Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban–forest interface are important factors for wildfire ignition.

[1]  A. Gill,et al.  Landscape fires as social disasters: An overview of ‘the bushfire problem’ , 2005 .

[2]  Montserrat Pallares-Barbera,et al.  Spatial distribution of ignitions in Mediterranean periurban and rural areas: The case of Catalonia , 2006 .

[3]  David T. Butry,et al.  Understanding Broadscale Wildfire Risks in a Human-Dominated Landscape , 2002, Forest Science.

[4]  M. Boyce,et al.  Evaluating resource selection functions , 2002 .

[5]  S. Ventura,et al.  ENVIRONMENTAL AND SOCIAL FACTORS INFLUENCING WILDFIRES IN THE UPPER MIDWEST, UNITED STATES , 2001 .

[6]  Ronald R Rindfuss,et al.  Developing a science of land change: challenges and methodological issues. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Peter F. Fisher,et al.  Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping* , 1996 .

[8]  Susan I. Stewart,et al.  Human influence on California fire regimes. , 2007, Ecological applications : a publication of the Ecological Society of America.

[9]  Walter C. Oechel,et al.  Global Change and Mediterranean-Type Ecosystems , 1995, Ecological Studies.

[10]  Cynthia A. Brewer,et al.  Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation , 2001 .

[11]  G. Perry,et al.  Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape , 2007, Ecosystems.

[12]  Stephen Sitch,et al.  Simulating fire regimes in human‐dominated ecosystems: Iberian Peninsula case study , 2002 .

[13]  P. Stern,et al.  People and pixels : linking remote sensing and social science , 1999 .

[14]  Sandra Luque,et al.  Habitat quality assessment using Weights-of-Evidence based GIS modelling: the case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest. , 2006 .

[15]  F. Agterberg,et al.  Weights of evidence modelling: a new approach to mapping mineral potential , 1990 .

[16]  Alan T. Murray,et al.  Integrating attribute and space characteristics in choropleth display and spatial data mining , 2000, Int. J. Geogr. Inf. Sci..

[17]  Mevin B. Hooten,et al.  Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model , 2003, Landscape Ecology.

[18]  George L. W. Perry,et al.  The role of land abandonment in landscape dynamics in the SPA 'Encinares del río Alberche y Cofio, Central Spain, 1984-1999 , 2004 .

[19]  David T. Butry,et al.  What Is the Price of Catastrophic Wildfire? , 2001, Journal of Forestry.

[20]  L. G. Pedraza.  Los incendios forestales en España , 1981 .

[21]  Martin Hale,et al.  A predictive GIS model for mapping potential gold and base metal mineralization in Takab area, Iran , 2001 .

[22]  J. Cardille.  Environmental and Social Factors Influencing Wildfires in the Upper Midwest, USA , 2001 .

[23]  Michael N. DeMers.  GIS modeling in raster , 2001 .

[24]  J. Mennis.  Generating Surface Models of Population Using Dasymetric Mapping , 2003, The Professional Geographer.

[25]  Jeffrey P. Prestemon,et al.  Comparing production function models for wildfire risk analysis in the wildland–urban interface , 2005 .

[26]  Mark S. Boyce,et al.  Modelling distribution and abundance with presence‐only data , 2006 .

[27]  E. Carranza,et al.  Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines , 2000 .

[28]  Alexandra D. Syphard,et al.  Simulating fire frequency and urban growth in southern California coastal shrublands, USA , 2007, Landscape Ecology.

[29]  George L. W. Perry,et al.  Spatial modelling of vegetation change in dynamic landscapes: a review of methods and applications , 2006 .

[30]  Richard A. Minnich,et al.  Mapping probability of fire occurrence in San Jacinto Mountains, California, USA , 1993 .

[31]  M. Vasconcelos,et al.  Spatial Prediction of Fire Ignition Probabilities: Comparing Logistic Regression and Neural Networks , 2001 .

[32]  Jian Yang,et al.  Spatial Patterns of Modern Period Human-Caused Fire Occurrence in the Missouri Ozark Highlands , 2007, Forest Science.

[33]  Uwe Deichmann,et al.  A Review of Spatial Population Database Design and Modeling , 1996 .

[34]  T. Sisk,et al.  Mapping the probability of large fire occurrence in northern Arizona, USA , 2006, Landscape Ecology.

[35]  J. Moreno,et al.  Patterns of Fire Occurrence Across a Climatic Gradient and Its Relationship to Meteorological Variables in Spain , 1995 .

[36]  F. Agterberg,et al.  Statistical applications in the earth sciences , 1990 .

[37]  M. Langford,et al.  Generating and mapping population density surfaces within a geographical information system. , 1994, The Cartographic journal.

[38]  Emilio Chuvieco,et al.  Mapping the Spatial Distribution of Forest Fire Danger Using GIS , 1996, Int. J. Geogr. Inf. Sci..

[39]  G. Bonham-Carter.  Geographic Information Systems for Geoscientists: Modelling with GIS , 1995 .

[40]  Sean White,et al.  Urban population distribution models and service accessibility estimation , 2008, Comput. Environ. Urban Syst..

[41]  Weiguo Song,et al.  Three types of power-law distribution of forest fires in Japan , 2006 .

[42]  Raul Romero-Calcerrada,et al.  GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain) , 2008, Landscape Ecology.

[43]  F. Chapin,et al.  Human Influences on Wildfire in Alaska from 1988 through 2005: An Analysis of the Spatial Patterns of Human Impacts , 2008 .

[44]  Jn Laplante,et al.  The continuing evolution of pedestrian walking speed assumptions , 2004 .

[45]  E. Carranza,et al.  Where Are Porphyry Copper Deposits Spatially Localized? A Case Study in Benguet Province, Philippines , 2002 .

[46]  Raymond J. Dezzani,et al.  Spatial autocorrelation of wildfire distribution in the Idyllwild Quadrangle, San Jacinto Mountain, California. , 1990 .

[47]  Mitchel Langford,et al.  Rapid facilitation of dasymetric-based population interpolation by means of raster pixel maps , 2007, Comput. Environ. Urban Syst..

[48]  Hong S. He,et al.  Spatial controls of occurrence and spread of wildfires in the Missouri Ozark Highlands. , 2008, Ecological applications : a publication of the Ecological Society of America.

[49]  A. Hirzel,et al.  Evaluating the ability of habitat suitability models to predict species presences , 2006 .