10.6084/m9.figshare.7159727.v1
Olga Viedma
Olga
Viedma
T.I.R. Urbieta
T.I.R.
Urbieta
J.M. Moreno
J.M.
Moreno
The relationships between wildfires and their drivers over time in a large rural area of Central Spain between 1979 and 2008
Springer Nature
2018
fire regime
wildfires
socioeconomic drivers
climate
landscapes
hazardousness
land use change
longitudinal data
non-stationarity
time series
spatio-temporal data
longitudinal negative binomial mixed model
zero-inflated negative binomial mixed model
fires
natural hazard
2018-12-11 15:48:07
Dataset
https://springernature.figshare.com/articles/dataset/The_relationships_between_wildfires_and_their_drivers_over_time_in_a_large_rural_area_of_Central_Spain_between_1979_and_2008/7159727
<div>This dataset contains data on wildfires and related biophysical and human-related factors—such as climate, landscapes and socioeconomic drivers—for a 56,000 km^2 rural area in Central Spain for the years 1979 to 2008.</div><div><br></div><div>There is <b>.xlsx </b>spreadsheet entitled <b>basefinal_PANEL_7908_allfires_zscore.xlsx</b>, which contains the following two tabs: one with the wildfire data, and one with a list of descriptions of the column headers. The spreadsheets are:</div><div>- <b>basefinal_PANEL_1979_2008</b>: contains data for wildfires and drivers, including fires of sizes 1-10 hectares, 10-100 hectares, and greater than 100 hectares, as well as all respective z-scores.</div><div></div><div>- <b>labels of variables</b>: contains descriptions of the column headers in the basefinal_PANEL_1979_2008, along with variables from the related manuscript and details of how the z-scores were calculated.</div><div><br></div><div>The spatial units are cells of 10x10km, and coordinates are given in the World Geodetic System 1984 (WGS84) UTM zone 30N. All variables were temporally interpolated and they are given in their original units and normalized by z-score.</div><div><br></div><div>The related publication provides a spatio-temporal assessment of the changing effects of climate, landscapes and socioeconomic drivers on wildfires. This was done by modelling the number of fires per cell per year using two types of mixed models: Longitudinal Negative Binomial (LNB) and Zero-Inflated Negative Binomial (ZINB), with time as an interacting factor.</div>