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

Abstract

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.

Keywords:

• epidemiology
• machine-learning
• presence and background learning
• search and rescue
• spatiotemporal data

Acknowledgments

The authors would like to thank Greg and Diane Ambrose, Sarah Jacobs, and Raj V. Bolla for editorial assistance and data preparation. The authors also credit Yosemite National Park Division of Resource Management and Science and Yosemite Search and Rescue Team for assistance, research permissions, and guidance.

Funding

This study is supported by the National Science Foundation [grant number BDI-0742986], [grant number SBE-1031914].