Transactions in GIS最新文献

{"title":"DePNR: A DeBERTa‐based deep learning model with complete position embedding for place name recognition from geographical literature","authors":"Weirong Li, Kai Sun, Shu Wang, Yunqiang Zhu, Xiaoliang Dai, Lei Hu","doi":"10.1111/tgis.13170","DOIUrl":"https://doi.org/10.1111/tgis.13170","url":null,"abstract":"Place names play an important role in linking physical places to human perception and are highly frequently used in the daily lives of people to refer to places in natural language. However, many place names may not be recorded in typical gazetteers due to their new establishment, colloquial nature, and different concerns. These unrecorded toponyms are often discussed in geographical literature; thus, it is necessary to automatically identify them from geographical literature and update existing gazetteers using computational approaches. Currently, the most advanced approaches are deep learning‐based models. However, existing models used only partial position information rather than complete position information of words in a sentence, which limits their performance in recognizing toponyms. To this end, we develop DePNR, a DeBERTa‐based deep learning model with complete position embedding for place name recognition from geographical literature. We train DePNR on two datasets and test it on a real dataset from geographical literature to evaluate its performance. The results show that DePNR achieves an <jats:italic>F</jats:italic>‐score of 0.8282, outperforming previous approaches, and can recognize new toponyms from literature text, potentially enriching existing gazetteers.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839496","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}

{"title":"Uncertainty propagation analysis for distributed hydrological forecasting using a neural network","authors":"Jaqueline A. J. P. Soares, Michael M. Diniz, Luiz Bacelar, Glauston R. T. Lima, Allan K. S. Soares, Stephan Stephany, Leonardo B. L. Santos","doi":"10.1111/tgis.13169","DOIUrl":"https://doi.org/10.1111/tgis.13169","url":null,"abstract":"The last few decades have presented a significant increase in hydrological disasters, such as floods. In some countries, most of the environmental, socioeconomic, and biodiversity losses are caused by floods. Thus, flood forecasting is crucial to support an efficient disaster warning system. This work proposes a model for hydrological forecasting based on a neural network with a geographically aligned input named GeoNN. It employs weather radar data to obtain accumulated rainfall in each grid cell of the watershed and make 15‐ and 120‐min predictions of the outlet river level. An uncertainty propagation analysis was performed for GeoNN from a collection of test cases obtained by either using different schemes of the dataset partitioning or introducing different additive‐noise rates to the input data to provide a probability of flood occurrence and also an ensemble prediction. Both this probability and the ensemble were able to detect occurrences of river levels exceeding a given flood threshold.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810406","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}

{"title":"A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit","authors":"Haoran Zeng, Haijun Wang, Bin Zhang","doi":"10.1111/tgis.13163","DOIUrl":"https://doi.org/10.1111/tgis.13163","url":null,"abstract":"Cellular automata (CA) models are effective tools for simulating future urban expansion. With the widespread use of high‐resolution geospatial data for CA simulation, the computational intensity of CA models has increased. Additionally, due to the continuous development of CA modeling research, many scholars have made improvements to the models to enhance their simulation accuracy, resulting in an increasing computational complexity of the model. Consequently, the simulation task based on CA requires vast computing time and memory space. In recent years, deep learning (DL) has experienced rapid development. Many open‐source DL frameworks support graphic processing unit (GPU) parallel computing and provide efficient application programming interfaces (APIs) that can be easily called to handle tasks of interest. In this study, a high‐performance CA model was constructed based on the similarity between the neighborhood effect calculation process of the CA model and the convolutional process in a convolutional neural network (CNN). The convolution function in the DL library is used to calculate the neighborhood effect of the CA model to reduce the time and memory consumption of CA‐based simulation. The experimental results show that compared with the conventional CA model, the execution time of the GPU‐convolution‐CA model proposed in this study has been reduced by more than 98%.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799435","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}

{"title":"A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint","authors":"Keyvan Bagheri, N. Samany, A. Toomanian, Mohammadreza Jelokhani‐Niaraki, L. Hajibabai","doi":"10.1111/tgis.13159","DOIUrl":"https://doi.org/10.1111/tgis.13159","url":null,"abstract":"Medical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140665301","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}

{"title":"A reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph","authors":"Yiting Lin, Daichao Li, Peng Peng, Jianqin Liang, Fei Ding, Xinlei Jin, Zhan Zeng","doi":"10.1111/tgis.13166","DOIUrl":"https://doi.org/10.1111/tgis.13166","url":null,"abstract":"The lack of multidimensional knowledge means that current reasoning methods for rice fertilization cannot make decisions accurate when faced with complex spatiotemporal conditions in general. Therefore, we propose a reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph. First, we systematically organize multisource expert knowledge about rice fertilization, and construct an ontology for rice fertilization consisting of five core elements: rice variety, planting environment, nutrition diagnosis, fertilization schemes, and time and place. Spatiotemporal differences in rice fertilization knowledge are expressed by assessing spatiotemporal concepts, relations, and state instances. Second, we propose a reasoning method for rice fertilization strategy based on the constructed knowledge graph. This method leverages a certainty factor model for nutrition diagnosis and integrates case‐based and rule‐based reasoning to determine fertilization schemes for different stages. Finally, taking Pucheng County, China, as an example, knowledge from crowd‐sensing data is obtained to construct a knowledge graph using the proposed method. The results demonstrate the method can support the expression and complex reasoning of rice fertilization decisions under different spatiotemporal conditions.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623726","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}

{"title":"Interactive visual query of density maps on latent space via flow‐based models","authors":"Ning Li, Tianyi Liang, Shiqi Jiang, Changbo Wang, Chenhui Li","doi":"10.1111/tgis.13164","DOIUrl":"https://doi.org/10.1111/tgis.13164","url":null,"abstract":"Visual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570349","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}

{"title":"Applicability of spatial planning system package for the LADM Turkey country profile","authors":"Okan Yılmaz, Mehmet Alkan","doi":"10.1111/tgis.13165","DOIUrl":"https://doi.org/10.1111/tgis.13165","url":null,"abstract":"Spatial planning plays a crucial role in shaping the future of urban development and land administration. While land registration information is necessary for spatial planning processes, it is also probable that changes in land registration data occur in line with spatial plan decisions. Development of the Land Administration Domain Model (LADM) Edition II It also aimed to extend its existing conceptual model with spatial plan data, considering the close connection between the two systems. The study aims to design a conceptual model for Turkey's spatial planning system within the LADM Turkey country profile context. This article researches the capability of the proposed conceptual model for representing spatial planning data with instance‐level diagrams and the implementation opportunities of a technical model. To demonstrate the functionality of the proposed model, the zoning status certificate, which contains spatial plan and land registration data and is provided to inform about the legal conditions before development, is chosen as the mission. The results show that the LADM Turkey country profile extended with spatial planning system data can represent spatial plan data and be implemented in a technical model to support land administration applications.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603018","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}

{"title":"A cloud‐based solution for trustless indigenous data sovereignty: Protecting Māori biodiversity management data in Aotearoa New Zealand","authors":"Pankajeshwara Sharma, Michael Martin, David Swanlund, Cecilia Latham, Dean Anderson, Waitangi Wood","doi":"10.1111/tgis.13153","DOIUrl":"https://doi.org/10.1111/tgis.13153","url":null,"abstract":"Indigenous peoples should be able to govern data about themselves, their territories, resources, and ways of life, collected by themselves or others. However, the progressive use of cloud computing for the geoweb raises data security and privacy concerns. We propose a complete and trustless approach for indigenous geospatial data sovereignty on the cloud by furnishing security functions at the core—the web browser. Geomasking permits sharing an anonymized dataset with less privileged users, while the original is protected and shared with sovereign data owners via public‐key encryption. The encrypted dataset's hash value is notarized on the blockchain for the verification of its authenticity when on the cloud. The application was designed for the protection of Biodiversity Management Areas stewarded by the Māori people in Aotearoa New Zealand. It enables diversified functions of geospatial data protection compared with previous works focusing on the cloud by solving data‐sharing problems without relying on a third party.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570440","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}

{"title":"Correction to ‘Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches’","authors":"","doi":"10.1111/tgis.13162","DOIUrl":"https://doi.org/10.1111/tgis.13162","url":null,"abstract":"<p>Wöltche, A. (<span>2023</span>). Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches. <i>Transactions in GIS</i>, <i>27</i>, 1959–1991. https://doi.org/10.1111/tgis.13107</p>\u0000<div>The numbering of the list on pages 1960 to 1961 in the Introduction is incorrect. The correct numbering of this list is stated below. <ul>\u0000<li><span>1.1 </span>Explains the terms we use in the remainder of this work.</li>\u0000<li><span>1.2 </span>Illustrates the main challenges of map matching.</li>\u0000<li><span>1.3 </span>Provides an overview of relevant literature and state-of-the-art Open Source Software (OSS).</li>\u0000<li><span>1.4 </span>Introduces our novel approach and displays the challenges we specifically address.</li>\u0000<li><span>1.5 </span>Gives a real-world example of how the current state-of-the-art compares to our new approach, which we describe as follows.</li>\u0000<li><span>2 </span>Gives the technology roadmap and explains our new approach, that is, a combination of several new and improved technologies:</li>\u0000<li><span>2.1 </span>Introduces our custom Trajectory Simplification (TS) algorithm that is used for FCD preprocessing. We combine a customized Douglas-Peucker algorithm with a custom point-cluster-merging algorithm to reduce simple noise patterns before actual map matching to improve performance.</li>\u0000<li><span>2.2 </span>Explains Candidate Search (CS), which is used to select possible mapping locations in the given road network for a given track. CS selects a candidate set for each point in a track by pointing to spatially nearby road locations (candidates). From each pair of adjacent candidate sets, one candidate is chosen so that a route between them (candidate route) can be computed.</li>\u0000<li><span>2.3 </span>Introduces our novel Candidate Adoption (CA) feature that depends on CS. It takes into account for each candidate set additional candidates from the surrounding candidate sets. This allows our map-matching algorithm to stochastically handle even large outliers and high noise of tracks in order to further improve accuracy.</li>\u0000<li><span>2.4 </span>Introduces our new comparison algorithm that allows to extract and evaluate the differences and similarities of alternative routes within the same road network, for example, matches to a given ground truth. With its ability to handle small inaccuracies between the given data, this algorithm is used in our benchmark for comparing the results of our new approach with existing solutions.</li>\u0000<li><span>2.5 </span>Introduces our new map-matching model based on Markov decision processes (MDPs) and Reinforcement Learning (RL) algorithms. The MDP uses absolute rewards for optimizing map-matching solutions. These rewards are calculated with our new map-matching metrics which evaluate direction changes on the candidate routes in addition to distances and lengths between track segments and candidate routes. Direction changes facilitate CA to penalize","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570014","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}

{"title":"The integration of GIS location‐based APIs and urban growth modeling for improved geographic access to hospital services","authors":"Anang Wahyu Sejati, Savira Nur Afifah Kusuma Putri, Imam Buchori, Walter Timo de Vries, Ghiffari Barbarossa, Candra Margarena, Chely Novia Bramiana","doi":"10.1111/tgis.13158","DOIUrl":"https://doi.org/10.1111/tgis.13158","url":null,"abstract":"This article aims to present an integration model of GIS with open data sourced from application programming interface (API) as a solution for the location set covering problem (LSCP) with an urban land dynamics model. The development of GIS which is increasingly advanced makes traditional GIS transition in the open data era to become more modern. One of the benefits is to help urban planners in determining the allocation of health facilities such as hospitals. This research takes the case of hospital service coverage during emergencies, especially during the COVID‐19 extraordinary event in Metropolitan Semarang, Indonesia. In addition to utilizing API‐base Location, the model process also uses a Cellular Automata‐based land use prediction model. Thus, the facility location plan not only considers service coverage but also land use growth which is a reflection of population growth. To analyze the problem of inequity of hospital services, this research combined the location‐based APIs‐based service area model with the urban growth model to evaluate the existing condition and predict the future of hospital service demand. It also uses the emergency standard with a maximum service distance of 1500 m and a maximum travel time of 7 min. The model confirmed that there are still critical spots not served by hospitals in Semarang City. According to the concept of health and place, it is essential to recommend adding two hospitals in unserved areas so that services are more evenly distributed in the future, especially in emergencies.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301403","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}