TY - GEN
T1 - Mobile Data Collection and Analysis in Conservation
AU - Wergeles, Nickolas M.
AU - Shang, Charles
AU - Peng, Zeshan
AU - Wang, Haidong
AU - Sartwell, Joel
AU - Treiman, Tom
AU - Beringer, Jeff
AU - Belant, Jerrold L.
AU - Millspaugh, Joshua
AU - McRoberts, Jon T.
AU - Shang, Yi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/28
Y1 - 2016/6/28
N2 - Mobile computing and big data analytics have great potential for improving efficiency, productivity, and knowledge discovery in conservation tasks. This paper presents some recent development of mobile computing and data analysis systems for the Missouri Department of Conservation (MDC), including a mobile data collection system, an improved bear tracking website, and new analytics results obtained from real Missouri bear and deer GPS trajectories. The mobile data collection system using Android tablets is developed for surveying the usage and status of conservation areas. The new bear tracking website is developed to be mobile friendly and can dynamically present information of tracked bears. Lastly, for analyzing real GPS trajectories obtained from Missouri bears and deer, a density-based spatial and temporal clustering method is developed for identifying stay regions in trajectories of low sampling rate GPS points. Using real world data, interesting movement patterns of a large number of bears and white-tailed deer have been obtained, therefore advancing a step in achieving a better understanding of the behaviors of various types of animals in their natural environments.
AB - Mobile computing and big data analytics have great potential for improving efficiency, productivity, and knowledge discovery in conservation tasks. This paper presents some recent development of mobile computing and data analysis systems for the Missouri Department of Conservation (MDC), including a mobile data collection system, an improved bear tracking website, and new analytics results obtained from real Missouri bear and deer GPS trajectories. The mobile data collection system using Android tablets is developed for surveying the usage and status of conservation areas. The new bear tracking website is developed to be mobile friendly and can dynamically present information of tracked bears. Lastly, for analyzing real GPS trajectories obtained from Missouri bears and deer, a density-based spatial and temporal clustering method is developed for identifying stay regions in trajectories of low sampling rate GPS points. Using real world data, interesting movement patterns of a large number of bears and white-tailed deer have been obtained, therefore advancing a step in achieving a better understanding of the behaviors of various types of animals in their natural environments.
KW - Bear and Deer Tracking
KW - Data Analytics
KW - Mobile Data Collection
KW - Trajectory Analysis
KW - Wildlife Conservation
UR - http://www.scopus.com/inward/record.url?scp=84979567361&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP.2016.7501706
DO - 10.1109/SMARTCOMP.2016.7501706
M3 - Conference contribution
AN - SCOPUS:84979567361
T3 - 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016
BT - 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016
Y2 - 18 May 2016 through 20 May 2016
ER -