Case study of visualizing global user download patterns using Google Earth and NASA World Wind

Journal Article · Tue Oct 09 04:00:00 EDT 2012 · Journal of Applied Remote Sensing, 6(1):Article No. 061703

DOI:https://doi.org/10.1117/1.JRS.6.061703· OSTI ID:1054847

Zong, Ziliang; Job, Joshua; Zhang, Xuesong; Nijim, Mais; Qin, Xiao

Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASAWorld Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the hot spot areas of research. Most importantly, our methods demonstrate an easy way to geovisualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1054847
• Report Number(s): PNNL-SA-91658; KP1601050
• Journal Information: Journal of Applied Remote Sensing, 6(1):Article No. 061703, Journal Name: Journal of Applied Remote Sensing, 6(1):Article No. 061703
• Country of Publication: United States
• Language: English