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Author (up) Bredeweg, E.; D'Antonio, A.; Esser, S.; Jacobs, A., pdf  url
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  Title Steps on a path: An application of machine learning using a random forest algorithm to predict visitor use levels on trails in Rocky Mountain National Park, USA. Type
  Year 2021 Publication The 10th MMV Conference: Managing outdoor recreation experiences in the Anthropocene – Resources, markets, innovations Abbreviated Journal  
  Volume MINA fagrapport Issue Pages 280-281  
  Keywords MMV10  
  Abstract Understanding the location and level of recreation use in park and protected areas (PPA) can be useful for effective visitor use management. While there is a wealth of geospatial data available online and in the manager databases of many PPA, the development and format of these datasets may be shaped more by the nature of GIS software than the way visitors explore and use a PPA system. Moreover, aspects important for visitor management such as quantification of visitor use levels on trails may be more difficult to source for each trail segment than physical trail characteristics (length, location, elevation profile, etc.). It would be expected that trail characteristics would influence the traffic of visitors, but there are many other factors such as accessibility, parking, or nearby attractions that can influence visitor behavior in complex ways. While we can obtain the physical characteristics, available amenities, and relative locations of trails within the entire PPA, we often do not have visitor use levels on the same extent. In order to examine visitor use levels on the scale of the entire PPA, we need to be able to model the relationship between physical location, trail characteristics, and amenities that ultimately shape visitor use.  
  Call Number Serial 4325  
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