共查询到20条相似文献,搜索用时 31 毫秒
1.
Alan M. MacEachren 《制图学和地理信息科学》2013,40(2):101-118
ABSTRACTThis paper highlights a selection of core ideas articulated by Bertin and leveraged by many researchers over time, with particular attention to how the ideas relate to developments in cartography, big data, and visual analytics. A primary contribution is a bibliometric analysis of the impact of Bertin’s Semiology of Graphics at its 50th anniversary. A briefer bibliometric assessment of Graphics and Graphic Information Processing impacts is also provided. The bibliometric analysis includes exploration of citations to Semiology of Graphics over the entire time span (in both English and French editions) as well as more focused analysis by topic and outlet since the advent of visual analytics as a research domain. Then, very recent research related to cartography, visual analytics, and big data is examined in detail to determine if and how Bertin’s ideas continue to be leveraged and extended for current data representation and analysis challenges. After outlining some limitations of the bibliometric analysis, discussion reflects on the current relevance of Bertin’s ideas, potential applications in visual analytics, and the need for a complement to Sémiologie Graphique focused on interactive visual interfaces to an increasingly diverse array of display forms. The paper concludes with thoughts on next steps. 相似文献
2.
来自社交网络的时空大数据具有海量和高动态的特性,有效选择时空数据进行聚焦挖掘分析至关重要。以微博位置签到数据为例,首先,对时空大数据空间聚类挖掘的有效选择问题进行了研究,针对社交网络时空数据不确定性问题,提出了时空大数据针对聚类挖掘的有效选择方法。聚类挖掘有效选择方法提出从空间、时间或属性等维度对时空大数据进行分割。然后,对分割得到的数据集进行空间探索分析(exploratory spatial data analysis,ESDA),得到具有聚类挖掘潜力的数据集。最后,以武汉市微博位置签到数据进行商圈热点探测为例,对提出的社交网络时空大数据聚类挖掘有效选择方法进行验证。结果表明,有效选择方法可以得到挖掘效率和精准性更高的时空数据集。 相似文献
3.
《International Journal of Digital Earth》2013,6(1):13-53
ABSTRACTBig Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications. 相似文献
4.
Scott Pezanowski Alan M MacEachren Alexander Savelyev Anthony C Robinson 《制图学和地理信息科学》2018,45(5):420-437
SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources. 相似文献
5.
ABSTRACT Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling. 相似文献
6.
《International Journal of Digital Earth》2013,6(7):749-763
ABSTRACTSince Al Gore created the vision for Digital Earth in 1998, a wide range of research in this field has been published in journals. However, little attention has been paid to bibliometric analysis of the literature on Digital Earth. This study uses a bibliometric analysis methodology to study the publications related to Digital Earth in the Science Citation Index database and Social Science Citation Index database (via the Web of Science online services) during the period from 1998 to 2015. In this paper, we developed a novel keyword set for ‘Digital Earth’. Using this keyword set, 11,061 scientific articles from 23 subject categories were retrieved. Based on the searched articles, we analyzed the spatiotemporal characteristics of publication outputs, the subject categories and the major journals. Then, authors’ performance, affiliations, cooperation, and funding institutes were evaluated. Finally, keywords were examined. Through keyword clustering, research hotspots in the field of Digital Earth were detected. We assume that the results coincide well with the position of Digital Earth research in the context of big data. 相似文献
7.
8.
Geospatial Ontology Development and Semantic Analytics 总被引:3,自引:0,他引:3
I Budak Arpinar Amit Sheth Cartic Ramakrishnan E Lynn Usery Molly Azami Mei-Po Kwan 《Transactions in GIS》2006,10(4):551-575
Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP. 相似文献
9.
《International Journal of Digital Earth》2013,6(7):737-748
ABSTRACTSupervised image classification has been widely utilized in a variety of remote sensing applications. When large volume of satellite imagery data and aerial photos are increasingly available, high-performance image processing solutions are required to handle large scale of data. This paper introduces how maximum likelihood classification approach is parallelized for implementation on a computer cluster and a graphics processing unit to achieve high performance when processing big imagery data. The solution is scalable and satisfies the need of change detection, object identification, and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications. 相似文献
10.
ABSTRACTPhoto-sharing services provide a rich resource of crowdsourced spatial data consisting of georeferenced imagery and metadata. Shared photos can provide valuable information for a variety of applications and geospatial analysis tasks, such as identifying tourist hot spots or traveled routes. Understanding the spatiotemporal patterns of photo contributions will allow analysts to assess the suitability of these data for related analysis tasks. Using California as a study area, this paper analyzes various aspects of photo contribution patterns of Panoramio and Flickr. It identifies areas where annual photo contributions are still growing and areas that undergo a decline in annual contributions. Multiple regression is used to identify which environmental correlates are associated with an increase in photo-sharing activities. Furthermore, panel data of annual contributions between 2006 and 2013 for California subcounties will be used in a regression model to demonstrate that there is a positive feedback effect between Panoramio and Flickr photo contributions, but no neighborhood effect. The results of this paper provide insight into the data quality of crowdsourced image collections. These collections are commonly used for geospatial applications, including tourist information services and the computation of scenic routes. 相似文献
11.
The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators. 相似文献
12.
《International Journal of Digital Earth》2013,6(7):781-801
ABSTRACTAlthough Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness. 相似文献
13.
14.
Mahmoud Sakr Esteban Zimányi Alejandro Vaisman Mohamed Bakli 《Transactions in GIS》2023,27(2):323-346
Performance indicators of road networks are a long-lasting topic of research. Existing schemes assess network properties such as the average speed on road segments and the queuing time at intersections. The increasing availability of user trajectories, collected mainly using mobile phones with a variety of applications, creates opportunities for developing user-centered performance indicators. Performing such an analysis on big trajectory data sets remains a challenge for the existing data management systems, because they lack support for spatiotemporal trajectory data. This article presents an end-to-end solution, based on MobilityDB, a novel moving object database system that extends PostgreSQL with spatiotemporal data types and functions. A new class of indicators is proposed, focused on the users' experience. The indicators address the network design, the traffic flow, and the driving comfort of the motorists. Furthermore, these indicators are expressed as analytical MobilityDB queries over a big set of real vehicle trajectories. 相似文献
15.
Yulun Zhou 《制图学和地理信息科学》2016,43(5):379-392
ABSTRACTIn the past decade, an explosion of data has taken place in Chinese cities due to widespread use of mobile Internet devices, Web 2.0 applications, and the development of the “Wired City.” With advances in data storage and high-performance computing, big/open urban data have opened up important avenues for urban studies, planning practice, and commercial consultancy. Urban researchers and planners are eager to make use of these abundant, sophisticated, and dynamic data to deepen their understanding on urban form and functions. However, in practice, access to such urban data is limited in China due to institutional constraints on data distribution and data holders’ hesitation to share data. And this hampers urban analytics. To draw reliable conclusions about the workings of complex urban systems, efficient and effective interoperation of multisource urban datasets is needed. Also, dealing with the heterogeneity between datasets is an equally critical challenge, especially for urban planners and government officers. They would derive value from data analytics, but have little data processing experience. To address these issues, we initiated SinoGrids (Plan Xu Xiake), a crowdsourcing platform that standardizes (or “downscales”) microscale urban data in China to facilitate its sharing and interoperation. To assess the performance evaluation of SinoGrids, we propose field-testing with actual urban data and their potential users. Digital desert, a son project of SinoGrids is also included. 相似文献
16.
17.
Thomas Esch Hubert Asamer Felix Bachofer Jakub Balhar Martin Boettcher Enguerran Boissier 《International Journal of Digital Earth》2020,13(1):136-157
ABSTRACTThe digital transformation taking place in all areas of life has led to a massive increase in digital data – in particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP’s pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development. 相似文献
18.
Caitlin Haedrich Vaclav Petras Anna Petrasova Stefan Blumentrath Helena Mitasova 《Transactions in GIS》2023,27(3):686-702
Open education materials are critical for the advancement of open science and the development of open-source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in-line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter , that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python-based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi-temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open-source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate-level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth. 相似文献
19.
ABSTRACTUrban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities. 相似文献
20.
大数据时代,交通数据如何更快更准确地实时反映交通状况成为研究热点。使用不同交通数据对不同的研究主题进行可视分析,进而揭示蕴含的交通信息知识,为交通部门提供决策支持。首先,分析了大数据的基本特征,总结了大数据可视分析的研究现状;其次,对交通大数据的基本构成、获取方式及数据类型特点进行详细描述;最后,按照不同的研究主题选择不同的数据源与不同的可视化表达方式,揭示不同研究主题蕴含的交通规律,为交通大数据的自动模型分析提供可靠的可视化知识。 相似文献