared to get the most out of land for both the public and the government. These plans manifest which facilities can be built and where they can be built on land based on defined requirements such as building height and road length. The Land Administration Domain Model (LADM) is well-known and widely used standard for describing Rights, Restrictions, and Responsibilities (RRRs) with respect to land and buildings. The next version of the standard will contain the Spatial Plan Information (SPI) part to enable better land-use planning. Three-dimensional (3D) land-use planning has gained attention to delineate detailed requirements inclusively and allow different spatial analysis that provides a basis for decisions in the planning. Data standards pertaining to 3D geoinformation are vital to put into practice 3D spatial planning. To this extent, CityJSON is proposed for the effortless and efficient use of 3D city models. This article thus first aims to extend the CityJSON schema based on the proposed SPI part of the LADM such that it allows modeling, storing, visualizing, and utilizing the features and attributes required for the implementation of 3D spatial planning. Then, the usability of the proposed extension schema is demonstrated by the real-world use cases that benefit from the exemplary CityJSON files that are created based on approved zoning plans in the country. The results of this study show that there is an important opportunity coming from the integration of international standards that enables semantic information along with their spatial counterparts within 3D spatial planning.
{"title":"Implementation of 3D spatial planning through the integration of the standards","authors":"Dogus Guler","doi":"10.1111/tgis.13122","DOIUrl":"https://doi.org/10.1111/tgis.13122","url":null,"abstract":"To plan future land use, zoning plans (i.e., spatial plans) are prepared to get the most out of land for both the public and the government. These plans manifest which facilities can be built and where they can be built on land based on defined requirements such as building height and road length. The Land Administration Domain Model (LADM) is well-known and widely used standard for describing Rights, Restrictions, and Responsibilities (RRRs) with respect to land and buildings. The next version of the standard will contain the Spatial Plan Information (SPI) part to enable better land-use planning. Three-dimensional (3D) land-use planning has gained attention to delineate detailed requirements inclusively and allow different spatial analysis that provides a basis for decisions in the planning. Data standards pertaining to 3D geoinformation are vital to put into practice 3D spatial planning. To this extent, CityJSON is proposed for the effortless and efficient use of 3D city models. This article thus first aims to extend the CityJSON schema based on the proposed SPI part of the LADM such that it allows modeling, storing, visualizing, and utilizing the features and attributes required for the implementation of 3D spatial planning. Then, the usability of the proposed extension schema is demonstrated by the real-world use cases that benefit from the exemplary CityJSON files that are created based on approved zoning plans in the country. The results of this study show that there is an important opportunity coming from the integration of international standards that enables semantic information along with their spatial counterparts within 3D spatial planning.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138561587","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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