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Folch & Sutherland - Using Linear Tracking Data to Estimate Backcountry Recreation Popularity
Estimating Backcountry Recreation Popularity in Arizona using Linear Tracking Data, leveraging presence data, background locations, and explanatory variables to predict recreation presence and assess risks to the public.
- The project uses a linear tracking data model to estimate backcountry recreation popularity in Arizona, including abandoned mine sites.
- The team is working on a partnership with a state university to access more powerful hardware for higher-resolution analysis.
- The model considers three inputs: presence data, background locations, and explanatory variables (activity, accessibility, climate, and demographics).
- The explanatory variables are grouped into three categories: activity, climate, and demographics.
- The team has gathered 20,000 bike segments and is working to get access to more data sources.
- The model uses Gaussian filter functions to smooth the data.
- The team is using rasterization to combine linear data sets (trails, waterways, roads, and access points) into a single raster.
- The project aims to provide estimates of where people are in relation to abandoned mine sites and assess the risk of physical danger to the public.
- The team is considering using the PRISM data to estimate weather data.
- The model uses logistic regression to predict the likelihood of recreation presence.
- The precision recall curve is used to analyze model performance.
- The team is working to refine their methods and consult with domain experts.
- The project aims to assist state officials in utilizing remediation funds to greatest positive effect for both the environment and residents of Arizona.