@article{288efb93e8944ab5bc3bdc7edf0c0364,
title = "TopoLens: Building a CyberGIS community data service for enhancing the usability of high-resolution national topographic datasets",
abstract = "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.",
keywords = "CyberGIS, geospatial big data, microservices, science gateway, topographic data",
author = "Hao Hu and Dandong Yin and Liu, {Yan Y.} and Jeff Terstriep and Xingchen Hong and Jeff Wendel and Shaowen Wang",
note = "Funding Information: This work is supported in part by USGS under grant number G14AC00244, and by the National Science Foundation (NSF) under grant numbers 1047916 and 1443080. The computational work used the NSF-supported ROGER supercomputer (1429699). This work is also part of the ECSS project (award number SES090019) of XSEDE, which is supported by NSF grant number 1053575. The authors would like to acknowledge the contribution of CyberGIS Center's students Sunwoo Kim and Yuliya Semibratova at the University of Illinois at Urbana-Champaign. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Funding Information: USGS, Grant/Award Number: G14AC00244; National Science Foundation (NSF), Grant/Award Number: 1047916 and 1443080; NSF, Grant/Award Number: 1429699; NSF XSEDE, Grant/Award Number: SES090019; NSF, Grant/Award Number: 1053575 Funding Information: This work is supported in part by USGS under grant number G14AC00244, and by the National Science Foundation (NSF) under grant numbers 1047916 and 1443080. The computational work used the NSF-supported ROGER supercomputer (1429699). This work is also part of the ECSS project (award number SES090019) of XSEDE, which is supported by NSF grant number 1053575. The authors would like to acknowledge the contribution of CyberGIS Center's students Sunwoo Kim and Yuliya Semibratova at the University of Illinois at Urbana-Champaign. Publisher Copyright: {\textcopyright} 2018 John Wiley & Sons, Ltd.",
year = "2019",
month = aug,
day = "25",
doi = "10.1002/cpe.4682",
language = "English (US)",
volume = "31",
journal = "Concurrency and Computation: Practice and Experience",
issn = "1532-0626",
publisher = "John Wiley & Sons, Ltd.",
number = "16",
}