Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$60.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: epidemiology; machine-learning; presence and background learning; search and rescue; spatiotemporal data

Document Type: Research Article

Affiliations: 1: School of Engineering and Sierra Nevada Research Institute, University of California, Merced, CA, USA 2: Geography Department, University of Kansas, Lawrence, KS, USA

Publication date: May 4, 2014

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more