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Initiated by the University Consortium of Geographic Information Science (UCGIS), the GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re-organization and content updating, resulting in a new digital version of the project. While the BoK topics provide useful materials for researchers and students to learn about GIS, the semantic relationships among the topics, such as semantic similarity, should also be identified so that a better and automated topic navigation can be achieved. Currently, the related topics are either defined manually by editors or authors, which may result in an incomplete assessment of topic relationships. To address this challenge, our research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text, including both deep neural networks and traditional machine learning approaches. Besides, a novel text summarization—KACERS (Keyword-Aware Cross-Encoder-Ranking Summarizer)—is proposed to generate a semantic summary of scientific publications. By identifying the semantic linkages among key topics, this work guides the future development and content organization of the GIS&T BoK project. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications and demonstrates the potential of the KACERS summarizer in semantic understanding of long text documents. 相似文献
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Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts 总被引：1，自引：0，他引：1 
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下载免费PDF全文 The ways in which geographic information are produced have expanded rapidly over recent decades. These advances have provided new opportunities for geographical information science and spatial analysis—allowing the tools and theories to be expanded to new domain areas and providing the impetus for theory and methodological development. In this light, old problems of inference and analysis are rediscovered and need to be reinterpreted, and new ones are made apparent. This article describes a new typology of geographical analysis problems that relates to uncertainties in the relationship between individual‐level data, represented as point features, and the geographic context(s) that they are associated with. We describe how uncertainty in context linkage (uncertain geographic context problem) is also related to, but distinct from, uncertainty in point‐event locations (uncertain point observation problem) and how these issues can impact spatial analysis. A case study analysis of a geosocial dataset demonstrates how alternative conclusions can result from failure to account for these sources of uncertainty. Sources of point observation uncertainties common in many forms of user‐generated and big spatial data are outlined and methods for dealing with them are reviewed and discussed. 相似文献
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Yang Zhou Mingjun Wang Chen Zhang Fu Ren Xiangyuan Ma Qingyun Du 《Transactions in GIS》2021,25(1):359-381
Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. 相似文献
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Object matching facilitates spatial data integration, updating, evaluation, and management. However, data to be matched often originate from different sources and present problems with regard to positional discrepancies and different levels of detail. To resolve these problems, this article designs an iterative matching framework that effectively combines the advantages of the contextual information and an artificial neural network. The proposed method can correctly aggregate one‐to‐many (1:N) and many‐to‐many (M:N) potential matching pairs using contextual information in the presence of positional discrepancies and a high spatial distribution density. This method iteratively detects new landmark pairs (matched pairs), based on the prior landmark pairs as references, until all landmark pairs are obtained. Our approach has been experimentally validated using two topographic datasets at 1:50 and 1:10k. It outperformed a method based on a back‐propagation neural network. The precision increased by 4.5% and the recall increased by 21.6%, respectively. 相似文献
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Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographic scope it refers to. The task of matching addresses happens every day and is present in various fields such as mail redirection, entity resolution, etc. Our work defines, and formalizes a framework to generate matching and mismatching pairs of addresses in the English language, and use it to evaluate various methods to automatically perform address matching. These methods vary widely from distance-based approaches to deep learning models. By studying the Precision, Recall, and Accuracy metrics of these approaches, we obtain an understanding of the best suited method for this setting of the address matching task. 相似文献
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Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN. 相似文献
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Spatial relations are frequently described and used in natural language texts, and relations play a core role in a range of applications—from supporting geographic information retrieval in natural language texts to locating people and objects in natural disaster response situations. In this article, we present a neuro-net spatial extraction model (NeuroSPE) designed to address various language irregularities (i.e., a variety of sentence structures) that occur in natural language texts. We also propose a two-stage workflow to generate a training dataset based on a collection of words and their associated frequencies. The first stage of the proposed workflow focuses on processing the words in the input data and their associated frequencies; then, the words are segmented into a set of groups and used to accelerate model training. The second stage automatically generates a variety of sentences that include two geographic entities and related spatial relation terms through deep learning iteration based on a unigram language model. We evaluate our method both qualitatively and quantitatively using a real dataset. The experimental results demonstrate that the proposed two-stage workflow effectively extracts spatial relations from natural language texts and outperforms other current state-of-the-art approaches. 相似文献
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Anyone involved in teaching the principles and applications of geographic information science and technology (GIS&T) understands the challenges of effective instruction within an environment subject to nearly constant change. Indeed, the dynamic nature of GIS&T introduces both new paradigms and increasingly powerful tools for exploring spatial relationships. However, while past efforts among educators and practitioners have identified knowledge and competencies important to GIS&T learning, less attention has been directed at methods used to teach GIS&T. For example, while some instructors employ traditional approaches such as lectures and structured laboratory exercises, others have shifted to active learning strategies such as “flipped classrooms” and collaborative, project‐oriented assignments. In this article, we assess the pedagogical approaches used to teach GIS&T courses through an Internet‐based survey of 318 college and university faculty. Our findings demonstrate that active learning pedagogies are becoming more firmly established, supplementing or replacing traditional teaching approaches. Contrary to our assumptions, age and teaching experience are not factors that influence the adoption of active learning strategies. Along with assessing instructional approaches, our survey identifies the challenges associated with teaching GIS&T, as identified by survey respondents. 相似文献
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Large, multivariate geographic datasets have been used to characterize geographic space with the help of spatial data mining tools. In our study, we explore the sufficiency of the Support Vector Machine (SVM), a popular machine‐learning technique for unsupervised classification and clustering, to help recognize hidden patterns in a college admissions dataset. Our college admissions dataset holds over 10,000 students applying to an undisclosed university during one undisclosed year. Students are qualified almost exclusively by their standardized test scores and school records, and a known admissions decision is rendered based on these criteria. Given that the university has a number of political, social and geographic econometric factors in its admissions decisions, we use SVM to find implicit spatial patterns that may favor students from certain geographic regions. We first explore the characteristics of the applicants in the college admissions case study. Next, we explain the SVM technique and our unique ‘threshold line’ methodology for both discrete (regional) and continuous (k‐neighbors) space. We then analyze the results of the regional and k‐neighbor tests in order to respond to the methodological and geographic research questions. 相似文献
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城市建成环境是人类赖以生存的人造环境,城市建成环境存量是指城市中建筑物和基础设施的材料质量。反演城市建成环境存量的空间分布,是数字城市建设的新方向,它能够帮助我们了解城市发展模式,更加有效管理城市资源和废弃物等,对发展城市循环经济、实现城市的可持续发展有着十分重要的意义。本文详细介绍了城市建成环境存量空间计算的3种方法（自上而下、自下而上和遥感计算方法）的理论基础和发展现状,总结了目前的几中方法都存在着过度依赖统计数据、无法兼顾研究区域大尺度和高空间分辨率等问题。在地理大数据时代,更多的数据源为存量的计算带来了新的研究方向。本文总结了新数据源的优势,并展望了结合地理大数据和机器学习方法的存量计算方法,为城市建成环境存量的空间计算提供了一种新的思路。 相似文献
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Inferring the home locations of Twitter users based on the spatiotemporal clustering of Twitter data 下载免费PDF全文
下载免费PDF全文 Residential locations play an important role in understanding the form and function of urban systems. However, it is impossible to release this detailed information publicly, due to the issue of privacy. The rapid development of location‐based services and the prevalence of global position system (GPS)‐equipped devices provide an unprecedented opportunity to infer residential locations from user‐generated geographic information. This article compares different approaches for predicting Twitter users' home locations at a precise point level based on temporal and spatial features extracted from geo‐tagged tweets. Among the three deterministic approaches, the one that estimates the home location for each user by finding the weighted most frequently visited (WMFV) cluster of that user always provides the best performance when compared with the other two methods. The results of a fourth approach, based on the support vector machine (SVM), are severely affected by the threshold value for a cluster to be identified as the home. 相似文献
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空间思维是地理科学认知学习与研究的基本思维模式。为满足新媒体融合背景下地理信息科学专业的素质人才培养需求，本文设计了包括地理空间模拟体验、空间信息地图绘读、隐喻信息语义认知和地理系统综合分析等针对空间思维能力训练的教学内容；明晰了地理空间思维在信息感知与整理、隐含信息挖掘、信息表达视觉化、行业服务支撑、空间治理决策等新媒体信息深度挖掘方向上的应用。本文研究有利于本科学生充分利用新媒体学习工具、系统化专业知识体系和提高自身地理空间思维素养，可以为地理信息科学本科专业教学提供有益的探索。 相似文献
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Youness Dehbi Andr Henn Gerhard Grger Viktor Stroh Lutz Plümer 《Transactions in GIS》2021,25(1):112-133
This article proposes a novel method for the 3D reconstruction of LoD2 buildings from LiDAR data. We propose an active sampling strategy which applies a cascade of filters focusing on promising samples at an early stage, thus avoiding the pitfalls of RANSAC‐based approaches. Filters are based on prior knowledge represented by (nonparametric) density distributions. In our approach samples are pairs of surflets—3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs provide parameters for model candidates such as azimuth, inclination and ridge height, as well as parameters estimating internal precision and consistency. This provides a ranking of roof model candidates and leads to a small number of promising hypotheses. Building footprints are derived in a preprocessing step using machine learning methods, in particular support vector machines. 相似文献
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Often, we are faced with questions regarding past events and the answers are hidden in the historical text archives. The growing developments in geographic information retrieval and temporal information retrieval techniques have given new ways to explore digital text archives for spatio‐temporal data. The question is how to retrieve the answers from the text documents. This work contributes to a better understanding of spatio‐temporal information extraction from text documents. Natural language processing techniques were used to develop an information extraction approach using the GATE language processing software. The developed framework uses gazetteer matching, spatio‐temporal relationship extraction and pattern‐based rules to recognize and annotate elements in historical text documents. The extracted spatio‐temporal data is used as input for GIS studies on the time–geography context of the German–Herero resistance war of 1904 in Namibia. Related issues when analyzing the historical data in current GIS are discussed. Particularly problematic are movement data in small scale with poor temporal density and trajectories that are short or connect very distant locations. 相似文献
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互联网数据中蕴含丰富的地理信息,其无处不在、形式与结构多样的特征决定了感知和融合面临许多技术难题。本文在分析互联网泛在地理信息分类和特征的基础上,系统研究其感知和融合技术的总体现状,总结了服务快速准确发现、深层网络数据高覆盖度采集、非结构化文本中位置信息提取和关联图像空间语义提取等感知技术发展现状,分析了异源几何数据匹配关联、地址标准化处理、同名实体语义对齐、地理实体关系构建等融合处理关键技术;在此基础上,总结和展望了互联网泛在地理信息感知融合技术在开放地理数据网络、城市治理与应急管理、网络监测与地理空间情报等领域的应用前景。 相似文献