网络GIS渐进式本科教学模式研究

地理信息科学与互联网技术的发展使得网络GIS(CyberGIS)相关技术逐渐渗透到各个应用领域,目前对于网络GIS专业本科毕业生的需求日益增加,给网络GIS本科教学带来了机遇和挑战.针对社会对网络GIS专业本科毕业生的需求和本科生自身的学习特点,文章提出了以实践性教学为主的网络GIS课程体系,设计了网络GIS本科渐进式教学模式,为综合培养本科生的网络GIS基础理论与实践动手能力的本科教学提供参考.     

Mapping spatiotemporal patterns of events using social media: a case study of influenza trends

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.     

Metadata and provenance for spatial analysis: the case of spatial weights

Within a CyberGIS environment, the development of effective mechanisms to encode metadata for spatial analytical methods and to track the provenance of operations is a key requirement. Spatial weights are a fundamental element in a wide range of spatial analysis methods that deal with testing for and estimating models with spatial autocorrelation. They form the link between the data structure in a GIS and the spatial analysis methods. Over time, the number of formats for spatial weights implemented in software has proliferated, without any standard or easy interoperability. In this paper, we propose a flexible format that provides a way to ensure interoperability within a cyberinfrastructure environment. We illustrate the format with an application of a spatial weights web service, which is part of an evolving spatial analytical workbench. We describe an approach to embed provenance in spatial weights structures and illustrate the performance of the web service by means of a number of small experiments.     

Optimizing an index with spatiotemporal patterns to support GEOSS Clearinghouse

A variety of Earth observation systems monitor the Earth and provide petabytes of geospatial data to decision-makers and scientists on a daily basis. However, few studies utilize spatiotemporal patterns to optimize the management of the Big Data. This article reports a new indexing mechanism with spatiotemporal patterns integrated to support Big Earth Observation (EO) metadata indexing for global user access. Specifically, the predefined multiple indices mechanism (PMIM) categorizes heterogeneous user queries based on spatiotemporal patterns, and multiple indices are predefined for various user categories. A new indexing structure, the Access Possibility R-tree (APR-tree), is proposed to build an R-tree-based index using spatiotemporal query patterns. The proposed indexing mechanism was compared with the classic R*-tree index in a number of scenarios. The experimental result shows that the proposed indexing mechanism generally outperforms a regular R*-tree and supports better operation of Global Earth Observation System of Systems (GEOSS) Clearinghouse.     

Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives

A spatial web portal (SWP) provides a web-based gateway to discover, access, manage, and integrate worldwide geospatial resources through the Internet and has the access characteristics of regional to global interest and spiking. Although various technologies have been adopted to improve SWP performance, enabling high-speed resource access for global users to better support Digital Earth remains challenging because of the computing and communication intensities in the SWP operation and the dynamic distribution of end users. This paper proposes a cloud-enabled framework for high-speed SWP access by leveraging elastic resource pooling, dynamic workload balancing, and global deployment. Experimental results demonstrate that the new SWP framework outperforms the traditional computing infrastructure and better supports users of a global system such as Digital Earth. Reported methodologies and framework can be adopted to support operational geospatial systems, such as monitoring national geographic state and spanning across regional and global geographic extent.     

Big Data and cloud computing: innovation opportunities and challenges

ABSTRACT

Big 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.     

A cyberinfrastructure for community resilience assessment and visualization

Disaster resilience is a major societal challenge. Cartography and GIS can contribute substantially to this research area. This paper describes a cyberinfrastructure for disaster resilience assessment and visualization for all counties in the United States. Aided by the Application Programming Interface-enabled web mapping and component-oriented web tools, the cyberinfrastructure is designed to better serve the US communities with comprehensive resilience information. The resilience assessment tool is based on the resilience inference measurement model. This web application delivers the resilience assessment tool to the users through applets. It provides an interactive tool for the users to visualize the historical natural hazards exposure and damages in the areas of their interest, compute the resilience indices, and produce on-the-fly maps and statistics. The app could serve as a useful tool for decision makers. This app won the top 10 runners-up in the Environmental Systems Research Institute (ESRI) Climate Resilience App Challenge 2014 and the top 5 in the scientific section of the ESRI Global Disaster App Challenge 2014.     

Parallelization of a regionalization heuristic in distributed computing platforms – a case study of parallel-p-compact-regions problem

In this paper, we report efforts to develop a parallel implementation of the p-compact regionalization problem suitable for multi-core desktop and high-performance computing environments. Regionalization for data aggregation is a key component of many spatial analytical workflows that are known to be NP-Hard. We utilize a low communication cost parallel implementation technique that provides a benchmark for more complex implementations of this algorithm. Both the initialization phase, utilizing a Memory-based Randomized Greedy and Edge Reassignment (MERGE) algorithm, and the local search phase, utilizing Simulated Annealing, are distributed over available compute cores. Our results suggest that the proposed parallelization strategy is capable of solving the compactness-driven regionalization problem both efficiently and effectively. We expect this work to advance CyberGIS research by extending its application areas into the regionalization world and to make a contribution to the spatial analysis community by proposing this parallelization strategy to solve large regionalization problems efficiently.     

面向未来课堂的网络GIS本科课程改革研究

随着网络GIS课程知识结构的不断发展,社会人才需求呈现出多样性,传统的网络GIS本科课程难以应对培养目标的动态性和学习者之间的差异性,使得网络GIS专业本科教学面临巨大的挑战。分析网络GIS课程改革的新需求,综合考虑网络GIS课程和学习者的特点,借鉴"未来课堂"教学理念,提出顾及学习者学习新特点的网络GIS"未来课堂"教学设计。促使网络GIS"未来课堂"能够顾及全体学习者的性格特征和兴趣,充分调动学习者的积极性,最终实现教与学有机统一的网络GIS"未来课堂"。