Cartography and Geographic Information Science最新文献

{"title":"Dimensions of Uncertainty: A spatiotemporal review of five COVID-19 datasets.","authors":"Dylan Halpern, Qinyun Lin, Ryan Wang, Stephanie Yang, Steve Goldstein, Marynia Kolak","doi":"10.1080/15230406.2021.1975311","DOIUrl":"10.1080/15230406.2021.1975311","url":null,"abstract":"<p><p>COVID-19 surveillance across the U.S. is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic.</p>","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"1 1","pages":"200-221"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44113959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithmic uncertainties in geolocating social media data for disaster management","authors":"D. Mandal, Lei Zou, J. Abedin, Bing Zhou, Mingzheng Yang, Binbin Lin, Heng Cai","doi":"10.1080/15230406.2023.2286385","DOIUrl":"https://doi.org/10.1080/15230406.2023.2286385","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"4 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952170","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 study on the aptitude of color hue, value, and transparency for geographic relevance encoding in mobile maps","authors":"Marco Olivieri, T. Reichenbacher","doi":"10.1080/15230406.2023.2283063","DOIUrl":"https://doi.org/10.1080/15230406.2023.2283063","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"133 34","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599040","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":"Trust in maps: what we know and what we need to know","authors":"Timothy J. Prestby","doi":"10.1080/15230406.2023.2281306","DOIUrl":"https://doi.org/10.1080/15230406.2023.2281306","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"341 ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139203693","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":"Using machine learning and data enrichment in the selection of roads for small-scale maps","authors":"I. Karsznia, Albert Adolf, S. Leyk, Robert Weibel","doi":"10.1080/15230406.2023.2283075","DOIUrl":"https://doi.org/10.1080/15230406.2023.2283075","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"31 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206387","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":"GeospatRE: extraction and geocoding of spatial relation entities in textual documents","authors":"Mehtab Alam Syed, E. Arsevska, Mathieu Roche, M. Teisseire","doi":"10.1080/15230406.2023.2264753","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264753","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"11 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208824","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":"Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes","authors":"Martin Knura","doi":"10.1080/15230406.2023.2273397","DOIUrl":"https://doi.org/10.1080/15230406.2023.2273397","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"16 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253524","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 comparison of performance using extrinsic and intrinsic bivariate cartographic visualizations with respect to cognitive style in experienced map users","authors":"Zdeněk Stachoň, Jiří Čeněk, David Lacko, Lenka Havelková, Martin Hanus, Wei-Lun Lu, Alžběta Šašinková, Pavel Ugwitz, Jie Shen, Čeněk Šašinka","doi":"10.1080/15230406.2023.2264752","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264752","url":null,"abstract":"ABSTRACTWhen spatial information is depicted on univariate or multivariate maps, different visualization designs should be considered to fit the designs to suit the target audience and define the map’s general purpose and therefore also the map user’s expected cognitive processes. Although multivariate maps have attracted research for decades, only several studies have compared the effectiveness of maps that use extrinsic and intrinsic encoding styles, and even fewer have tried to incorporate other map-related factors that could significantly affect the user’s performance and clarify the relationship between the selected encoding style’s efficiency and the user’s cognitive processes. In this paper, we report on an empirical replication study focused on the performance differences of experienced map users solving a task using a map and the possible effect of their cognitive styles on the efficiency of bivariate map encoding styles and the map task type. For the experiment, we recruited 77 spatial planning and geography university students in China considered as experienced map users. The study indicated that extrinsic visualizations outperformed intrinsic visualizations in the main observed variables of correctness and response time but not always significantly. A detailed analysis of the tasks, which involved the use of either one variable or two variables concurrently, confirmed our hypothesis.KEYWORDS: Bivariate mapextrinsicintrinsiccognitive styleresponse timeaccuracy AcknowledgmentsThe study was supported by the Czech Science Foundation (GC19-09265J: The Influence of Socio-Cultural Factors and Writing Systems on the Perception and Cognition of Complex Visual Stimuli. We would like to thank the HUME Lab–Experimental Humanities Laboratory, Masaryk University, for providing us with the necessary machine time and equipment.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe dataset, data-analytic scripts (in R) and Supplementary material is accessible in the Open Science Foundation (OSF) repository under the following link: https://osf.io/kyu56/Additional informationFundingThe work was supported by the Grantová Agentura České Republiky [GC19-09265J].","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"28 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954479","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":"Keeping walls straight: data model and training set size matter for deep learning in building generalization","authors":"Cheng Fu, Zhiyong Zhou, Yu Feng, Robert Weibel","doi":"10.1080/15230406.2023.2264757","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264757","url":null,"abstract":"ABSTRACTDeep learning-backed models have shown their potential of conducting map generalization tasks. However, pioneering studies for raster-based building generalization encountered a common “wabbly-wall effect” that makes the predicted building shapes unrealistic. This effect was identified as a critical challenge in the existing studies. This work proposes a layered data representation model that separately stores a building for generalization and its context buildings in different channels. Incorporating adjustments to training sample generation and prediction tasks, we show how even without using more complex deep learning architectures, the widely used Residual U-Net can already produce straight walls for the generalized buildings and maintains rectangularity and parallelism of the buildings very well for building simplification and aggregation in the scale transition from 1:5,000 to 1:10,000 and 1:5,000 to 1:15,000, respectively. Experiments with visual evaluation and quantitative indicators such as Intersection over Union (IoU), fractality, and roughness index show that using a larger input tensor size is an easy but effective solution to improve prediction. Balancing samples with data augmentation and introducing an attention mechanism to increase network learning capacity can help in certain experiment settings but have obvious tradeoffs. In addition, we find that the defects observed in previous studies may be due to a lack of enough training samples. We thus conclude that the wabbly-wall challenge can be solved, paving the way for further studies of applying raster-based deep learning models on map generalization.POLICY HIGHLIGHTS Demonstrates the effectiveness of the proposed data structure with multiple evaluation indicatorsIdentifies a “wabbly-wall effect” a challenge in deep-learning backed image based map generalizationProposes a layered data structure that separates a target building and its surrounding buildings to ease the learning task in training deep learning models for raster-based map generalization.KEYWORDS: Map generalizationdeep learningrasterbuilding simplificationU-Net AcknowledgmentsThe authors also appreciate the comments of four anonymous reviewers which helped improve the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe raw maps that support the findings are available by request to Dr Yu Feng (y.feng@tum.de). The codes for U-Net and its variants are from third-party authors who are not affiliated with this manuscript. The codes for data preprocessing and the models adapted from U-Net models are available here: https://doi.org/10.6084/m9.figshare.21901086.v1.Supplemental dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2264757Notes1. https://github.com/LeeJunHyun/Image_SegmentationAdditional informationFundingThis research was supported by the Swiss National Science Foundation through proj","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902814","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":"MultiLineStringNet: a deep neural network for linear feature set recognition","authors":"Pengbo Li, Haowen Yan, Xiaomin Lu","doi":"10.1080/15230406.2023.2264756","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264756","url":null,"abstract":"ABSTRACTPattern recognition of linear feature sets, such as river networks, road networks, and contour clusters, is essential in cartography and geographic information science. Previous studies have investigated many methods to identify the patterns of linear feature sets; the key to each of these studies is to generate a reasonable and computable representation for each set. However, most existing methods are only designed for a specific task or data type and cannot provide a general solution for formalizing linear feature sets owing to their complex geometric characteristics, spatial relations and distributions. In addition, some methods require human involvement to specify characteristics, choose parameters, and determine the weights of different measures. To reduce human intervention and improve adaptability to various feature types, this paper proposes a novel deep learning architecture for learning the representations of linear feature sets. The presented model accepts vector data directly without extra data conversion and feature extraction. After generating local, neighborhood, and global representations of inputs, the representations are aggregated accordingly to perform pattern recognition tasks, including classification and segmentation. In the experiments, building groups classification and road interchanges segmentation achieved accuracies of 98% and 89%, respectively, indicating the model’s effectiveness and adaptability.KEYWORDS: Linear feature setpattern recognitiondeep learningbuilding group classificationroad interchange detection AcknowledgmentsThe authors sincerely thank the editors and the anonymous reviewers for their valuable feedback and insightful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data and code that support the findings of this study are available with the identifier at the public link (https://doi.org/10.6084/m9.figshare.21789881).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41930101, 42161066], Gansu Provincial Department of Education: The “Innovation Star” Project of Excellent Postgraduates [2023CXZX-506] and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, No. [KF-2022-07-015].","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"31 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953957","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}