Wednesday, April 23, 2014

Space-time analyses for forecasting future incident occurrence: a case study from Yosemite National Park using the presence and background learning algorithm

This follow up paper to the Yosemite Search and Rescue Incident Georeferencing Study has been published in the International Journal of Geographical Information Science. Many thanks to my colleagues and all of the volunteers who have helped support this project! 


To address a spatiotemporal challenge such as incident prevention, we need information about the time and place where incidents have occurred in the past. Using geographic coordinates of previous incidents in coincidence with spatial layers corresponding to environmental variables, we can produce probability maps in geographic and temporal space. Here, we evaluate spatial statistic and machine learning approaches to answer an important space-time question: where and when are wildland search and rescue (WiSAR) incidents most likely to occur within Yosemite National Park (YNP)? We produced a monthly probability map for the year 2011 based on the presence and background learning (PBL) algorithm that successfully forecasts the most likely areas of WiSAR incident occurrence based on environmental variables (distance to anthropogenic and natural features, vegetation, elevation, and slope) and the overlap with historic incidents from 2001 to 2010. This will allow decision-makers to spatially allocate resources where and when incidents are most likely to occur. In the process, we not only answered questions related to a real-world problem but also used novel space-time analyses that give us insight into machine learning principles. The GIScience findings from this applied research have major implications for best practices in future space-time research in the fields of epidemiology and ecological niche modeling.

Download the Paper
The IJGIS will provide free access for the first 50 downloads. Since the GIScience community already subscribes to this publication, I thought I would open this up to the Search and Rescue GIS Community: Download Here

Conclusion for Search and Rescue GIS
  • Both where and when an incident occurs is important.
  • SAR incidents occur where visitation is likely highest (obvious) - but visitor use activity is also not well mapped in recreational areas like Yosemite. Therefore it is difficult to map risk factors independently. 
  • If you don't map where an incident has occurred how will anyone else ever learn from the experience? 
  • Maps are an extremely compelling tool for telling a story about a place and capturing institutional knowledge.
  • GIS is an under utilized tool in Search and Rescue and this research is just beginning to scratch the surface
This research initiative is supported by the National Science Foundation (grant nos. BDI-0742986 and SBE-1031914). I would like to thank Yosemite Search and Rescue, Yosemite Volunteers-In-Parks, and the Yosemite National Park Division of Resource Management and Science for research permissions (OMB#1024-0236) and constructive suggestions. Special thanks to my Dissertation Commitee: Dr. Samuel Traina, Dr. Ruth Mostern, Dr. Yihsu Chen, labmates Wenkai Li and Otto Alvarez, co-authors Yu Liu and John Wieczorek, and especially my PhD advisor Dr. Quinghua Guo. Thank you to volunteers Diane and Greg Ambrose and Sarah Nurit for all of the Georeferencing and clerical work!

If we want to collaborate in follow up research, contact the Spatial Analysis & Research Center at University of California Merced (SpARC)

This map below is just a point layer of cumulative incidents. Stay tuned for time-enabled maps and maps that filter by incident type.

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