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Kernel Regression of Directional Data with Application to Wind and Wildfire Data in Los Angeles County, California

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Abstract:

This article describes a method of kernel regression that can be used to investigate the relationship between a directional explanatory variable and a real-valued response variable. Cross-validation and bootstrap methods for obtaining sensible bandwidths and standard error estimates are also described. The proposed method is applied to wildfire and meteorological data from Los Angeles County, California, with the goal of summarizing and quantifying the impact of wind direction on the total area burned in wildfires ignited on a particular day. The results confirm that winds blowing from the northeast and east are associated with the ignition of wildfires of significantly larger burn areas than winds from other directions; the mean burn area of wildfires ignited on days with winds from the northeast is approximately 4.7 times that associated with the winds from the southwest. A major reason for this increase in burn area is confounding of wind direction with meteorological variables such as temperature, wind speed, and humidity, but even when generalized additive modeling is used to control for these meteorological variables, winds from the northeast are still associated with the ignition of fires of substantially larger burn areas.

Keywords: circular statistics; cross-validation; directional statistics; generalized additive; model, wildfire

Document Type: Research Article

Publication date: 2011-08-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
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    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
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