31 results on '"Wang, Shaowen"'
Search Results
2. An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data
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Lyu, Fangzheng, Wang, Shaohua, Han, Su Yeon, Catlett, Charlie, and Wang, Shaowen
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- 2022
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3. Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19
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Lyu, Fangzheng, Kang, Jeon-Young, Wang, Shaohua, Han, Su Yeon, Li, Zhiyu, Wang, Shaowen, Shaw, Shih-Lung, Series Editor, and Sui, Daniel, Series Editor
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- 2021
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4. Mapping dynamic human sentiments of heat exposure with location-based social media data.
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Lyu, Fangzheng, Zhou, Lixuanwu, Park, Jinwoo, Baig, Furqan, and Wang, Shaowen
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USER-generated content ,NATURAL language processing ,CITY dwellers ,HEAT waves (Meteorology) ,SPATIAL resolution - Abstract
Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States [ABSTRACT FROM AUTHOR]
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- 2024
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5. CyberGIS for Transforming Geospatial Discovery and Innovation
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Wang, Shaowen, Goodchild, Michael F., Sui, Daniel Z., Managing Editor, Wang, Shaowen, editor, and Goodchild, Michael F., editor
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- 2019
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6. EasyScienceGateway: A new framework for providing reproducible user environments on science gateways.
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Michels, Alexander, Padmanabhan, Anand, Li, Zhiyu, and Wang, Shaowen
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CYBERINFRASTRUCTURE ,COMMUNITY life - Abstract
Summary: Science gateways have become a core part of the cyberinfrastructure ecosystem by increasing access to computational resources and providing community platforms for sharing and publishing education and research materials. While science gateways represent a promising solution for computational reproducibility, common methods for providing users with their user environments on gateways present challenges which are difficult to overcome. This article presents EasyScienceGateway: a new framework for providing user environments on science gateways to resolve these challenges, provides the technical details on implementing the framework on a science gateway based on Jupyter Notebook, and discusses our experience applying the framework to the CyberGIS‐Jupyter and CyberGIS‐Jupyter for Water gateways. [ABSTRACT FROM AUTHOR]
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- 2024
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7. CyberGIS-Enabled Urban Sensing from Volunteered Citizen Participation Using Mobile Devices
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Yin, Junjun, Gao, Yizhao, Wang, Shaowen, Thakuriah, Piyushimita (Vonu), editor, Tilahun, Nebiyou, editor, and Zellner, Moira, editor
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- 2017
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8. Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA
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Kang, Jeon-Young, Michels, Alexander, Lyu, Fangzheng, Wang, Shaohua, Agbodo, Nelson, Freeman, Vincent L., and Wang, Shaowen
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- 2020
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9. Towards Reproducible Research on CyberGISX with Lmod and Easybuild
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Michels, Alexander, Padmanabhan, Anand, Li, Zhiyu, and Wang, Shaowen
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Geospatial Software ,cyberGIS ,Easybuild ,Jupyter - Abstract
JupyterHub [1] has become a popular choice in many scientific communities, offering an easy-to-use interface for users with little to no frontend development work while promoting reproducible and replicable (R&R) science [2]. In the broad geospatial science community, CyberGISX [3] provides such a gateway environment with many cyberGIS (i.e., geospatial information science and systems based on advanced cyberinfrastructure) and geospatial software packages prebuilt and ready to use. Like other JupyterHub-based solutions, CyberGISX also provides container-based access for its users and must balance a trade-off between providing a static compute environment which enhances R&R and continuously updating the software environment to keep up with advances in scientific software. Solutions such as Binder [4] have attempted to address this trade-off by having required dependencies encoded in the package and building the software environment at the time of use. However, such a solution comes with two major disadvantages: (a) software is built at the time it is needed, increasing startup time and introducing the possibility that some of the dependencies of the environment are no longer available or have changed; and (b) the onus of specifying and managing software installations is passed to notebook developers, many of whom are domain scientists and not comfortable with such responsibilities. To address these challenges and enhance R&R with minimal effort from end-users, we have designed and implemented a solution on CyberGISX that allows software to be kept on an external file server mounted into each user's environment. Scientific software is installed with Easybuild [5] and managed by Lmod [6] giving a variety of benefits: (1) the compute environment is more standardized and easily reproducible outside of the gateway; (2) multiple versions of software can be made available to users without increasing container size; and (3) the exact copies of software are always available on the gateway instead of being rebuilt for every release, further enhancing R&R. We also employ an Easybuild-installed Anaconda [7] to create and manage conda environments on the file server. The combination of the software stack from Easybuild and Python environment from conda provides end-users with kernels for their Jupyter notebooks which are persistent and unchanged as the gateway's container updates. This design enhances R&R and adds functionality for advanced users without introducing technical barriers to non-technical end-users. As such, domain scientists using this solution need not build their own software and specify dependencies, which helps prevent the notebooks they have developed from getting broken by the next software release. This talk explores the new architecture and applications of this solution to CyberGISX [3] and CyberGIS-Jupyter for Water (CJW) [8].
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- 2021
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10. Enabling computationally intensive geospatial research on CyberGIS-Jupyter with CyberGIS-Compute
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Padmanabhan, Anand, Xiao, Zimo, Vandewalle, Rebecca, Michels, Alexander, and Wang, Shaowen
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cyberGIS ,GIScience ,Cyberinfrastructure ,Jupyter - Abstract
Geospatial research and education have become increasingly dependent on cyberGIS, defined as geographic information science and systems based on advanced cyberinfrastructure (CI), [1] to tackle computation and data challenges. However, the use of advanced cyberGIS capabilities has typically been constrained to a small set of research groups who have the technical expertise of using CI resources. Over the past few years CyberGIS-Jupyter [2,3] has been developed to provide access to cyberGIS capabilities through an easy-to-use Jupyter Notebook interface which has made cyberGIS more accessible. For many cyberGIS and geospatial applications accessing CI resources needed for solving complex problems at scale. However, leveraging CI resources for geospatial application is challenging both due to the steep learning curve and lack of appropriate tools. CyberGIS-Compute fills this gap by providing an easy-to-use middleware tool for using and contributing geospatial application codes that leverage CI resources. This substantially lowers the learning curve for both geospatial users and developers to access cyberGIS capabilities at scale. CyberGIS-Compute is backed by Virtual ROGER (Resourcing Open Geospatial Education and Research); a geospatial supercomputer with access to a number of readily available popular geospatial libraries. With CyberGIS-Compute we have designed an easy-to-use middleware and associated Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. This presentation will first describe the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. Lastly, we will also descrive mechanism to contribute applications to the CyberGIS-Compute framework.
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- 2021
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11. Understanding the multifaceted geospatial software ecosystem: a survey approach.
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Vandewalle, Rebecca C., Barley, William C., Padmanabhan, Anand, Katz, Daniel S., and Wang, Shaowen
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GEOSPATIAL data ,SOFTWARE development tools ,COMPUTER software ,INTERNET forums ,ECOSYSTEMS - Abstract
Understanding the characteristics of the rapidly evolving geospatial software ecosystem in the United States is critical to enable convergence research and education that are dependent on geospatial data and software. This paper describes a survey approach to better understand geospatial use cases, software and tools, and limitations encountered while using and developing geospatial software. The survey was broadcast through a variety of geospatial-related academic mailing lists and listservs. We report both quantitative responses and qualitative insights. As 42% of respondents indicated that they viewed their work as limited by inadequacies in geospatial software, ample room for improvement exists. In general, respondents expressed concerns about steep learning curves and insufficient time for mastering geospatial software, and often limited access to high-performance computing resources. If adequate efforts were taken to resolve software limitations, respondents believed they would be able to better handle big data, cover broader study areas, integrate more types of data, and pursue new research. Insights gained from this survey play an important role in supporting the conceptualization of a national geospatial software institute in the United States with the aim to drastically advance the geospatial software ecosystem to enable broad and significant research and education advances. [ABSTRACT FROM AUTHOR]
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- 2021
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12. A CyberGIS Approach to Spatiotemporally Explicit Uncertainty and Global Sensitivity Analysis for Agent-Based Modeling of Vector-Borne Disease Transmission.
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Kang, Jeon-Young, Aldstadt, Jared, Vandewalle, Rebecca, Yin, Dandong, and Wang, Shaowen
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STATISTICS ,INHOMOGENEOUS materials ,SPATIOTEMPORAL processes ,INFECTIOUS disease transmission ,PARAMETERS (Statistics) - Abstract
Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2020
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13. A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping.
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Liu, Yan Y., Maidment, David R., Tarboton, David G., Zheng, Xing, and Wang, Shaowen
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GEOSPATIAL data ,FLOODS ,STREAMFLOW ,DIGITAL elevation models ,MANAGEMENT - Abstract
Abstract: We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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14. A multidimensional spatial scan statistics approach to movement pattern comparison.
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Gao, Yizhao, Li, Ting, Wang, Shaowen, Jeong, Myeong-Hun, and Soltani, Kiumars
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ORIGIN & destination traffic surveys ,TRAFFIC patterns ,SPATIAL analysis (Statistics) ,TAXICABS ,BIG data - Abstract
This paper describes a multidimensional spatial scan statistics approach to comparing spatial movement patterns based on origin-destination (OD) representation. This approach aims to evaluate differences and similarities between the spatial distributions of a pair of OD movement datasets, and detect areas where the two spatial distributions differ the most. Specifically, two OD datasets being compared are modeled as a bivariate marked spatial point process in a multidimensional space, consisting of points representing individual OD movement records. Such multidimensional space is formed by the Cartesian product of the origins’ and the destinations’ geographic spaces. With this spatial data model, one can evaluate how two movement distributions differ from each other by testing against a random labeling null hypothesis. A multidimensional Bernoulli spatial scan statistics method is developed to detect OD region pairs with abnormally high concentrations of one movement dataset over the other. The existence and the spatial extents of these OD region pairs indicate whether and where the two movement distributions differ. Two case studies were conducted to evaluate the approach by comparing morning and afternoon taxi trips (individual movements), and county-to-county migration flows between age groups (aggregated movement flows), and demonstrated that areas with the most significant spatial distribution differences could be detected from large movement datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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15. Mapping spatiotemporal patterns of events using social media: a case study of influenza trends.
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Gao, Yizhao, Wang, Shaowen, Padmanabhan, Anand, Yin, Junjun, and Cao, Guofeng
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SPATIOTEMPORAL processes , *SOCIAL media , *HETEROGENEITY , *INFLUENZA , *SPACETIME - Abstract
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 usingTwitterdata. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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16. CyberGIS-BioScope: a cyberinfrastructure-based spatial decision-making environment for biomass-to-biofuel supply chain optimization.
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Hu, Hao, Lin, Tao, Liu, Yan Y., Wang, Shaowen, and Rodríguez, Luis F.
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GEOGRAPHIC information systems ,MOTION picture projection ,CYBERINFRASTRUCTURE ,DECISION making ,BIOMASS energy ,SUPPLY chains ,MATHEMATICAL optimization - Abstract
Biomass, for example, energy crops, forests, and agricultural residues, has emerged as a renewable energy option to alleviate the consumption of limited fossil fuel resources and the consequent environmental issues. Designing an effective and efficient biomass-to-biofuel supply chain involves sophisticated decision-making processes, often requiring collaborative work on data integration, model specification, scenario analysis, and coordinated implementation and management. To establish an integrated system for such work, challenges exist in (1) the limited interoperability between bioenergy models and geographic information systems (GIS); (2) interactive scenario construction, evaluation, and sharing; and (3) complex optimization problem solving that requires advanced cyberinfrastructure resources to support interactive decision-making. To resolve these challenges, this paper describes CyberGIS-BioScope, an interactive and collaborative cyberGIS-based spatial decision-making environment for biomass-to-biofuel supply chain optimization. The CyberGIS-BioScope takes advantage of cyberGIS capabilities to process and analyze spatial data and enhance visualization and sharing of optimization results. Meanwhile, the integrated environment makes the complex optimization model and advanced cyberinfrastructure resources easily accessible for agricultural scientists and decision-makers and thus accelerates their scientific discovery and decision-making processes. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
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- 2015
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17. CyberGIS-enabled decision support platform for biomass supply chain optimization.
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Lin, Tao, Wang, Shaowen, Rodríguez, Luis F., Hu, Hao, and Liu, Yan
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GEOGRAPHIC information systems , *BIOMASS energy , *SUPPLY chains , *FEEDSTOCK , *DECISION support systems , *CYBERINFRASTRUCTURE - Abstract
Biomass supply chain optimization aims to facilitate large-scale production of biofuels by improving the efficiency and effectiveness of biomass feedstock provision. Most existing models are not web based, limited by the accessibility for real-world applications. A CyberGIS-enabled biomass supply chain decision support platform was developed to improve model accessibility and computational performance. The platform includes four major components: BioScope optimization model, GISolve middleware, high-performance cyberinfrastructure, and an interactive web interface. The workflow and functions of each component are provided to illustrate the development and usage of the platform. Case studies and associated system performance have been evaluated to demonstrate the utility of the CyberGIS-enabled decision support platform. Through leveraging cyberinfrastructure resources and interactive web-based interface, the platform enables solving complex biomass supply chain optimization problems. The improved computational performance could provide responsive decision support for group-based applications. [ABSTRACT FROM AUTHOR]
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- 2015
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18. Computational and data sciences for health-GIS.
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Shi, Xun and Wang, Shaowen
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GEOGRAPHIC information systems , *PUBLIC health research , *DATA science , *BIG data , *MONTE Carlo method - Abstract
Computational and data sciences are transforming the entire science enterprise. In the arena of GIS, this is represented by the emergence of cyberGIS. We provide an overview of applying the cyberGIS approach to spatial analysis for health studies. We emphasize that cyberGIS is not just aserviceto traditional spatial analyses, but itself is an alternative approach to problem solving. Some fundamental and profound distinctions of cyberGIS approaches in health-GIS include the following: (1) they may greatly reduce the reliance on models or assumptions, and instead seek actual empirical evidence through mining a large amount of data orvirtualempirical evidence generated through computation; (2) they tend to be non-parametric and tend to generate local solution; (3) they are scalable to high-resolution and less aggregated data; (4) they tend to be stochastic rather than deterministic; and (5) with these approaches, the large amount of data may not be only from input data-sets, but also from analytical workflows. We described the kernel ratio estimation for local intensity estimation, therestrictedandcontrolledMonte Carlo for data disaggregation, andunrestrictedandcontrolledMonte Carlo for statistical significance evaluation as examples of the cyberGIS approaches in health-GIS. [ABSTRACT FROM PUBLISHER]
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- 2015
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19. CyberGIS Gateway for enabling data-rich geospatial research and education.
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Liu, Yan, Padmanabhan, Anand, and Wang, Shaowen
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GEOGRAPHIC information systems ,GATEWAYS (Computer networks) ,GEOSPATIAL data ,EDUCATION ,COMPUTER software ,CYBERINFRASTRUCTURE - Abstract
This paper describes CyberGIS Gateway as an online problem-solving environment for multiple science communities to conduct data-rich geospatial research and education. CyberGIS Gateway is a key modality in the CyberGIS software environment. Scalable gateway application integration has been the focus of CyberGIS Gateway in order to efficiently develop highly interactive online geographic information systems (GIS) user interface components and couple a rich collection of heterogeneous and distributed geospatial data and analytical services for advanced cyberGIS capabilities on advanced cyberinfrastructure. An open mashup and service API approach is developed to address the integration challenges in CyberGIS Gateway application development. This approach is applied and evaluated in developing several representative cyberGIS data and analytical applications. The experience gained from the integration practice is shared. The education and outreach activities in CyberGIS Gateway are presented to illustrate the impact of CyberGIS Gateway in GIScience communities and the effective collaboration within the science gateway community. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
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- 2015
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20. FluMapper: A cyberGIS application for interactive analysis of massive location-based social media.
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Padmanabhan, Anand, Wang, Shaowen, Cao, Guofeng, Hwang, Myunghwa, Zhang, Zhenhua, Gao, Yizhao, Soltani, Kiumars, and Liu, Yan
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GEOGRAPHIC information systems ,APPLICATION software ,SOCIAL media ,INFORMATION theory ,SOCIAL networks ,DATA analysis - Abstract
SUMMARY Social media have experienced a spectacular rise in popularity, attracting hundreds of millions of users and generating unprecedented amount of content that increasingly contain location and place information. Collectively, the massive location information in these data provides an excellent opportunity to better understand many geographic phenomena and geospatial dynamics in a timely fashion. Recent studies capitalizing on social networking and media data show significant societal impacts in many areas including prediction of stock market and infectious disease surveillance. However, because location-based social media data are often massive, generated dynamically, and unstructured, significant computation, data, and visualization challenges need to be resolved. This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data-driven framework using cyberGIS-geographic information systems (GIS) based on advanced cyberinfrastructure-to resolve aforementioned challenges. Specifically, FluMapper-an application on the CyberGIS Gateway-is employed as a case study to demonstrate the data-driven framework and seamless integration of massive location-based social media data and spatial analytical services within the online problem solving environment of the Gateway. FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real-time social media data in an efficient and scalable manner and to support interactive visualization. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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21. CyberGIS software: a synthetic review and integration roadmap.
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Wang, Shaowen, Anselin, Luc, Bhaduri, Budhendra, Crosby, Christopher, Goodchild, Michael F., Liu, Yan, and Nyerges, Timothy L.
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GEOGRAPHIC information system software , *DATA integration , *CYBERINFRASTRUCTURE , *GEOSPATIAL data , *ROAD maps - Abstract
CyberGIS – defined as cyberinfrastructure-based geographic information systems (GIS) – has emerged as a new generation of GIS representing an important research direction for both cyberinfrastructure and geographic information science. This study introduces a 5-year effort funded by the US National Science Foundation to advance the science and applications of CyberGIS, particularly for enabling the analysis of big spatial data, computationally intensive spatial analysis and modeling (SAM), and collaborative geospatial problem-solving and decision-making, simultaneously conducted by a large number of users. Several fundamental research questions are raised and addressed while a set of CyberGIS challenges and opportunities are identified from scientific perspectives. The study reviews several key CyberGIS software tools that are used to elucidate a vision and roadmap for CyberGIS software research. The roadmap focuses on software integration and synthesis of cyberinfrastructure, GIS, and SAM by defining several key integration dimensions and strategies. CyberGIS, based on this holistic integration roadmap, exhibits the following key characteristics: high-performance and scalable, open and distributed, collaborative, service-oriented, user-centric, and community-driven. As a major result of the roadmap, two key CyberGIS modalities – gateway and toolkit – combined with a community-driven and participatory approach have laid a solid foundation to achieve scientific breakthroughs across many geospatial communities that would be otherwise impossible. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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22. A communication-aware framework for parallel spatially explicit agent-based models.
- Author
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Shook, Eric, Wang, Shaowen, and Tang, Wenwu
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MULTIAGENT systems , *GEOGRAPHIC information systems , *GEOGRAPHIC spatial analysis , *CYBERINFRASTRUCTURE , *PARALLEL computers , *LOAD balancing (Computer networks) - Abstract
Parallel spatially explicit agent-based models (SE-ABM) exploit high-performance and parallel computing to simulate spatial dynamics of complex geographic systems. The integration of parallel SE-ABM with CyberGIS could facilitate straightforward access to massive computational resources and geographic information systems to support pre- and post-simulation analysis and visualization. However, to benefit from CyberGIS integration, parallel SE-ABM must overcome the challenge of communication management for orchestrating many processor cores in parallel computing environments. This paper examines and addresses this challenge by describing a generic framework for the management of inter-processor communication to enable parallel SE-ABM to scale to high-performance parallel computers. The framework synthesizes four interrelated components: agent grouping, rectilinear domain decomposition, a communication-aware load-balancing strategy, and entity proxies. The results of a series of computational experiments based on a template agent-based model demonstrate that parallel computational efficiency diminishes as inter-processor communication increases, particularly when scaling a fixed-size model to thousands of processor cores. Therefore, effective communication management is crucial. The communication framework is shown to efficiently scale up to 2048 cores, demonstrating its ability to effectively scale to thousands of processor cores to support the simulation of billions of agents. In a simulated scenario, the communication-aware load-balancer reduced both overall simulation time and communication percentage improving overall computational efficiency. By examining and addressing inter-processor communication challenges, this research enables parallel SE-ABM to efficiently use high-performance computing resources, which reduces the barriers for synergistic integration with CyberGIS. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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23. An attention U-Net model for detection of fine-scale hydrologic streamlines.
- Author
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Xu, Zewei, Wang, Shaowen, Stanislawski, Lawrence V., Jiang, Zhe, Jaroenchai, Nattapon, Sainju, Arpan Man, Shavers, Ethan, Usery, E. Lynn, Chen, Li, Li, Zhiyu, and Su, Bin
- Subjects
- *
AGRICULTURAL water supply , *CLIMATE change models , *DEEP learning , *REMOTE sensing , *ENVIRONMENTAL monitoring , *MACHINE learning - Abstract
Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains. • A deep learning model for incorporating multi-scale remote sensing information is created. • A novel application of the model for fine-scale hydrologic streamline detection is developed. • An innovative streamline detection method for fully harnessing LiDAR data is presented. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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24. CyberGIS Gateway for enabling data-rich geospatial research and education.
- Author
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Liu, Yan, Padmanabhan, Anand, and Wang, Shaowen
- Abstract
This paper describes CyberGIS Gateway, an online problem-solving environment, for multiple science communities to conduct data-rich geospatial research and education. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
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25. ViCTS: A novel network partition algorithm for scalable agent-based modeling of mass evacuation.
- Author
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Yin, Dandong, Wang, Shaowen, and Ouyang, Yanfeng
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PARALLEL algorithms , *VORONOI polygons , *PARALLEL programming , *EMERGENCY management , *CYBERINFRASTRUCTURE , *HOME range (Animal geography) , *CYBER intelligence (Computer security) , *EMERGENCY contraceptives , *SENDAI Earthquake, Japan, 2011 - Abstract
Emergency evacuation is a critical response to deadly disasters such as hurricanes, floods, and earthquakes, etc. However, mass emergency evacuation itself is a complex process that sometimes could lead to chaotic situations and unintended consequences. In many emergency scenarios, mass evacuation is necessary to cope with severe public threats within tight spatiotemporal ranges. To better understand complex phenomena like mass evacuation, and study possible consequences, agent-based models (ABMs) have been widely developed in previous work. Existing models simulate individual behaviors, posing computational challenges when applied to large geographic areas and sophisticated behaviors. A key strategy for resolving such computational challenges is to partition transportation networks into smaller regions and resolve corresponding computational costs by taking advantage of advanced cyberinfrastructure and cyberGIS. In this study, a novel network partition algorithm is developed to improve the scalability of agent-based modeling of mass evacuation based on a cutting-edge cyberGIS-enabled computational framework that exploits the spatial movement patterns of emergency evacuation. Specifically, the algorithm is termed as Voronoi Clustering based on Target-Shift, or ViCTS. It is enlightened by network Voronoi diagrams and designed to resolve computational scalability challenges caused by the unique characteristics of evacuation traffic. We conducted a set of computational experiments with real street network data in various evacuation scenarios to test the effectiveness and efficiency of the algorithm. Computational experiments show that ViCTS outperforms a widely used network partition algorithm for microscopic traffic simulation in terms of achieving optimal computational performance by balancing computational loads and reducing communications across high-performance parallel computing resources. • Conventional network partition algorithms result in suboptimal computational performance. • Voronoi diagrams and target-shifting proximity are employed to resolve computational challenges. • The ViTCS algorithm achieves optimal computational performance, and enables scalable agent-based modeling of mass evacuation. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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26. TopoLens: Building a CyberGIS community data service for enhancing the usability of high‐resolution national topographic datasets.
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Hu, Hao, Yin, Dandong, Liu, Yan Y., Terstriep, Jeff, Hong, Xingchen, Wendel, Jeff, and Wang, Shaowen
- Subjects
COMMUNITY services ,GEOSPATIAL data ,WEB-based user interfaces ,VIRTUAL communities ,BIG data ,GEOLOGICAL surveys - Abstract
Summary: In recent years, geospatial data have exploded to massive volume and diversity and subsequently cause serious usability issues for researchers in various scientific areas. This paper describes a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. Specifically, the framework aims to enhance the usability of national elevation dataset released by the U.S. Geological Survey in the contiguous United States at the resolution of 1/3 arc‐second. A community data service, namely TopoLens, is created to demonstrate the workflow integration of national elevation dataset and the associated computation and analysis. Two user‐friendly environments, including a publicly available web application and a private workspace based on the Jupyter notebook, are provided for users to access both precomputed and on‐demand computed high‐resolution elevation data. The system architecture of TopoLens is implemented by exploiting the ROGER supercomputer, the first cyberGIS supercomputer dedicated to geospatial problem‐solving. The usability of TopoLens has been acknowledged in the topographic user community evaluation. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
27. CyberGIS‐Jupyter for reproducible and scalable geospatial analytics.
- Author
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Yin, Dandong, Liu, Yan, Hu, Hao, Terstriep, Jeff, Hong, Xingchen, Padmanabhan, Anand, and Wang, Shaowen
- Subjects
GATEWAYS (Computer networks) ,GEOGRAPHIC information systems ,GEOSPATIAL data ,BIG data ,CYBERINFRASTRUCTURE - Abstract
Summary: The interdisciplinary field of cyberGIS (geographic information science and systems (GIS) based on advanced cyberinfrastructure) has a major focus on data‐ and computation‐intensive geospatial analytics. The rapidly growing needs across many application and science domains for such analytics based on disparate geospatial big data poses significant challenges to conventional GIS approaches. This paper describes CyberGIS‐Jupyter, an innovative cyberGIS framework for achieving data‐intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on ROGER, the first cyberGIS supercomputer. The framework adapts the Notebook with built‐in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud‐computing approaches. As a desirable outcome, data‐intensive and scalable geospatial analytics can be efficiently developed and improved and seamlessly reproduced among multidisciplinary users in a novel cyberGIS science gateway environment. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
28. A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization.
- Author
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Hu, Hao, Lin, Tao, Wang, Shaowen, and Rodriguez, Luis F.
- Subjects
- *
GEOGRAPHIC information systems , *BIOMASS , *SUPPLY chains , *SENSITIVITY analysis , *QUANTITATIVE research - Abstract
Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43 gal −1 , considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
29. CyberGIS Considerations for Structured Participation Methods in Collaborative Problem Solving
- Author
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Roderick, Mary J., Nyerges, Timothy L., Avraam, Michalis, Sui, Daniel Z., Managing Editor, Wang, Shaowen, editor, and Goodchild, Michael F., editor
- Published
- 2019
- Full Text
- View/download PDF
30. Integrating GIScience Application Through Mashup
- Author
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Yang, Chaowei, Fu, Pinde, Goodchild, Michael F., Xu, Chen, Sui, Daniel Z., Managing Editor, Wang, Shaowen, editor, and Goodchild, Michael F., editor
- Published
- 2019
- Full Text
- View/download PDF
31. Mapping Spatial Information Landscape in Cyberspace with Social Media
- Author
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Yang, Jiue-An, Tsou, Ming-Hsiang, Spitzberg, Brian, An, Li, Gawron, Jean Mark, Gupta, Dipak, Sui, Daniel Z., Managing Editor, Wang, Shaowen, editor, and Goodchild, Michael F., editor
- Published
- 2019
- Full Text
- View/download PDF
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