Impact du changement d'échelle sur l'étude des causes des feux de forêts du sud-est de la France

The Modifiable Areal Unit Problem (MAUP) is a well-known issue related to the influence of the spatial support on statistical observations. It occurs when different spatial units making different spatial partitions are used and when the resulting measures vary according to those partitions. In this paper, we first draw a state of the art. Considering the particular problem of (up)scaling, we propose a method to visualize the sensitivity of the spatial statistics to the support. We test this method on forest fires in Southern France, handling a sample from the Promethee database.

[1]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[2]  Lin Liu,et al.  Reducing MAUP bias of correlation statistics between water quality and GI illness , 2008, Comput. Environ. Urban Syst..

[3]  Hadrien Beauguitte Laurent Buard Elodie Et Al. Commenges R et espace : traitement de l'information géographique , 2014 .

[4]  Didier Josselin,et al.  Impact of a Change of Support on the Assessment of Biodiversity with Shannon Entropy , 2008, SDH.

[5]  D. Freedman,et al.  A solution to the ecological inference problem , 1997 .

[6]  M. Tanner,et al.  Ecological Inference: New Methodological Strategies , 2004 .

[7]  Bruno Fady,et al.  ANALYSE EXPLORATOIRE DES EFFETS DE SUPPORT SPATIAL ET DE ROBUSTESSE STATISTIQUE SUR LA FIABILITÉ DE LA MESURE DE LA (BIO)DIVERSITÉ , 2009 .

[8]  D. Steel,et al.  Using Census Data to Investigate the Causes of the Ecological Fallacy , 1998, Environment & planning A.

[9]  David G Steel,et al.  Rules for Random Aggregation , 1996 .

[10]  Marielle Jappiot,et al.  What causes large fires in Southern France , 2013 .

[11]  André Ravel,et al.  How to choose geographical units in ecological studies: proposal and application to campylobacteriosis. , 2013, Spatial and spatio-temporal epidemiology.

[12]  A S Fotheringham,et al.  The Modifiable Areal Unit Problem in Multivariate Statistical Analysis , 1991 .

[13]  M. Charlton,et al.  Quantitative geography : perspectives on spatial data analysis by , 2001 .

[14]  Didier Josselin,et al.  Sensibilité des indices de diversité à l'agrégation , 2007, Rev. Int. Géomatique.

[15]  C. E. Gehlke,et al.  Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Tract Material , 1934 .

[16]  Gary King,et al.  A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data , 1998 .

[17]  E. H. Simpson,et al.  The Interpretation of Interaction in Contingency Tables , 1951 .

[18]  H. Blalock Causal Inferences in Nonexperimental Research , 1966 .

[19]  Laure L. Charleux GWR, MAUP et lissage par potentiels , 2005, Rev. Int. Géomatique.

[20]  Yuehui Chen,et al.  Foundations of Computational Intelligence , 2009 .

[21]  Arthur H. Robinson,et al.  THE NECESSITY OF WEIGHTING VALUES IN CORRELATION ANALYSIS OF AREAL DATA , 1956 .

[22]  S. Openshaw A million or so correlation coefficients : three experiments on the modifiable areal unit problem , 1979 .

[23]  W. S. Robinson,et al.  Ecological correlations and the behavior of individuals. , 1950, International journal of epidemiology.

[24]  David G Steel,et al.  Aggregation and Ecological Effects in Geographically Based Data , 2010 .