Transactions in GIS最新文献

{"title":"Dynamic data‐driven railway bridge construction knowledge graph update method","authors":"Jianbo Lai, Jun Zhu, Yukun Guo, Jigang You, Yakun Xie, Jianlin Wu, Ya Hu","doi":"10.1111/tgis.13111","DOIUrl":"https://doi.org/10.1111/tgis.13111","url":null,"abstract":"Abstract Effectively integrating and correlating multisource data involved in the bridge construction process is crucial for the improvement of the bridge informatization level. In the current issues of dynamic numerous data and low information sharing between different engineering departments, the traditional information management methods are inefficient in providing comprehensive and accurate data support for construction safety. Focusing on the bridge construction stage, this article proposes a dynamic data‐driven construction method of railway bridge construction knowledge graph (KG) in combination with dynamic data (materials, personnel, equipment and sensors) in the construction process and KG technology. By taking a railway bridge as a case, the study develops a prototype system and analyzes the effectiveness of bridge construction KG in material traceability, personnel and equipment management and construction safety guidance, which can provide comprehensive and accurate data support for bridge construction management and construction optimization. The results show that: (1) bridge construction KG that takes into account the dynamic features of bridge projects can effectively integrate multiple elements; (2) the bridge construction KG is dynamically updated through real‐time comparison and advance prediction based on the dynamic data collected by multi‐sensing equipment at the construction site, and can provide effective data support for guiding bridge construction safety; and (3) the construction management prototype system based on railway bridge construction KG can provide accurate data support for material traceability, personnel and equipment management and assisted risk event decision‐making. The results of the comparative experiment between the KG group and the spreadsheet group showed that utilizing the KG saved approximately 50% of time and achieved a 20% higher accuracy rate in the material traceability task compared to the spreadsheet group. In general, this study proposes a dynamic data‐driven construction method of railway bridge construction KG, which can effectively realize the effective integration and management of multisource data in the bridge construction process, provide the necessary scientific basis for fine bridge management, and help to improve bridge informatization management level.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616566","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":"Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches","authors":"Adrian Wöltche","doi":"10.1111/tgis.13107","DOIUrl":"https://doi.org/10.1111/tgis.13107","url":null,"abstract":"Abstract Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883314","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 hierarchical spatio‐temporal object knowledge graph model for dynamic scene representation","authors":"Xinke Zhao, Yibing Cao, Jiahe Wang, Xinhua Fan, Minjie Chen","doi":"10.1111/tgis.13109","DOIUrl":"https://doi.org/10.1111/tgis.13109","url":null,"abstract":"Abstract Spatio‐temporal knowledge is essential in understanding the dynamic aspects of complex scenes. However, existing knowledge graphs have limitations, such as inadequate time description, inflexible expression of semantic relationships, and difficulties in accessing GIS platforms. The article proposes the spatio‐temporal object knowledge graph (STOKG), consisting of the object concept layer, spatio‐temporal object layer, and dynamic version layer. To demonstrate the practical usefulness of the STOKG model, the Henan epidemic knowledge graph is created using epidemiological data from early 2020, which shows the dynamic evolution of the spatio‐temporal objects of cases from the geography and semantic perspectives. Finally, the STOKG model is compared with the existing models in terms of accuracy, completeness and repetitiveness. The experimental results show that the STOKG model provides a more flexible and comprehensive approach to representing spatio‐temporal knowledge, which is useful for applications in fields such as geography, epidemiology, and environmental science.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739073","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":"Overcoming unreported violence using place‐based ambulance data: The case for mapping hotspots based on health data for crime prevention initiatives","authors":"David Simmonds, Barak Ariel, Vincent Harinam","doi":"10.1111/tgis.13105","DOIUrl":"https://doi.org/10.1111/tgis.13105","url":null,"abstract":"Abstract A key concern in crime analysis is the “hidden crime” problem. Crime events unaccounted for in police records limit the external validity of official statistics and, more importantly, hinder the ability of the police to manage crime and utilize their resources effectively. The problem is exacerbated in proactive initiatives aimed at curbing violence through hotspot policing, where inaccuracies and imprecision, or, worse, no data at all, diminish prevention efforts. Previous studies have sought to overcome the data problem by juxtaposing police records with ambulance data on assault callouts and have found profound disparities. Specifically, researchers matched “crime hotspots” with “ambulance hotspots” (rather than individual events) because patient confidentiality considerations have prevented health professionals from sharing subject‐level data with the police. However, health services can safely share spatial data on wider areas that do not disclose personal information. We build on this line of inquiry by analyzing data from the Thames Valley, United Kingdom, and observing spatial hotspots of different sizes. The results demonstrate that while the police and ambulance services attend to the same communities and similar types of facilities, the police are “blinded” to the location of nearly 8 out of 10 assaults. The incongruency is shown even with severe assaults, but to a lesser extent. We then simulate the reduction in injuries if the police had access to health data at different spatial levels and show that even under the most conservative set of assumptions, such an approach can prevent between 113 and 116 violent injuries each year that might otherwise require hospitalization.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193061","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 <scp>cyberGIS</scp> approach to exploring neighborhood‐level social vulnerability for disaster risk management","authors":"Su Yeon Han, Jeon‐Young Kang, Fangzheng Lyu, Furqan Baig, Jinwoo Park, Danielle Smilovsky, Shaowen Wang","doi":"10.1111/tgis.13106","DOIUrl":"https://doi.org/10.1111/tgis.13106","url":null,"abstract":"Abstract Timely identification of disaster‐prone neighborhoods and examination of disparity in disaster exposure are critical for policymakers to plan efficient disaster management strategies. Many studies have investigated racial, ethnic, and geographic disparities and populations most vulnerable to disasters. However, little attention has been paid to the development of easily accessible and reusable tools to enable: (1) the prompt identification of vulnerable neighborhoods; and (2) the examination of social disparity in disaster impact. In this research, we have developed a visual analytics tool that allows users to: (1) delineate neighborhoods based on their selection of variables; and (2) explore which neighborhoods are susceptible to the impacts of disasters based on specific socioeconomic and demographic characteristics. Through an exploration of COVID‐19 data in the case study, we revealed that the tool can provide new insights into the identification of vulnerable neighborhoods that need immediate attention for disaster control, management, and relief.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387187","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":"Towards a <scp>spatio‐temporal</scp> multicriteria evaluation method: A suitability analysis of residential units in a <scp>3D</scp> urban environment","authors":"Kendra Munn, Suzana Dragićević","doi":"10.1111/tgis.13101","DOIUrl":"https://doi.org/10.1111/tgis.13101","url":null,"abstract":"Abstract Spatial multi‐criteria evaluation (MCE) techniques aid urban planning management by analyzing decision problem alternatives for solutions to help inform decision‐making. However, there is a lack of such methods that incorporate the temporal dimension, an important factor when analyzing the dynamic urban landscape and decisions surrounding its changes. A novel spatio‐temporal MCE approach is proposed that operates in three‐dimensional (3D) space and time to identify changing suitability values of decision alternatives. This space–time method is implemented to evaluate the suitability of residential units over a 15‐year period in part of downtown City of Vancouver, Canada. The results indicate that the majority of units exhibit a decrease in suitability with time due to depreciation and reduction of assets like view and privacy from the construction of new buildings. The proposed method can be used by urban planners and developers to assist in long‐term assessments of proposed development scenarios and their impact on existing urban infrastructure.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135435063","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":"Counterfactual reasoning in space and time: Integrating graphical causal models in computational movement analysis","authors":"Saeed Rahimi, Antoni B. Moore, Peter A. Whigham, Peter Dillingham","doi":"10.1111/tgis.13100","DOIUrl":"https://doi.org/10.1111/tgis.13100","url":null,"abstract":"Abstract Movement analysis is distinguished by an emphasis on understanding via observation and association. However, an important component of movement from the human and computer modeling perspective is the processes that bring about movement behavior in the first place. This article contextualizes the graphical causal modeling framework (for association, intervention, and counterfactual causal analysis) in GIScience, and more specifically within movement analysis studies. This is done by modeling the movement behavior of football players, applied to spatiotemporal data generated by an agent‐based simulation. The movement dataset is thoroughly analyzed to infer the statistical associations among its variables, to estimate the effect of an intervention on some of those variables, and to answer a few counterfactual questions from the observations. We conclude that causal graphs (i.e., directed acyclic graphs), if implemented correctly, can assist analysts in infering causal relations from movement data. This research suggests the integration of causal graphs and agent‐based paradigms as one solution for computational movement analysis.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135826150","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 spatiotemporal analysis of metro‐stop based accessibility to bus services in Shanghai, China","authors":"Yuji Shi, Zhuofan Yan","doi":"10.1111/tgis.13103","DOIUrl":"https://doi.org/10.1111/tgis.13103","url":null,"abstract":"Abstract Ground‐level bus offers flexible services with relatively inexpensive fares and high‐level accessibility, thus is the dominant feeder mode for metro in the majority of metropolitan areas in China. Intermodal transfer between the metro and bus networks is therefore a crucial element in the successful operation of an integrated transit system. In this circumstance, a key challenge is lack of appropriate methodologies to evaluate the spatial–temporal disparities of intermodal transfers between metro and bus. To address this issue, this research aims to promote an existing two‐step floating catchment area (2SFCA) model by incorporating the temporal variability of service supply, demand, and travel time to provide a more realistic estimation of accessibility using smart card data and automatic vehicle location data. The proposed methodology was applied in a case study of the metropolitan area of Shanghai in the specific context of metro‐stop based accessibility to bus services. The results of the case study show that the daily fluctuations in output metro‐to‐bus transfer accessibility values are highly sensitive to temporal dynamics of transfer time, service supply, and demand. It is hoped the results output here could give planners and policymakers greater insight into spatiotemporal dynamics on transfer accessibility, and help to establish an effective and efficient integrated transit system.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072663","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":"Investigating factors in indoor transmission of respiratory disease through agent‐based modeling","authors":"Moongi Choi, A. Hohl","doi":"10.1111/tgis.13099","DOIUrl":"https://doi.org/10.1111/tgis.13099","url":null,"abstract":"The transmission of respiratory diseases such as COVID‐19 is exacerbated in densely populated urban areas and crowded indoor settings. Despite the majority of transmissions occurring in such settings, controlling viral spread through individual‐level contacts indoors remains challenging. Experimental studies have investigated the transmission patterns of respiratory behaviors such as coughing or sneezing in controlled spatial environments. However, the effects of dynamic movement and spatial structures have been ignored, making it difficult to apply findings to urban policy and planning. To address this gap, we developed agent‐based simulations to investigate individual virus inhalation patterns across multiple scenarios in a symmetrical and formulaic indoor space. We conducted sensitivity analysis using regression emulator models to identify significant factors for viral transmission. Our results indicate positive associations with viral transmission in descending order of: (1) stay time; (2) encounter frequency; and (3) initial infected population; while negative associations are: (4) mask wearing; (5) distance to infected people; (6) nearest infected people's mask wearing; and (7) distance to entrance. We also found that narrow passages between obstacles increase virus transmission from breathing. Furthermore, we conducted a case study to investigate the potential of reducing the amount of individually inhaled virus by controlling behaviors and spatial environments. Our findings suggest that mask wearing and reduced stay time can substantially reduce transmission risk, while a large number of contacts and high grouping time result in the growth of the infected population at a certain threshold. These results provide guidance for decision makers to formulate guidelines for curbing the spread of respiratory diseases in indoor spaces.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"235 1","pages":"1794 - 1827"},"PeriodicalIF":2.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89037112","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":"DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network","authors":"Chen Chu, Hengcai Zhang, Peixiao Wang, Feng Lu","doi":"10.1111/tgis.13095","DOIUrl":"https://doi.org/10.1111/tgis.13095","url":null,"abstract":"Accurate and interpretable prediction of crowd flow would benefit business management and public security. The existing studies are challenged to adapt to the indoor environment due to its complex and dynamic spatial interaction patterns. In this study, we propose a crowd flow predicting method for indoor shopping malls, which simultaneously features temporal variables and semantic factors to suit the shopping mall environment. A deep learning model named DeepIndoorCrowd is presented. The model aims at capturing temporal dependencies and the semantic pattern in crowd flow to generate an accurate multi‐horizon prediction. With a multi‐term temporal dependency capturing structure, the model is effective in learning both daily and weekly patterns of the indoor crowd flow in a shopping mall and is able to provide the temporal interpretation of the prediction result. Moreover, a semantic‐temporal fusion module is introduced to utilize the semantic information of stores in prediction, which has proved to be effective in enhancing the model's ability to learn temporal patterns. Experiments were conducted on a real‐world dataset to verify the proposed approach. The ablation study demonstrates that the DeepIndoorCrowd can effectively improve the efficiency and accuracy of the prediction up to 18.7%. In addition, some interesting indoor crowd flow patterns were discovered by analyzing the model's interpretation of the prediction result. The proposed prediction method provides an intuitive way of modeling indoor crowd flow, and the experiment's outcome can help indoor managers better understand stores' flow traffic.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"15 1","pages":"1699 - 1723"},"PeriodicalIF":2.4,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90694748","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}