Christopher A. Ramezan, Aaron E. Maxwell, Joshua T. Meadows
As the demand for geospatial analytics continues to grow, geographic information systems (GIS) professionals are needed to build, operate, and maintain GIS technologies, data, and software to provide geospatial insights for modern industries and organizations. To best train the next generation of GIS professionals, an understanding of qualifications and requirements of GIS positions is needed. Thus, this work analyzes 508 GIS positions, grouped by position type (analysts, developers, educators, managers, specialists, technicians) to provide insights on key pre‐requisite requirements, such as education, experience, certifications, soft communication skills, programming skills, and knowledge of GIS or IT. In general, possession of a bachelor's degree in GIS, geography, or computer science, prior professional experience, and knowledge of GIS and IT software were common pre‐requisites for most GIS roles. Soft communication skills were also frequently desired for GIS roles. We also found that some position requirements tended to vary by position type, such as manager and developer roles requiring on average 5 years or higher prior experience, while analyst, specialist, and technician roles had much lower experience and education requirements. Higher education institutions and GIS training programs should note the desired requirements for GIS position types and continue to refine programs and develop pathways for success for aspiring GIS professionals.
{"title":"An analysis of qualifications and requirements for geographic information systems (GIS) positions in the United States","authors":"Christopher A. Ramezan, Aaron E. Maxwell, Joshua T. Meadows","doi":"10.1111/tgis.13176","DOIUrl":"https://doi.org/10.1111/tgis.13176","url":null,"abstract":"As the demand for geospatial analytics continues to grow, geographic information systems (GIS) professionals are needed to build, operate, and maintain GIS technologies, data, and software to provide geospatial insights for modern industries and organizations. To best train the next generation of GIS professionals, an understanding of qualifications and requirements of GIS positions is needed. Thus, this work analyzes 508 GIS positions, grouped by position type (analysts, developers, educators, managers, specialists, technicians) to provide insights on key pre‐requisite requirements, such as education, experience, certifications, soft communication skills, programming skills, and knowledge of GIS or IT. In general, possession of a bachelor's degree in GIS, geography, or computer science, prior professional experience, and knowledge of GIS and IT software were common pre‐requisites for most GIS roles. Soft communication skills were also frequently desired for GIS roles. We also found that some position requirements tended to vary by position type, such as manager and developer roles requiring on average 5 years or higher prior experience, while analyst, specialist, and technician roles had much lower experience and education requirements. Higher education institutions and GIS training programs should note the desired requirements for GIS position types and continue to refine programs and develop pathways for success for aspiring GIS professionals.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"7 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940014","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}
This article proposes a new approach to market area analysis. Market area analysis is conducted in various academic fields, such as retail geography, marketing science, transportation science, and tourism study. It aims to understand the factors that affect visitors' choice behavior, which improves the performance of various sites, such as stores, restaurants, museums, and stadiums. Methods for market area analysis, however, have not been fully developed in the literature. To fill the research gap, this article proposes new methods of market area analysis. The first method considers the relationship between a site and its visitors. Our focus is on the spatial pattern of visitors around a site. The second method discusses the spatial relationship between the visitors of two sites. We evaluate the competing relationship between different sites. We applied the methods to the analysis of mountain climbers in Japan. The results gave us useful and interesting empirical findings, indicating the method's soundness.
{"title":"Market area analysis with a focus on the spatial relationship between sites and their visitors","authors":"Yukio Sadahiro, Hidetaka Matsumoto","doi":"10.1111/tgis.13167","DOIUrl":"https://doi.org/10.1111/tgis.13167","url":null,"abstract":"This article proposes a new approach to market area analysis. Market area analysis is conducted in various academic fields, such as retail geography, marketing science, transportation science, and tourism study. It aims to understand the factors that affect visitors' choice behavior, which improves the performance of various sites, such as stores, restaurants, museums, and stadiums. Methods for market area analysis, however, have not been fully developed in the literature. To fill the research gap, this article proposes new methods of market area analysis. The first method considers the relationship between a site and its visitors. Our focus is on the spatial pattern of visitors around a site. The second method discusses the spatial relationship between the visitors of two sites. We evaluate the competing relationship between different sites. We applied the methods to the analysis of mountain climbers in Japan. The results gave us useful and interesting empirical findings, indicating the method's soundness.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"66 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942555","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}
Floods are becoming more widely acknowledged as a common occurrence of nature's dangers on a global scale. Although forecasting models primarily focus on timely warnings, models aimed at evaluating dangerous zones can play a vital role in shaping policies for adaptation, mitigation, and reducing the risk of disasters. Using machine learning techniques including hybrid black widow optimization (BWO) with XGBoost, LGBoost, and AdaBoost. We generate a flood susceptibility map for considered region of lower mahanadi basin (LMB). This study examines the effectiveness of these machine learning models in assessing and mapping flood susceptibility, while also providing suggestions for future research in this area. Flood susceptibility model was developed using 13 variables: Altitude, Aspect, Curvature, Distance from river, Drainage Density, Stream Power Index (SPI), Sediment Transport Index (STI), Rainfall intensity, Land Use Land Cover (LULC), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), Normalized Difference Vegetation Index (NDVI), and slope. Additionally, flood inventory data were incorporated into the model. Dataset was divided into a 70% portion for training model and a 30% portion for validating model. To assess the performance of the model, several evaluation metrics were employed, including receiver operating characteristic (ROC) curve and other performance indices. Evaluation of flood susceptibility mapping, using ROC curve method in combination with flood density yielded strong and reliable results for various models. BWO‐XGBoost achieved a score of 0.889, BWO‐LGBoost achieved a score of 0.937, and BWO‐ADABoost achieved a score of 0.904. These scores indicate effectiveness of these models in accurately predicting flood susceptibility in the study area. A comparison was made with commonly used methods in flood susceptibility assessment to evaluate the efficiency of proposed models. It was found that having a first‐class and enlightening database is crucial for accurately classifying flood types in flood susceptibility mapping. This aspect greatly contributes to improving the overall performance of the model. Among the evaluated methods, the hybrid model BWO‐LGBoost demonstrated better performance compared with others, indicating its effectiveness in accurately predicting flood susceptibility.
{"title":"Flood susceptibility modeling by integrating tree‐based regression with metaheuristic algorithm, BWO","authors":"Deba Prakash Satapathy, Bibhu Prasad Mishra","doi":"10.1111/tgis.13171","DOIUrl":"https://doi.org/10.1111/tgis.13171","url":null,"abstract":"Floods are becoming more widely acknowledged as a common occurrence of nature's dangers on a global scale. Although forecasting models primarily focus on timely warnings, models aimed at evaluating dangerous zones can play a vital role in shaping policies for adaptation, mitigation, and reducing the risk of disasters. Using machine learning techniques including hybrid black widow optimization (BWO) with XGBoost, LGBoost, and AdaBoost. We generate a flood susceptibility map for considered region of lower mahanadi basin (LMB). This study examines the effectiveness of these machine learning models in assessing and mapping flood susceptibility, while also providing suggestions for future research in this area. Flood susceptibility model was developed using 13 variables: Altitude, Aspect, Curvature, Distance from river, Drainage Density, Stream Power Index (SPI), Sediment Transport Index (STI), Rainfall intensity, Land Use Land Cover (LULC), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), Normalized Difference Vegetation Index (NDVI), and slope. Additionally, flood inventory data were incorporated into the model. Dataset was divided into a 70% portion for training model and a 30% portion for validating model. To assess the performance of the model, several evaluation metrics were employed, including receiver operating characteristic (ROC) curve and other performance indices. Evaluation of flood susceptibility mapping, using ROC curve method in combination with flood density yielded strong and reliable results for various models. BWO‐XGBoost achieved a score of 0.889, BWO‐LGBoost achieved a score of 0.937, and BWO‐ADABoost achieved a score of 0.904. These scores indicate effectiveness of these models in accurately predicting flood susceptibility in the study area. A comparison was made with commonly used methods in flood susceptibility assessment to evaluate the efficiency of proposed models. It was found that having a first‐class and enlightening database is crucial for accurately classifying flood types in flood susceptibility mapping. This aspect greatly contributes to improving the overall performance of the model. Among the evaluated methods, the hybrid model BWO‐LGBoost demonstrated better performance compared with others, indicating its effectiveness in accurately predicting flood susceptibility.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"29 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940013","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}
Coastal landscapes exert a significant impact on the human sentimental perceptions and physical and mental well‐being of people. However, little is known about explicitly linking between the landscape characteristics and people's sentimental preferences expressed in social media data. The main objective of this study was to explore the nonlinear and interaction effects of key factors that influenced sentiments in the coastal areas of Hong Kong, considering both subjective landscape preferences and objective landscape patterns. We quantified users' sentiment polarity based on the crowdsourcing textual data of Flickr. To study users' subjective landscape preferences, we computed various visual landscape objects' proportion in images. Meanwhile, eight user clusters and nine image clusters were detected by the identified visual object labels. We quantified objective landscape patterns considering the land use pattens and the availability of public service facilities. Finally, we utilized an interpretable classification model to analyze the factors that may affect sentiments and their interplay interactions. We found that ecotourism‐related clusters exhibited the most positive sentiment. The proportion of floor and sky pixels in images exhibits the highest global relative importance when predicting sentiments. This study extends a new insight on the relationship between landscape characteristics and sentiments from both subjective and objective perspectives based on social media data and interpretable machine learning methods. This research may help decision‐makers in designing landscapes that aptly satisfy to the needs of the public and promote sustainable management of the coastal environment.
{"title":"Unraveling the relationship between coastal landscapes and sentiments: An integrated approach based on social media data and interpretable machine learning methods","authors":"Haojie Cao, Min Weng, Mengjun Kang, Shiliang Su","doi":"10.1111/tgis.13175","DOIUrl":"https://doi.org/10.1111/tgis.13175","url":null,"abstract":"Coastal landscapes exert a significant impact on the human sentimental perceptions and physical and mental well‐being of people. However, little is known about explicitly linking between the landscape characteristics and people's sentimental preferences expressed in social media data. The main objective of this study was to explore the nonlinear and interaction effects of key factors that influenced sentiments in the coastal areas of Hong Kong, considering both subjective landscape preferences and objective landscape patterns. We quantified users' sentiment polarity based on the crowdsourcing textual data of Flickr. To study users' subjective landscape preferences, we computed various visual landscape objects' proportion in images. Meanwhile, eight user clusters and nine image clusters were detected by the identified visual object labels. We quantified objective landscape patterns considering the land use pattens and the availability of public service facilities. Finally, we utilized an interpretable classification model to analyze the factors that may affect sentiments and their interplay interactions. We found that ecotourism‐related clusters exhibited the most positive sentiment. The proportion of floor and sky pixels in images exhibits the highest global relative importance when predicting sentiments. This study extends a new insight on the relationship between landscape characteristics and sentiments from both subjective and objective perspectives based on social media data and interpretable machine learning methods. This research may help decision‐makers in designing landscapes that aptly satisfy to the needs of the public and promote sustainable management of the coastal environment.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"54 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940015","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}
Nai Yang, Zhitao Deng, Fangtai Hu, Yi Chao, Lin Wan, Qingfeng Guan, Zhiwei Wei
Understanding the spatial distribution patterns of urban perception and analyzing the correlation between human emotional perception and street composition elements are important for accurately understanding how people interact with the urban environment, urban planning, and urban management. Previous studies on urban perception using street view data have not fully considered the actual level of attention to different visual elements when browsing street view images. In this article, we use eye tracking technology to collect eye movement data and subjective perception evaluation data when people browse street view images, and analyze the correlation between the time to first fixation, duration of first fixation, and fixation frequency of different visual elements and the six perceptual outcomes of wealthy, safe, lively, beautiful, boring, and depressing. Furthermore, this article integrates eye movement data with street view semantic data and introduces a novel method for predicting urban perception using a machine learning algorithm. The proposed method outperforms a comparative model that solely relies on semantic data, exhibiting higher accuracy in perception prediction. Additionally, the study presents a perceptual mapping of the prediction results, providing a visual representation of the predicted urban perception outcomes. As vision is the primary perceptual channel, this study achieves a more objective and scientifically reliable urban perception, which is of reference value for the study of physical and mental health due to the urban physical environment.
{"title":"Urban perception by using eye movement data on street view images","authors":"Nai Yang, Zhitao Deng, Fangtai Hu, Yi Chao, Lin Wan, Qingfeng Guan, Zhiwei Wei","doi":"10.1111/tgis.13172","DOIUrl":"https://doi.org/10.1111/tgis.13172","url":null,"abstract":"Understanding the spatial distribution patterns of urban perception and analyzing the correlation between human emotional perception and street composition elements are important for accurately understanding how people interact with the urban environment, urban planning, and urban management. Previous studies on urban perception using street view data have not fully considered the actual level of attention to different visual elements when browsing street view images. In this article, we use eye tracking technology to collect eye movement data and subjective perception evaluation data when people browse street view images, and analyze the correlation between the time to first fixation, duration of first fixation, and fixation frequency of different visual elements and the six perceptual outcomes of wealthy, safe, lively, beautiful, boring, and depressing. Furthermore, this article integrates eye movement data with street view semantic data and introduces a novel method for predicting urban perception using a machine learning algorithm. The proposed method outperforms a comparative model that solely relies on semantic data, exhibiting higher accuracy in perception prediction. Additionally, the study presents a perceptual mapping of the prediction results, providing a visual representation of the predicted urban perception outcomes. As vision is the primary perceptual channel, this study achieves a more objective and scientifically reliable urban perception, which is of reference value for the study of physical and mental health due to the urban physical environment.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"27 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881806","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}
Weirong Li, Kai Sun, Shu Wang, Yunqiang Zhu, Xiaoliang Dai, Lei Hu
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 F‐score of 0.8282, outperforming previous approaches, and can recognize new toponyms from literature text, potentially enriching existing gazetteers.
地名在将自然地点与人类感知联系起来方面发挥着重要作用，在人们的日常生活中被频繁使用，以自然语言指代地点。然而，许多地名由于其新建立、口语化和不同的关注点，可能没有被记录在典型的地名录中。这些未记录的地名经常在地理文献中被讨论；因此，有必要使用计算方法从地理文献中自动识别这些地名并更新现有地名录。目前，最先进的方法是基于深度学习的模型。然而，现有模型仅使用了部分位置信息，而非单词在句子中的完整位置信息，这限制了其识别地名的性能。为此，我们开发了基于 DeBERTa 的深度学习模型 DePNR，该模型具有完整的位置嵌入，可用于地理文献中的地名识别。我们在两个数据集上对 DePNR 进行了训练，并在地理文献的真实数据集上对其进行了测试，以评估其性能。结果表明，DePNR 的 F 分数达到 0.8282，优于之前的方法，并且可以从文献文本中识别新地名，从而丰富现有的地名录。
{"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":"11 1","pages":""},"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}
Jaqueline A. J. P. Soares, Michael M. Diniz, Luiz Bacelar, Glauston R. T. Lima, Allan K. S. Soares, Stephan Stephany, Leonardo B. L. Santos
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.
{"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":"1 1","pages":""},"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}
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%.
单元自动机（CA）模型是模拟未来城市扩张的有效工具。随着高分辨率地理空间数据在 CA 模拟中的广泛应用，CA 模型的计算强度也随之增加。此外，由于 CA 模型研究的不断发展，许多学者对模型进行了改进以提高其模拟精度，导致模型的计算复杂度不断增加。因此，基于 CA 的仿真任务需要大量的计算时间和内存空间。近年来，深度学习（DL）得到了快速发展。许多开源的深度学习框架都支持图形处理器（GPU）并行计算，并提供了高效的应用编程接口（API），可以方便地调用这些接口来处理感兴趣的任务。本研究基于 CA 模型的邻域效应计算过程与卷积神经网络（CNN）中的卷积过程之间的相似性，构建了一个高性能 CA 模型。利用 DL 库中的卷积函数计算 CA 模型的邻域效应，以减少基于 CA 仿真的时间和内存消耗。实验结果表明，与传统的 CA 模型相比，本研究提出的 GPU-卷积-CA 模型的执行时间减少了 98% 以上。
{"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":"14 1","pages":""},"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}
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.
{"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":"208 1","pages":""},"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}
Ning Li, Tianyi Liang, Shiqi Jiang, Changbo Wang, Chenhui Li
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.
{"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":"117 1","pages":""},"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}
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