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A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain

Lara Vilar A C , Douglas. G. Woolford B , David L. Martell B and M. Pilar Martín A
+ Author Affiliations
- Author Affiliations

A Centre for Human and Social Sciences, Spanish Council for Scientific Research, Albasanz 26-28, E-28037 Madrid, Spain.

B Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON, M5S 3B3, Canada.

C Corresponding author. Email:

International Journal of Wildland Fire 19(3) 325-337
Submitted: 27 March 2009  Accepted: 10 November 2009   Published: 13 May 2010


This paper describes the development and validation of a spatio-temporal model for human-caused wildfire occurrence prediction at a regional scale. The study area is the 8028-km2 region of Madrid, located in central Spain, where more than 90% of wildfires are caused by humans. We construct a logistic generalised additive model to estimate daily fire ignition risk at a 1-km2 grid spatial resolution. Spatially referenced socioeconomic and weather variables appear as covariates in the model. Spatial and temporal effects are also included. The variables in the model were selected using an iterative approach, which we describe. We use the model to predict the expected number of fires in our study area during the 2002–05 period, by aggregating the estimated probabilities over space–time scales of interest. The estimated partial effects of the presence of railways, roads, and wildland–urban interface in forest areas were highly significant, as were the observed daily maximum temperature and precipitation.

Additional keywords: fire risk, generalised additive models, geographic information systems, logistic, non-parametric spline smoothing, socioeconomic variables, wildland fire, wildland–urban interface.


This research received partial support from Firemap project CGL2004-06049-C04-01/CLI, funded by the Spanish Ministry of Education, through FPI scholarship BES-2005-7712. Additional funding from the National Institute for Complex Data Structures and Geomatics for Informed Decisions (GEOIDE SII Project 51) is also gratefully acknowledged. We thank Inmaculada Aguado, Mariano García, Héctor Nieto, Marta Yebra and Felipe Verdú from the Department of Geography of the University of Alcalá (Spain) for their advice and the data they supplied. We would like also thank Robert Kruus from the Fire Management Systems Laboratory in the Faculty of Forestry at the University of Toronto (Canada) for his assistance in database preparation. Historic fire data has been provided by the Fire Department of the region of Madrid and the Spanish Ministry of Environment, while other data was provided by the Madrid Regional Environmental Office.


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A Reignited fire: the re-occurrence of a wildfire that was previously classified as having been under control.